Evidence map›Paper›PMID 38902737›Full record

ArticleJournal of translational medicine2024

Novel hypoxia- and lactate metabolism-related molecular subtyping and prognostic signature for colorectal cancer.

An Huang, Zhuang Sun, Haopeng Hong, Yong Yang, Jiajia Chen, Zhaoya Gao, Jin Gu

Abstract read
In one paragraph

Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed.

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  12. Cancers · 2026
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  16. The consensus molecular subtypes of esophageal squamous cell carcinoma.Signal transduction and targeted therapy · 2026
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  17. Protein Lactylation in Liver Disease: A Comprehensive Review.Expert reviews in molecular medicine · 2026
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

An Huang *Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Surgery III, Peking University Cancer Hospital & Institute, Haidian District, Beijing, 100142, China.
Zhuang Sun *Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Surgery III, Peking University Cancer Hospital & Institute, Haidian District, Beijing, 100142, China.
Haopeng HongKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Surgery III, Peking University Cancer Hospital & Institute, Haidian District, Beijing, 100142, China.
Yong YangDepartment of Gastrointestinal Surgery, Peking University Shougang Hospital, Beijing, 100144, China.
Jiajia ChenDepartment of Gastrointestinal Surgery, Peking University Shougang Hospital, Beijing, 100144, China.
Zhaoya GaoDepartment of Gastrointestinal Surgery, Peking University Shougang Hospital, Beijing, 100144, China.
Jin GuKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Surgery III, Peking University Cancer Hospital & Institute, Haidian District, Beijing, 100142, China. zlguj@bjmu.edu.cn.ORCID 0000-0002-9650-1963

Funding

National Natural Science Foundation of China 82073223
6 · The paper itself

Abstract

backgroundColorectal cancer (CRC) is a serious global health burden because of its high morbidity and mortality rates. Hypoxia and massive lactate production are hallmarks of the CRC microenvironment. However, the effects of hypoxia and lactate metabolism on CRC have not been fully elucidated. This study aimed to develop a novel molecular subtyping based on hypoxia-related genes (HRGs) and lactate metabolism-related genes (LMRGs) and construct a signature to predict the prognosis of patients with CRC and treatment efficacy.

methodsBulk and single-cell RNA-sequencing and clinical data of CRC were downloaded from the TCGA and GEO databases. HRGs and LMRGs were obtained from the Molecular Signatures Database. The R software package DESeq2 was used to perform differential expression analysis. Molecular subtyping was performed using unsupervised clustering. A predictive signature was developed using univariate Cox regression, random forest model, LASSO, and multivariate Cox regression analyses. Finally, the sensitivity of tumor cells to chemotherapeutic agents before and after hypoxia was verified using in vitro experiments.

resultsWe classified 575 patients with CRC into three molecular subtypes and were able to distinguish their prognoses clearly. The C1 subtype, which exhibits high levels of hypoxia, has a low proportion of CD8 + T cells and a high proportion of macrophages. The expression of immune checkpoint genes is generally elevated in C1 patients with severe immune dysfunction. Subsequently, we constructed a predictive model, the HLM score, which effectively predicts the prognosis of patients with CRC and the efficacy of immunotherapy. The HLM score was validated in GSE39582, GSE106584, GSE17536, and IMvigor210 datasets. Patients with high HLM scores exhibit high infiltration of CD8 + exhausted T cells (Tex), especially terminal Tex, and oxidative phosphorylation (OXPHOS)-Tex in the immune microenvironment. Finally, in vitro experiments confirmed that CRC cell lines were less sensitive to 5-fluorouracil, oxaliplatin, and irinotecan under hypoxic conditions.

conclusionWe constructed novel hypoxia- and lactate metabolism-related molecular subtypes and revealed their immunological and genetic characteristics. We also developed an HLM scoring system that could be used to predict the prognosis and efficacy of immunotherapy in patients with CRC.

Indexed as

Colorectal NeoplasmsLactic AcidAntineoplastic AgentsCell HypoxiaCell Line, TumorFemaleGene Expression Regulation, NeoplasticHumansHypoxiaMaleMiddle AgedPrognosisTumor MicroenvironmentAntineoplastic AgentsLactic AcidColorectal cancerHypoxiaImmune microenvironmentLactate metabolismMolecular subtypePrognosis

Identifiers

PMID38902737
PMCPMC11191174

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.