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pmid:41189944

Unveiling hypoxic regulatory networks by bioinformatics: mechanisms of hypoxia-related hub genes driving rituximab resistance and poor prognosis in DLBCL.

Jiayi Yao · Juan Huang · Yuanfei Mao · Zizhen Xu

Frontiers in oncology · 2025 doi:10.3389/fonc.2025.1592441

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Experiment

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Data

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pmid:41189944
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title
Unveiling hypoxic regulatory networks by bioinformatics: mechanisms of hypoxia-related hub genes driving rituximab resistance and poor prognosis in DLBCL.
doi
10.3389/fonc.2025.1592441
pmid
41189944
authors array · 4 items
[0]
Jiayi Yao
[1]
Juan Huang
[2]
Yuanfei Mao
[3]
Zizhen Xu
journal
Frontiers in oncology
year
2025
abstract
Background: Diffuse large B-cell lymphoma (DLBCL), an aggressive subtype of non-Hodgkin lymphoma, exhibits heterogeneous clinical outcomes. While rituximab, a CD20 inhibitor, combined with chemotherapy has improved survival in some patients, resistance remains prevalent, particularly in hypoxic tumor microenvironments. Understanding hypoxia-related genes (HRGs) and their role in rituximab resistance is critical to addressing therapeutic challenges in high-risk DLBCL. Methods: Gene expression profiles from GEO datasets (GSE56315: DLBCL tumor vs. normal; GSE104212: hypoxia-treated DLBCL cell lines) were analyzed to identify overlapping genes between DLBCL-signature genes (DSGs) and HRGs. protein interaction network topology analysis and Lasso regression modeling of TCGA-DLBC dataset were employed to screen regulator and hub genes. Hub genes linked to rituximab response and survival were validated in DLBCL patients receiving rituximab therapy. Functional enrichment analysis was used to explore associated pathways. The expression of the identified regulator and hub genes was validated using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results: 58 overlapping genes were identified between DSGs and HRGs. PPI network and Lasso regression revealed 5 MS4A1 regulator genes and 10 hub genes. Among these, LGALS1 (HR = 0.588, p = 0.00085), TIMP1 (HR = 0.591, p = 0.00098), ANXA1 (HR = 0.614, p=0.0024) and STAP1 (HR = 0.633, p=0.0035) were significantly associated with overall survival and GPNMB (AUC = 0.869), CDCA7 (AUC = 0.686), and STAP1 (AUC = 0.663) associated with treatment response in rituximab-treated patients. Functional analysis implicated these genes in B-cell receptor (BCR) and PI3K-AKT signaling pathways, suggesting their mechanistic roles in therapeutic resistance. Conclusions: This study identifies hypoxia-associated genes critical to rituximab resistance in DLBCL, highlighting potential therapeutic targets. Their involvement in BCR and PI3K-AKT pathways underscores novel vulnerabilities for overcoming refractory disease. Our findings provide a foundation for developing strategies to improve outcomes in high-risk DLBCL patients with hypoxic microenvironments.
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Transcriptome-wide differential expression analysis was systematically conducted according to fold change (FC) and t-test.
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Transcriptome-wide differential expression analysis was systematically conducted according to fold change (FC) and t-test.
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Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10846 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56315 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104212.
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Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10846 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56315 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104212.
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The study identified 58 overlapping genes between DLBCL-signature genes and hypoxia-related genes.
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58 overlapping genes were identified between DSGs and HRGs.
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The study identified 58 overlapping genes between DLBCL-signature genes and hypoxia-related genes.
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Role of hypoxia in Diffuse Large B-cell Lymphoma: Metabolic repression and selective translation of HK2 facilitates development of DLBCL
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GSM361240
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GSM361242
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GSM361243
[419]
GSM361244
sample_count
420
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Prediction of survival in diffuse large B cell lymphoma treated with chemotherapy plus Rituximab
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
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[3] object · 15 fields
id
data:geo:GSE56315
role
raw
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geo-series
media_type
application/vnd.ncbi.geo-series+json
uri
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56315
sha256
null
bytes
null
access
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null
reader_status
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version
null
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Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10846 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56315 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104212.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10846 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56315 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104212.
access_statement_kind
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GSM1358939
[1]
GSM1358940
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GSM1358941
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GSM1358942
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GSM1358943
[5]
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[6]
GSM1358945
[7]
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[8]
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GSM1358948
[10]
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[11]
GSM1358950
[12]
GSM1358951
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GSM1358952
[14]
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[15]
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[16]
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[17]
GSM1358958
[18]
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[19]
GSM1358960
[20]
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[21]
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[22]
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[23]
GSM1358969
[24]
GSM1358972
[25]
GSM1358975
[26]
GSM1358976
[27]
GSM1358977
[28]
GSM1358980
[29]
GSM1358982
[30]
GSM1358983
[31]
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[32]
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[33]
GSM1358986
[34]
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[35]
GSM1358988
[36]
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[37]
GSM1358992
[38]
GSM1358994
[39]
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[40]
GSM1358996
[41]
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[42]
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[45]
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[47]
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[48]
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[49]
GSM1359005
[50]
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[52]
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[85]
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[87]
GSM1359060
sample_count
88
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Diffuse Large B-Cell Lymphoma Classification System That Associates Normal B-Cell Subset Phenotypes With Prognosis
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
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false
licence_read
false
gaps array · 6 items
[0]
asset:data:geo:GSE104212 not downloaded
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asset:data:geo:GSE104212 licence not read
[2]
asset:data:geo:GSE10846 not downloaded
[3]
asset:data:geo:GSE10846 licence not read
[4]
asset:data:geo:GSE56315 not downloaded
[5]
asset:data:geo:GSE56315 licence not read
analysis object · 4 fields
state
partial
methods array · 4 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
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time_to_event_with_censoring
evidence object · 3 fields
score
0.8
band
high
quote
Kaplan-Meier
[1] object · 10 fields
id
method-2
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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
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edge_list
evidence object · 3 fields
score
0.7
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PPI
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id
method-3
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network_analysis
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Network / pathway analysis
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pathway_enrich
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parameters object · 0 fields
data_shapes array · 1 item
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edge_list
evidence object · 3 fields
score
0.8
band
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quote
GO enrichment
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id
method-4
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null
specific
null
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Transcriptome-wide differential expression analysis using fold change and t-test
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
Transcriptome-wide differential expression analysis was systematically conducted according to fold change (FC) and t-test.
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
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meta_analysis
[1]
machine_learning
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survival_analysis
[3]
statistical_regression
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pca
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molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
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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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true
required_operations array · 1 item
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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]
GSE56315
[1]
GSE104212
[2]
GSE10846
resolved_accessions array · 3 items
[0]
GSE56315
[1]
GSE104212
[2]
GSE10846
statements array · 1 item
[0]
Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10846 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56315 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104212.
basis
The paper names GSE56315, GSE104212, GSE10846 in NCBI 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 · 1 item
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survival_km
assets array · 2 items
[0]
source:pmid:41189944
[1]
data:geo:GSE104212
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:GSE104212
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
2026-11-01T20:04:50Z
independence
single_reader
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method
verbatim
availability
verbatim
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5
usage object · 3 fields
prompt_tokens
10802
completion_tokens
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cached_tokens
256
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id
extraction
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reader
MiniMax-M3.1-Flash
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minimax
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review.independent_reader
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review.human_critique
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gaps array · 1 item
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execution.not_run
provenance object · 7 fields
entities array · 3 items
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type
paper
label
pmid:41189944
[1] object · 3 fields
id
source:pmid:41189944
type
manuscript
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PMID 41189944 full text
[2] object · 3 fields
id
data:geo:GSE104212
type
dataset
label
GEO GSE104212
activities array · 3 items
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activity-reading-sci-2026-11-01
type
classification
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pmid:41189944
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source:pmid:41189944
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PMID 41189944 full text
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data:geo:GSE104212
type
dataset
label
GEO GSE104212
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agent:corpus-assay-engine
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extraction
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agent:minimax-reader
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activity-resolve-GSE104212
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used array · 1 item
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data:geo:GSE104212
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agent:corpus-assay-engine
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agent:corpus-assay-engine
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lineage object · 4 fields
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sha256:20f3fdbdf0c8d3c6fdb8319e1e2a4f8feb5f17ce7d35fe8184b00cce05aa9394
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[0]
tion quantitative polymerase chain reaction (RT-qPCR). Results: 58 overlapping genes were identif
[1]
n quantitative polymerase chain reaction (RT-qPCR). Results: 58 overlapping genes were identif
[2]
tion quantitative polymerase chain reaction (RT-qPCR) analysis on the 6 genes with clinical signi
[3]
n quantitative polymerase chain reaction (RT-qPCR) analysis on the 6 genes with clinical signi
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paper_text_order:1276
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99e1195c7d6f
confidence
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[0]
iao Tong University in Shanghai, China, were cultured in RPMI-1640 medium (Gibco, USA) supplemented w
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41189944", "kind": "science-record", "revision": 3, "content_hash": "sha256:a1ddfef3062db9b739fcaa7e217f88aed21a9da13a00197c9a66184d6d5c0084", "created_at": "2026-11-01T20:31:13Z", "updated_at": "2026-11-01T22:14:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Unveiling hypoxic regulatory networks by bioinformatics: mechanisms of hypoxia-related hub genes driving rituximab resistance and poor prognosis in DLBCL.", "doi": "10.3389/fonc.2025.1592441", "pmid": "41189944", "authors": ["Jiayi Yao", "Juan Huang", "Yuanfei Mao", "Zizhen Xu"], "journal": "Frontiers in oncology", "year": 2025, "abstract": "Background: Diffuse large B-cell lymphoma (DLBCL), an aggressive subtype of non-Hodgkin lymphoma, exhibits heterogeneous clinical outcomes. While rituximab, a CD20 inhibitor, combined with chemotherapy has improved survival in some patients, resistance remains prevalent, particularly in hypoxic tumor microenvironments. Understanding hypoxia-related genes (HRGs) and their role in rituximab resistance is critical to addressing therapeutic challenges in high-risk DLBCL.\nMethods: Gene expression profiles from GEO datasets (GSE56315: DLBCL tumor vs. normal; GSE104212: hypoxia-treated DLBCL cell lines) were analyzed to identify overlapping genes between DLBCL-signature genes (DSGs) and HRGs. protein interaction network topology analysis and Lasso regression modeling of TCGA-DLBC dataset were employed to screen regulator and hub genes. Hub genes linked to rituximab response and survival were validated in DLBCL patients receiving rituximab therapy. Functional enrichment analysis was used to explore associated pathways. The expression of the identified regulator and hub genes was validated using reverse transcription quantitative polymerase chain reaction (RT-qPCR).\nResults: 58 overlapping genes were identified between DSGs and HRGs. PPI network and Lasso regression revealed 5 MS4A1 regulator genes and 10 hub genes. Among these, LGALS1 (HR = 0.588, p = 0.00085), TIMP1 (HR = 0.591, p = 0.00098), ANXA1 (HR = 0.614, p=0.0024) and STAP1 (HR = 0.633, p=0.0035) were significantly associated with overall survival and GPNMB (AUC = 0.869), CDCA7 (AUC = 0.686), and STAP1 (AUC = 0.663) associated with treatment response in rituximab-treated patients. Functional analysis implicated these genes in B-cell receptor (BCR) and PI3K-AKT signaling pathways, suggesting their mechanistic roles in therapeutic resistance.\nConclusions: This study identifies hypoxia-associated genes critical to rituximab resistance in DLBCL, highlighting potential therapeutic targets. Their involvement in BCR and PI3K-AKT pathways underscores novel vulnerabilities for overcoming refractory disease. Our findings provide a foundation for developing strategies to improve outcomes in high-risk DLBCL patients with hypoxic microenvironments.", "claims": [{"id": "claim-method", "statement": "Transcriptome-wide differential expression analysis was systematically conducted according to fold change (FC) and t-test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41189944", "locator": "sections[4] \"Identification of differential expression genes\" paragraph 2", "quote": "Transcriptome-wide differential expression analysis was systematically conducted according to fold change (FC) and t-test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10846 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56315 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104212.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41189944", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10846 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56315 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104212."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified 58 overlapping genes between DLBCL-signature genes and hypoxia-related genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41189944", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "58 overlapping genes were identified between DSGs and HRGs."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified 58 overlapping genes between DLBCL-signature genes and hypoxia-related genes.", "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:41189944"], "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:GSE104212"], "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:GSE104212"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41189944", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2025.1592441", "sha256": "sha256:b36f22b4eafca0430abf0bf8e0f24868c9e75018badaf13c33bbbf6943df554c", "bytes": 39581, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2025.1592441", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12580093", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE104212", "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=GSE104212", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE104212", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10846 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56315 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104212.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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