Evidence map›Paper›PMID 25339833›Full record

ArticleWorld journal of gastroenterology2014

Gene expression profile of peripheral blood in colorectal cancer.

Yu-Tien Chang, Chi-Shuan Huang, Chung-Tay Yao, Sui-Lung Su, Harn-Jing Terng, Hsiu-Ling Chou, Yu-Ching Chou, Kang-Hua Chen, Yun-Wen Shih, Chian-Yu Lu and 9 more

Open access · hybridAbstract readValidation Study
In one paragraph

Article in World journal of gastroenterology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 20% 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

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.

  1. Pooled it
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  3. Transcriptome Profiling Associated withInternational journal of molecular sciences · 2024
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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

19 authors at 1 institution in 1 country.

Yu-Tien ChangYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Chi-Shuan HuangYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Chung-Tay YaoYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Sui-Lung SuYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Harn-Jing TerngYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Hsiu-Ling ChouYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Yu-Ching ChouYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Kang-Hua ChenYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Yun-Wen ShihYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Chian-Yu LuYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Ching-Huang LaiYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Chen-En JianYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Chiao-Huang LinYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Chien-Ting ChenYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Yi-Syuan WuYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Ke-Shin LinYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Thomas WetterYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Chi-Wen ChangYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Chi-Ming ChuYu-Tien Chang, Yun-Wen Shih, Chen-En Jian, Chiao-Huang Lin, Chien-Ting Chen, Yi-Syuan Wu, Ke-Shin Lin, Chi-Ming Chu, Division of Biomedical Statistics and Informatics, School of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Ten Chen Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimOptimal molecular markers for detecting colorectal cancer (CRC) in a blood-based assay were evaluated.

methodsA matched (by variables of age and sex) case-control design (111 CRC and 227 non-cancer samples) was applied. Total RNAs isolated from the 338 blood samples were reverse-transcribed, and the relative transcript levels of candidate genes were analyzed. The training set was made of 162 random samples of the total 338 samples. A logistic regression analysis was performed, and odds ratios for each gene were determined between CRC and non-cancer. The samples (n = 176) in the testing set were used to validate the logistic model, and an inferred performance (generality) was verified. By pooling 12 public microarray datasets(GSE 4107, 4183, 8671, 9348, 10961, 13067, 13294, 13471, 14333, 15960, 17538, and 18105), which included 519 cases of adenocarcinoma and 88 controls of normal mucosa, we were able to verify the selected genes from logistic models and estimate their external generality.

resultsThe logistic regression analysis resulted in the selection of five significant genes (P < 0.05; MDM2, DUSP6, CPEB4, MMD, and EIF2S3), with odds ratios of 2.978, 6.029, 3.776, 0.538 and 0.138, respectively. The five-gene model performed stably for the discrimination of CRC cases from controls in the training set, with accuracies ranging from 73.9% to 87.0%, a sensitivity of 95% and a specificity of 95%. In addition, a good performance in the test set was obtained using the discrimination model, providing 83.5% accuracy, 66.0% sensitivity, 92.0% specificity, a positive predictive value of 89.2% and a negative predictive value of 73.0%. Multivariate logistic regressions analyzed 12 pooled public microarray data sets as an external validation. Models that provided similar expected and observed event rates in subgroups were termed well calibrated. A model in which MDM2, DUSP6, CPEB4, MMD, and EIF2S3 were selected showed the result in logistic regression analysis (H-L P = 0.460, R2= 0.853, AUC = 0.978, accuracy = 0.949, specificity = 0.818 and sensitivity = 0.971).

conclusionA novel gene expression profile was associated with CRC and can potentially be applied to blood-based detection assays.

Indexed as

Gene Expression ProfilingAdenocarcinomaAgedBiomarkers, TumorChi-Square DistributionColorectal NeoplasmsFemaleGenetic Predisposition to DiseaseHumansLogistic ModelsMaleMultivariate AnalysisOdds RatioOligonucleotide Array Sequence AnalysisPhenotypePredictive Value of TestsBiomarkers, TumorColorectal cancerGene expressionInternetMicroarray

Identifiers

PMID25339833
PMCPMC4202375
OpenAlexW2132282429

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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.