Evidence map›Paper›PMID 28943898›Full record

ArticleOncology letters2017

Metastasis-associated in colon cancer 1: A promising biomarker for the metastasis and prognosis of colorectal cancer.

He Li, Yi-Xin Chen, Jia-Gen Wen, Hong-Hao Zhou

Abstract read
In one paragraph

Article in Oncology letters, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. The impacts ofJournal of Cancer · 2023
    Article
  5. Article
  6. Review
  7. Article
  8. Genetic Variants ofInternational journal of molecular sciences · 2021
    Article
  9. Article
  10. Article
  11. Effects ofJournal of Cancer · 2020
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. 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

4 authors.

He LiDepartment of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, P.R. China.
Yi-Xin ChenDepartment of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, P.R. China.
Jia-Gen WenDepartment of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, P.R. China.
Hong-Hao ZhouDepartment of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colorectal cancer (CRC) is the fourth most frequent type of malignancy in the world. Metastasis accounts for >90% mortalities in patients with CRC. The metastasis-associated in colon cancer 1 (MACC1) gene has been identified as a novel biomarker for the prediction of metastasis and disease prognosis, particularly for patients with early-stage disease. Previous clinical studies demonstrated that MACC1 expression and polymorphisms in CRC tissues were indicators of metastasis, and that circulating transcripts in plasma were also significantly associated with the survival of patients. The present review describes the use of MACC1 beyond its utility in the clinic. By elucidating the upstream and downstream signal pathways of MACC1, the well-known mechanisms of MACC1-mediated cell proliferation, invasion, migration and epithelial-mesenchymal transition (EMT) are summarized, as well as the potential signaling pathways. Furthermore, the underlying mechanisms by which the overexpression of MACC1 causes cisplatin resistance are emphasized.

Indexed as

biomarkercolorectal cancermechanismmetastasismetastasis-associated in colon cancer 1

Identifiers

PMID28943898
PMCPMC5605967

What OpenQuestion holds

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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.