Evidence map›Paper›PMID 37267125›Full record

ArticleeLife2023

Comprehensive characterization of tumor microenvironment in colorectal cancer via molecular analysis.

Xiangkun Wu, Hong Yan, Mingxing Qiu, Xiaoping Qu, Jing Wang, Shaowan Xu, Yiran Zheng, Minghui Ge, Linlin Yan, Li Liang

Abstract read
In one paragraph

Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

32 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Reinvigorating CD8Journal of nanobiotechnology · 2026
    Article
  6. Review
  7. Review
  8. Review
  9. Applications and development of in situ nucleic acid visualization techniques.Frontiers in bioengineering and biotechnology · 2026
    Review
  10. Article
  11. Review
  12. Article
  13. Review
  14. The role of NADFrontiers in oncology · 2026
    Review
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Review
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

10 authors.

Xiangkun Wu *Department of Pathology, Nanfang Hospital/School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Hong Yan *Department of Pathology, Nanfang Hospital/School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Mingxing Qiu *Department of Pathology, Nanfang Hospital/School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Xiaoping Qu *Nanjing Simcere Medical Laboratory Science Co., Ltd, Nanjing, China.
Jing WangDepartment of Pathology, Nanfang Hospital/School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Shaowan XuDepartment of Pathology, Nanfang Hospital/School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Yiran ZhengNanjing Simcere Medical Laboratory Science Co., Ltd, Nanjing, China.
Minghui GeNanjing Simcere Medical Laboratory Science Co., Ltd, Nanjing, China.
Linlin YanNanjing Simcere Medical Laboratory Science Co., Ltd, Nanjing, China.
Li LiangDepartment of Pathology, Nanfang Hospital/School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.ORCID https://orcid.org/0000-0003-2424-4926

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colorectal cancer (CRC) remains a challenging and deadly disease with high tumor microenvironment (TME) heterogeneity. Using an integrative multi-omics analysis and artificial intelligence-enabled spatial analysis of whole-slide images, we performed a comprehensive characterization of TME in colorectal cancer (CCCRC). CRC samples were classified into four CCCRC subtypes with distinct TME features, namely, C1 as the proliferative subtype with low immunogenicity; C2 as the immunosuppressed subtype with the terminally exhausted immune characteristics; C3 as the immune-excluded subtype with the distinct upregulation of stromal components and a lack of T cell infiltration in the tumor core; and C4 as the immunomodulatory subtype with the remarkable upregulation of anti-tumor immune components. The four CCCRC subtypes had distinct histopathologic and molecular characteristics, therapeutic efficacy, and prognosis. We found that the C1 subtype may be suitable for chemotherapy and cetuximab, the C2 subtype may benefit from a combination of chemotherapy and bevacizumab, the C3 subtype has increased sensitivity to the WNT pathway inhibitor WIKI4, and the C4 subtype is a potential candidate for immune checkpoint blockade treatment. Importantly, we established a simple gene classifier for accurate identification of each CCCRC subtype. Collectively our integrative analysis ultimately established a holistic framework to thoroughly dissect the TME of CRC, and the CCCRC classification system with high biological interpretability may contribute to biomarker discovery and future clinical trial design.

Indexed as

Biomedical ResearchColorectal NeoplasmsArtificial IntelligenceHumansImmunocompromised HostTumor Microenvironmentcancer biologycolorectal cancercomputational biologyhistopathologymolecular classificationmulti-omics analysissystems biologytumor microenvironment

Identifiers

PMID37267125
PMCPMC10238095

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.