Evidence map›Paper›PMID 25182706›Full record

Trial reportMedical oncology (Northwood, London, England)2014

Serum human kallikrein 7 represents a new marker for cervical cancer.

Weiwei Li, Yi Zhao, Lina Ren, Xin Wu

Abstract readClinical Trial
PubMed Publisher
In one paragraph

Trial report in Medical oncology (Northwood, London, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

4 authors.

Weiwei LiDepartment of Obstetrics and Gynecology, First Hospital of China Medical University, North Nanjing Street, No. 155, Shenyang, 110001, China.
Yi Zhao
Lina Ren
Xin Wu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Our study was aimed to investigate the diagnostic and prognostic performance of serum human tissue kallikrein 7 (KLK7) in cervical cancer. The study comprised of 78 patients with cervical cancer, 36 patients with benign cervical diseases, and 50 healthy controls. Serum KLK7 levels were determined using ELISA method. Receiver operating characteristic curve was performed to assess the diagnostic performance of serum KLK7. Survival curves by the Kaplan-Meier method were plotted to display overall survival distributions. Univariable and multivariable Cox regression analysis were performed to assess independent prognostic factors for overall survival in cervical cancer. Serum KLK7 levels in cervical cancer patients were 3.13±1.99 μg/ml, significantly higher than the values obtained from patients with benign cervical disease (1.40±1.14 μg/ml; P<0.001) and healthy controls (1.27±0.73 μg/ml; P<0.001), respectively. The sensitivity, specificity, positive and negative predictive values of serum KLK7 for cervical cancer were 75.6, 80.2, 77.6, and 78.4%, respectively. Serum KLK7 levels in patients with cervical cancer were significantly correlated with FIGO stage, lymph node metastasis, and stromal invasion. When analyzed with a survival curve, high serum KLK7 level was correlated with poor prognosis. Our findings suggest that serum KLK7 may be a valuable diagnostic biomarker for cervical cancer, and may help to determine the individual prognosis of these patients.

Indexed as

AdultAgedBiomarkers, TumorEnzyme-Linked Immunosorbent AssayFemaleHumansKallikreinsMiddle AgedNeoplasm ProteinsPredictive Value of TestsPrognosisROC CurveSensitivity and SpecificitySurvival RateUterine Cervical NeoplasmsBiomarkers, TumorKallikreinsKLK7 protein, humanNeoplasm Proteins

Identifiers

What OpenQuestion holds

Textmetadata
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.