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pmid:33679749

Hypoxic Characteristic in the Immunosuppressive Microenvironment of Hepatocellular Carcinoma.

Zhuomao Mo · Daiyuan Liu · Dade Rong · Shijun Zhang

Frontiers in immunology · 2021 doi:10.3389/fimmu.2021.611058

Paper

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Experiment

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Data

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pmid:33679749
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2026-11-01T15:41:27Z
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title
Hypoxic Characteristic in the Immunosuppressive Microenvironment of Hepatocellular Carcinoma.
doi
10.3389/fimmu.2021.611058
pmid
33679749
authors array · 4 items
[0]
Zhuomao Mo
[1]
Daiyuan Liu
[2]
Dade Rong
[3]
Shijun Zhang
journal
Frontiers in immunology
year
2021
abstract
Background: Generally, hepatocellular carcinoma (HCC) exists in an immunosuppressive microenvironment that promotes tumor evasion. Hypoxia can impact intercellular crosstalk in the tumor microenvironment. This study aimed to explore and elucidate the underlying relationship between hypoxia and immunotherapy in patients with HCC. Methods: HCC genomic and clinicopathological datasets were obtained from The Cancer Genome Atlas (TCGA-LIHC), Gene Expression Omnibus databases (GSE14520) and International Cancer Genome Consortium (ICGC-LIRI). The TCGA-LIHC cases were divided into clusters based on single sample gene set enrichment analysis and hierarchical clustering. After identifying patients with immunosuppressive microenvironment with different hypoxic conditions, correlations between immunological characteristics and hypoxia clusters were investigated. Subsequently, a hypoxia-associated score was established by differential expression, univariable Cox regression, and lasso regression analyses. The score was verified by survival and receiver operating characteristic curve analyses. The GSE14520 cohort was used to validate the findings of immune cell infiltration and immune checkpoints expression, while the ICGC-LIRI cohort was employed to verify the hypoxia-associated score. Results: We identified hypoxic patients with immunosuppressive HCC. This cluster exhibited higher immune cell infiltration and immune checkpoint expression in the TCGA cohort, while similar significant differences were observed in the GEO cohort. The hypoxia-associated score was composed of five genes (ephrin A3, dihydropyrimidinase like 4, solute carrier family 2 member 5, stanniocalcin 2, and lysyl oxidase). In both two cohorts, survival analysis revealed significant differences between the high-risk and low-risk groups. In addition, compared to other clinical parameters, the established score had the highest predictive performance at both 3 and 5 years in two cohorts. Conclusion: This study provides further evidence of the link between hypoxic signals in patients and immunosuppression in HCC. Defining hypoxia-associated HCC subtypes may help reveal potential regulatory mechanisms between hypoxia and the immunosuppressive microenvironment, and our hypoxia-associated score could exhibit potential implications for future predictive models.
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To further verify the hypoxia-related score, a receiver operating characteristic (ROC) curve was constructed to examine the prognostic accuracy.
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sections[5] "Generation of Hypoxia-Related Score in the Immunosuppressive Cluster" paragraph 2
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To further verify the hypoxia-related score, a receiver operating characteristic (ROC) curve was constructed to examine the prognostic accuracy.
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Publicly available datasets were analyzed in this study. This data can be found here: TCGA database (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), and ICGC database (https://dcc.icgc.org/).
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sections[13] "Data Availability Statement" paragraph 2
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Publicly available datasets were analyzed in this study. This data can be found here: TCGA database (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), and ICGC database (https://dcc.icgc.org/).
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Publicly available datasets were analyzed in this study. This data can be found here: TCGA database (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), and ICGC database (https://dcc.icgc.org/).
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Publicly available datasets were analyzed in this study. This data can be found here: TCGA database (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), and ICGC database (https://dcc.icgc.org/).
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Gene expression data of human hepatocellular carcinoma (HCC)
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Baseline characteristics of patients in two cohorts.
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Identification and Validation of an Immunosuppressive HCC Cluster
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Baseline characteristics of patients in two cohorts.
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[2]
Number
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Percent (%)
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TCGA-LIHC (n = 377)
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[0]
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249
[3]
66
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[0]
[1]
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128
[3]
34
[4] array · 4 items
[0]
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≤ 65 years
[2]
235
[3]
62.5
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[0]
[1]
>65 years
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141
[3]
37.5
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[0]
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122
[3]
68
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255
[3]
32
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TNM Stage (24 patients missing)
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[2]
175
[3]
50
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[1]
II
[2]
87
[3]
24.6
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[0]
[1]
III
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86
[3]
24.4
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[1]
IV
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5
[3]
1
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[0]
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G1
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55
[3]
14
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[1]
G2
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180
[3]
48
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[1]
G3
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124
[3]
33
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5
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185
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49
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26
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22
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3
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ICGC-LIRI (n = 260)
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Survival status
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214
[3]
82.4
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[1]
Death
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46
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17.6
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≤ 65 years
[2]
98
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37.7
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62.3
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15.4
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45
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30.8
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section[8]/table-wrap[1]#T1
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To further verify the hypoxia-related score, a receiver operating characteristic (ROC) curve was constructed to examine the prognostic accuracy.
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The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards.
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Publicly available datasets were analyzed in this study. This data can be found here: TCGA database (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), and ICGC database (https://dcc.icgc.org/).
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data:geo:GSE14520
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33679749", "kind": "science-record", "revision": 4, "content_hash": "sha256:ee90b8f12b65d38d25d28eb658f876b602b27de13492f41f8503168346410e42", "created_at": "2026-11-01T15:41:27Z", "updated_at": "2026-11-01T21:24:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Hypoxic Characteristic in the Immunosuppressive Microenvironment of Hepatocellular Carcinoma.", "doi": "10.3389/fimmu.2021.611058", "pmid": "33679749", "authors": ["Zhuomao Mo", "Daiyuan Liu", "Dade Rong", "Shijun Zhang"], "journal": "Frontiers in immunology", "year": 2021, "abstract": "Background: Generally, hepatocellular carcinoma (HCC) exists in an immunosuppressive microenvironment that promotes tumor evasion. Hypoxia can impact intercellular crosstalk in the tumor microenvironment. This study aimed to explore and elucidate the underlying relationship between hypoxia and immunotherapy in patients with HCC. Methods: HCC genomic and clinicopathological datasets were obtained from The Cancer Genome Atlas (TCGA-LIHC), Gene Expression Omnibus databases (GSE14520) and International Cancer Genome Consortium (ICGC-LIRI). The TCGA-LIHC cases were divided into clusters based on single sample gene set enrichment analysis and hierarchical clustering. After identifying patients with immunosuppressive microenvironment with different hypoxic conditions, correlations between immunological characteristics and hypoxia clusters were investigated. Subsequently, a hypoxia-associated score was established by differential expression, univariable Cox regression, and lasso regression analyses. The score was verified by survival and receiver operating characteristic curve analyses. The GSE14520 cohort was used to validate the findings of immune cell infiltration and immune checkpoints expression, while the ICGC-LIRI cohort was employed to verify the hypoxia-associated score. Results: We identified hypoxic patients with immunosuppressive HCC. This cluster exhibited higher immune cell infiltration and immune checkpoint expression in the TCGA cohort, while similar significant differences were observed in the GEO cohort. The hypoxia-associated score was composed of five genes (ephrin A3, dihydropyrimidinase like 4, solute carrier family 2 member 5, stanniocalcin 2, and lysyl oxidase). In both two cohorts, survival analysis revealed significant differences between the high-risk and low-risk groups. In addition, compared to other clinical parameters, the established score had the highest predictive performance at both 3 and 5 years in two cohorts. Conclusion: This study provides further evidence of the link between hypoxic signals in patients and immunosuppression in HCC. Defining hypoxia-associated HCC subtypes may help reveal potential regulatory mechanisms between hypoxia and the immunosuppressive microenvironment, and our hypoxia-associated score could exhibit potential implications for future predictive models.", "claims": [{"id": "claim-method", "statement": "To further verify the hypoxia-related score, a receiver operating characteristic (ROC) curve was constructed to examine the prognostic accuracy.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33679749", "locator": "sections[5] \"Generation of Hypoxia-Related Score in the Immunosuppressive Cluster\" paragraph 2", "quote": "To further verify the hypoxia-related score, a receiver operating characteristic (ROC) curve was constructed to examine the prognostic accuracy."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: TCGA database (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), and ICGC database (https://dcc.icgc.org/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33679749", "locator": "sections[13] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: TCGA database (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), and ICGC database (https://dcc.icgc.org/)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33679749"], "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:GSE14520"], "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:GSE14520"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33679749", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2021.611058", "sha256": "sha256:1872e8088498441beddf6f680ab89379f774224d36b811db92208712b3db2572", "bytes": 36667, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2021.611058", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7928397", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE14520", "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=GSE14520", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE14520", "version": null, "note": "Publicly available datasets were analyzed in this study. 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