Evidence map›Paper›PMID 33436913›Full record

ArticleScientific reports2021

Precise diagnosis of three top cancers using dbGaP data.

Xu-Qing Liu, Xin-Sheng Liu, Jian-Ying Rong, Feng Gao, Yan-Dong Wu, Chun-Hua Deng, Hong-Yan Jiang, Xiao-Feng Li, Ye-Qin Chen, Zhi-Guo Zhao and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

18 authors.

Xu-Qing LiuHuaiyin Institute of Technology, Huaian, 223003, China. liuxuqing688@163.com.
Xin-Sheng LiuNanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
Jian-Ying RongJiangsu Vocational College of Electronics and Information, Huaian, 223003, China.
Feng GaoHuaiyin Institute of Technology, Huaian, 223003, China.
Yan-Dong WuHuaiyin Institute of Technology, Huaian, 223003, China.
Chun-Hua DengHuaiyin Institute of Technology, Huaian, 223003, China.
Hong-Yan JiangHuaiyin Institute of Technology, Huaian, 223003, China.
Xiao-Feng LiHuaiyin Institute of Technology, Huaian, 223003, China.
Ye-Qin ChenJiangsu Vocational College of Electronics and Information, Huaian, 223003, China.
Zhi-Guo ZhaoHuaiyin Institute of Technology, Huaian, 223003, China.
Yu-Ting LiuHuaiyin Institute of Technology, Huaian, 223003, China.
Hai-Wen ChenHuaiyin Institute of Technology, Huaian, 223003, China.
Jun-Liang LiHuaiyin Institute of Technology, Huaian, 223003, China.
Yu HuangHuaiyin Institute of Technology, Huaian, 223003, China.
Cheng-Yao JiHuaiyin Institute of Technology, Huaian, 223003, China.
Wen-Wen LiuHuaiyin Institute of Technology, Huaian, 223003, China.
Xiao-Hu LuoHuaiyin Institute of Technology, Huaian, 223003, China.
Li-Li XiaoHuaiyin Institute of Technology, Huaian, 223003, China.

Funding

Statistical MethodsP01CA087969 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ROSNER, BERNARD A · 2000 to 2019
$77.8M
UNIVERSITY OF TEXAS--SPORE IN LUNG CANCERP50CA070907 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI HEYMACH, JOHN V., MINNA, JOHN D. · 1996 to 2024
$57.4M
Risk Factors for Breast Cancer in Younger NursesR01CA050385 · NCI · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI ELIASSEN, A. HEATHER, WILLETT, WALTER C. · 1989 to 2019
$32.3M
GENETIC SUSCEPTIBILITY TO CANCER IN MULTIETHNIC COHORTSR01CA063464 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HENDERSON, BRIAN E · 2001 to 2005
$23.2M
Multiethnic Cohort Study of Diet and CancerR37CA054281 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI KOLONEL, LAURENCE N. · 2003 to 2012
$23.2M
VALIDATION OF A DIET HISTORY METHOD FOR EPIDEMIOLOGIC STUDIES OF CANCERP01CA033619 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI KOLONEL, LAURENCE N. · 1985 to 2010
$18.5M
Premonopausal Hormone Levels and Risk of Breast CancerR01CA067262 · NCI · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI HANKINSON, SUSAN E · 2002 to 2011
$11.9M
GENETIC EPIDEMIOLOGY OF LUNG CANCERU01CA076293 · NCI · UNIVERSITY OF CINCINNATI · PI ANDERSON, MARSHALL W · 1999 to 2009
$11.5M
Translational studies of Hsp90 inhibitors in NSCLCP50CA090578 · NCI · DANA-FARBER CANCER INST · PI YEAP, BEOW Y · 2008 to 2012
$11.4M
BIOCHEMICAL MARKERS IN THE NURSES'HEALTH STUDY COHORTR01CA049449 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI HANKINSON, SUSAN E · 1989 to 2008
$11.3M
Characterizing Genetic Susceptibility to Breast and Prostate Cancer; the BPC3U01CA098758 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HENDERSON, BRIAN E · 2003 to 2010
$11.0M
Characterizing Genetic Susceptibility to Breast and Prostate Cancer; the BPC3U01CA098233 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI HUNTER, DAVID JOHN · 2003 to 2010
$10.8M
NCI NIH HHS P01 CA033619NCI NIH HHS P01 CA087969NCI NIH HHS P50 CA070907NCI NIH HHS P50 CA090578NCI NIH HHS R01 CA049449NCI NIH HHS R01 CA050385NCI NIH HHS R01 CA054281NCI NIH HHS R01 CA055769NCI NIH HHS R01 CA060691NCI NIH HHS R01 CA063464NCI NIH HHS R01 CA065725NCI NIH HHS R01 CA067262NCI NIH HHS R01 CA074386NCI NIH HHS R01 CA080127NCI NIH HHS R01 CA084354NCI NIH HHS R01 CA092824NCI NIH HHS R01 CA115857NCI NIH HHS R01 CA121197NCI NIH HHS R01 CA132839NCI NIH HHS R01 CA133996NCI NIH HHS R01 CA149462NCI NIH HHS R03 CA077118NCI NIH HHS R37 CA054281NCI NIH HHS U01 CA049449NCI NIH HHS U01 CA063464NCI NIH HHS U01 CA067262NCI NIH HHS U01 CA076293NCI NIH HHS U01 CA098233NCI NIH HHS U01 CA098758NCI NIH HHS U01 CA136792NHGRI NIH HHS N01 HG065403NHGRI NIH HHS U01 HG004726NHGRI NIH HHS U01 HG005922
6 · The paper itself

Abstract

The challenge of decoding information about complex diseases hidden in huge number of single nucleotide polymorphism (SNP) genotypes is undertaken based on five dbGaP studies. Current genome-wide association studies have successfully identified many high-risk SNPs associated with diseases, but precise diagnostic models for complex diseases by these or more other SNP genotypes are still unavailable in the literature. We report that lung cancer, breast cancer and prostate cancer as the first three top cancers worldwide can be predicted precisely via 240-370 SNPs with accuracy up to 99% according to leave-one-out and 10-fold cross-validation. Our findings (1) confirm an early guess of Dr. Mitchell H. Gail that about 300 SNPs are needed to improve risk forecasts for breast cancer, (2) reveal an incredible fact that SNP genotypes may contain almost all information that one wants to know, and (3) show a hopeful possibility that complex diseases can be precisely diagnosed by means of SNP genotypes without using phenotypical features. In short words, information hidden in SNP genotypes can be extracted in efficient ways to make precise diagnoses for complex diseases.

Indexed as

AlgorithmsBreast NeoplasmsComputational BiologyComputer SimulationDatabases, GeneticFemaleGenome-Wide Association StudyGenotypeHumansLung NeoplasmsMalePhenotypePolymorphism, Single NucleotideProstatic Neoplasms

Identifiers

PMID33436913
PMCPMC7804208

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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.