Evidence map›Paper›PMID 34464504›Full record

ArticleJournal of cellular and molecular medicine2021

CDKN1C as a prognostic biomarker correlated with immune infiltrates and therapeutic responses in breast cancer patients.

Jianguo Lai, Xiaoyi Lin, Fangrong Cao, Hsiaopei Mok, Bo Chen, Ning Liao

Abstract read
In one paragraph

Article in Journal of cellular and molecular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
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  3. Article
  4. Article
  5. Article
  6. Article
  7. ConcomitantJournal of breast cancer · 2023
    Article
  8. The Mechanism of DNA Methylation and miRNA in Breast Cancer.International journal of molecular sciences · 2023
    Review
  9. Article
  10. Article
  11. Article
  12. 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.

Jianguo LaiDepartment of Breast Cancer, Guangdong Provincial People's Hospital,Guangdong Academy of Medical Sciences, Guangzhou, China.ORCID 0000-0002-0120-0245
Xiaoyi LinDepartment of Breast Cancer, Guangdong Provincial People's Hospital,Guangdong Academy of Medical Sciences, Guangzhou, China.
Fangrong CaoDepartment of Breast Cancer, Guangdong Provincial People's Hospital,Guangdong Academy of Medical Sciences, Guangzhou, China.
Hsiaopei MokDepartment of Breast Cancer, Guangdong Provincial People's Hospital,Guangdong Academy of Medical Sciences, Guangzhou, China.
Bo ChenDepartment of Breast Cancer, Guangdong Provincial People's Hospital,Guangdong Academy of Medical Sciences, Guangzhou, China.ORCID 0000-0002-9099-6248
Ning LiaoDepartment of Breast Cancer, Guangdong Provincial People's Hospital,Guangdong Academy of Medical Sciences, Guangzhou, China.ORCID 0000-0002-1780-5652

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer (BC) prognosis and therapeutic sensitivity could not be predicted efficiently. Previous evidence have shown the vital roles of CDKN1C in BC. Therefore, we aimed to construct a CDKN1C-based model to accurately predicting overall survival (OS) and treatment responses in BC patients. In this study, 995 BC patients from The Cancer Genome Atlas database were selected. Kaplan-Meier curve, Gene set enrichment and immune infiltrates analyses were executed. We developed a novel CDKN1C-based nomogram to predict the OS, verified by the time-dependent receiver operating characteristic curve, calibration curve and decision curve. Therapeutic response prediction was followed based on the low- and high-nomogram score groups. Our results indicated that low-CDKN1C expression was associated with shorter OS and lower proportion of naïve B cells, CD8 T cells, activated NK cells. The predictive accuracy of the nomogram for 5-year OS was superior to the tumour-node-metastasis stage (area under the curve: 0.746 vs. 0.634, p < 0.001). The nomogram exhibited excellent predictive performance, calibration ability and clinical utility. Moreover, low-risk patients were identified with stronger sensitivity to therapeutic agents. This tool can improve BC prognosis and therapeutic responses prediction, thus guiding individualized treatment decisions.

Indexed as

Biomarkers, TumorBreast NeoplasmsComputational BiologyCyclin-Dependent Kinase Inhibitor p57Databases, GeneticDisease ManagementDisease SusceptibilityFemaleGene Expression ProfilingHumansKaplan-Meier EstimateLymphocytes, Tumor-InfiltratingNeoplasm StagingPrognosisProportional Hazards ModelsROC CurveBiomarkers, TumorCDKN1C protein, humanCyclin-Dependent Kinase Inhibitor p57breast cancerCDKN1Coverall survivalprognosistherapeutic response

Identifiers

PMID34464504
PMCPMC8500970

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LicenceCC BY
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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.