Evidence map›Paper›PMID 22969891›Full record

ArticleExperimental and therapeutic medicine2012

Elevated thymidine kinase 1 in serum following neoadjuvant chemotherapy predicts poor outcome for patients with locally advanced breast cancer.

Zhi-Heng Huang, Xing-Song Tian, Rong Li, Xian-Ming Wang, Wen Wen, Hong Guan, Ya-Jie Yang

Open access · bronzeAbstract read
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Article in Experimental and therapeutic medicine, 2012. 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
1.1field-weighted citation impact, top 23% 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, 14 citations in OpenAlex.

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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 at 1 institution in 1 country.

Zhi-Heng HuangDepartment of Breast Surgery, Shandong Province Hospital, Shandong University, Jinan 250012;
Xing-Song Tian
Rong Li
Xian-Ming Wang
Wen Wen
Hong Guan
Ya-Jie Yang
Shandong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patients with locally advanced breast cancer (LABC) commonly have an unfavorable prognosis. A molecular predictor for the identification of at-risk patients is urgently required. Thymidine kinase 1 in serum (S-TK1) is an enzyme involved in the synthesis of DNA precursors. In studies using immunohistochemistry, it was reported to be a more useful proliferation marker than Ki-67 in breast, lung and colorectal carcinoma. In the present study, we extended the research of prior breast carcinoma studies by postulating that in patients with LABC, overexpression of S-TK1 following neoadjuvant chemotherapy predicts cancer outcome. An experimental design consisting of 48 patients with LABC was prospectively constructed and analyzed. All patients received neoadjuvant chemotherapy and definitive surgical therapy. Study homogeneity was maintained by standardized treatment, surveillance and compliance protocols. The S-TK1 concentration was detected using the anti-TK1 chicken IgY antibody, using a dot-blot immuno-assay. After a median follow-up of 30 months, the results indicated a statistically significant trend (unadjusted). Patients with high S-TK1 overexpression had a significantly higher incidence of recurrence (P=0.006) and cancer death (P= 0.0128) than those with low S-TK1 overexpression. A multivariate analysis provided identical results. The hazards ratio for developing recurrence in patients with higher S-TK1 expression was 6-7 times higher than the hazards ratio in patients with lower expression. In conclusion, our results indicate that a high S-TK1 concentration in sera from LABC patients receiving neoadjuvant chemotherapy is predictive of cancer outcome.

Identifiers

PMID22969891
PMCPMC3438657
OpenAlexW2060182107

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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.