Evidence map›Paper›PMID 29048650›Full record

ArticleOncology reports2017

Predicting radiotherapy response for patients with soft tissue sarcoma by developing a molecular signature.

Zaixiang Tang, Qinghua Zeng, Yan Li, Xinyan Zhang, Mark J Suto, Bo Xu, Nengjun Yi

Abstract read
In one paragraph

Article in Oncology reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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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.

Zaixiang TangDepartment of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu 215123, P.R. China.
Qinghua ZengDrug Discovery Division, Southern Research Institute, Birmingham, AL 35294, USA.
Yan LiDepartment of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
Xinyan ZhangDepartment of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
Mark J SutoDrug Discovery Division, Southern Research Institute, Birmingham, AL 35294, USA.
Bo XuDrug Discovery Division, Southern Research Institute, Birmingham, AL 35294, USA.
Nengjun YiDepartment of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soft tissue sarcomas are rare and aggressive tumors arising from connective tissues. Adjuvant radiotherapy is a commonly used treatment approach for the majority of sarcomas. We attempted to identify a gene signature that can predict radiosensitive patients who are most likely to have a better treatment response from radiotherapy, compared with disease progression. Using the publicly available data of soft tissue sarcoma from The Cancer Genome Atlas, we developed a cross-validation procedure to identify a predictive gene signature for radiosensitivity. The results showed that the predicted radiosensitive patients who received radiotherapy had significantly improved treatment response. We further provide supportive evidence to validate our sensitivity prediction. Results showed that the predicted radiosensitive patients who received radiotherapy had significantly improved survival than patients who did not. ROC analysis showed that the developed gene signature had a powerful prediction on treatment response. We further found that predicted radiosensitive patients who received radiotherapy had a significantly reduced rate of new tumor events. Finally, we validated our gene signature using a hierarchical cluster analysis, and found that the predicted sensitivities were well-matched with results from the cluster analysis. These results are consistent with our expectation, suggesting that the identified gene signature and radiosensitivity prediction are effective. The genes involved in the signature may provide a molecular basis for prognostic studies and radiotherapy target discovery.

Indexed as

AgedCluster AnalysisComputational BiologyDatabases, GeneticDisease ProgressionFemaleGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansMaleMiddle AgedRadiotherapy, AdjuvantSarcomaSequence Analysis, RNASurvival AnalysisTreatment Outcome

Identifiers

PMID29048650
PMCPMC5780036

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.