Evidence map›Paper›PMID 38201487›Full record

ReviewCancers2023

Multilevel Heterogeneity of Colorectal Cancer Liver Metastasis.

Hao Chen, Chongya Zhai, Xian Xu, Haidong Wang, Weidong Han, Jiaying Shen

Open access · goldAbstract readReview
In one paragraph

Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 24% of its field
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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
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

6 authors at 1 institution in 1 country.

Hao ChenDepartment of Medical Oncology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, China.
Chongya ZhaiDepartment of Medical Oncology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, China.
Xian XuDepartment of Medical Oncology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, China.
Haidong WangDepartment of Medical Oncology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, China.
Weidong HanDepartment of Medical Oncology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, China.ORCID 0000-0001-7227-3671
Jiaying ShenDepartment of Medical Oncology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, China.
Sir Run Run Shaw Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colorectal cancer liver metastasis (CRLM) is a highly heterogeneous disease. Therapies that target both primary foci and liver metastasis are severely lacking. Therefore, understanding the features of metastatic tumor cells in the liver is valuable for the overall control of CRLM patients. In this review, we summarize the heterogeneity exhibited in CRLM from five aspects (gene, transcriptome, protein, metabolism, and immunity). In addition to genetic heterogeneity, the other four aspects exhibit significant heterogeneity. Compared to primary CRC, the dysregulation of epithelial-mesenchymal transition (EMT)-related proteins, the enhanced metabolic activity, and the increased infiltration of immunosuppressive cells are detected in CRLM. Preclinical evidence shows that targeting the EMT process or enhancing cellular metabolism may represent a novel approach to increasing the therapeutic efficacy of CRLM.

Indexed as

colorectal cancer liver metastasisgeneheterogeneityimmunemetabolismproteintherapytranscriptome

Identifiers

PMID38201487
PMCPMC10778489
OpenAlexW4390061596

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.