A novel unsupervised algorithm for biological process-based analysis on cancer

Tianci Song, Sha Cao, Sheng Tao, Sen Liang, Wei Du, Yanchun Liang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The aberrant alterations of biological functions are well known in tumorigenesis and cancer development. Hence, with advances in high-throughput sequencing technologies, capturing and quantifying the functional alterations in cancers based on expression profiles to explore cancer malignant process is highlighted as one of the important topics among cancer researches. In this article, we propose an algorithm for quantifying biological processes by using gene expression profiles over a sample population, which involves the idea of constructing principal curves to condense information of each biological process by a novel scoring scheme on an individualized manner. After applying our method on several large-scale breast cancer datasets in survival analysis, a subset of these biological processes extracted from corresponding survival model is then found to have significant associations with clinical outcomes. Further analyses of these biological processes enable the study of the interplays between biological processes and cancer phenotypes of interest, provide us valuable insights into cancer biology in biological process level and guide the precision treatment for cancer patients. And notably, prognosis predictions based on our method are consistently superior to the existing state of art methods with the same intention.

Original languageEnglish (US)
Article number4671
JournalScientific Reports
Volume7
Issue number1
DOIs
StatePublished - Dec 1 2017
Externally publishedYes

Fingerprint

Biological Phenomena
Neoplasms
Survival Analysis
Transcriptome
Carcinogenesis
Breast Neoplasms
Technology
Phenotype
Survival
Research

ASJC Scopus subject areas

  • General

Cite this

A novel unsupervised algorithm for biological process-based analysis on cancer. / Song, Tianci; Cao, Sha; Tao, Sheng; Liang, Sen; Du, Wei; Liang, Yanchun.

In: Scientific Reports, Vol. 7, No. 1, 4671, 01.12.2017.

Research output: Contribution to journalArticle

Song, Tianci ; Cao, Sha ; Tao, Sheng ; Liang, Sen ; Du, Wei ; Liang, Yanchun. / A novel unsupervised algorithm for biological process-based analysis on cancer. In: Scientific Reports. 2017 ; Vol. 7, No. 1.
@article{cd586856304e4d4cb03a808f4b81e823,
title = "A novel unsupervised algorithm for biological process-based analysis on cancer",
abstract = "The aberrant alterations of biological functions are well known in tumorigenesis and cancer development. Hence, with advances in high-throughput sequencing technologies, capturing and quantifying the functional alterations in cancers based on expression profiles to explore cancer malignant process is highlighted as one of the important topics among cancer researches. In this article, we propose an algorithm for quantifying biological processes by using gene expression profiles over a sample population, which involves the idea of constructing principal curves to condense information of each biological process by a novel scoring scheme on an individualized manner. After applying our method on several large-scale breast cancer datasets in survival analysis, a subset of these biological processes extracted from corresponding survival model is then found to have significant associations with clinical outcomes. Further analyses of these biological processes enable the study of the interplays between biological processes and cancer phenotypes of interest, provide us valuable insights into cancer biology in biological process level and guide the precision treatment for cancer patients. And notably, prognosis predictions based on our method are consistently superior to the existing state of art methods with the same intention.",
author = "Tianci Song and Sha Cao and Sheng Tao and Sen Liang and Wei Du and Yanchun Liang",
year = "2017",
month = "12",
day = "1",
doi = "10.1038/s41598-017-04961-6",
language = "English (US)",
volume = "7",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",

}

TY - JOUR

T1 - A novel unsupervised algorithm for biological process-based analysis on cancer

AU - Song, Tianci

AU - Cao, Sha

AU - Tao, Sheng

AU - Liang, Sen

AU - Du, Wei

AU - Liang, Yanchun

PY - 2017/12/1

Y1 - 2017/12/1

N2 - The aberrant alterations of biological functions are well known in tumorigenesis and cancer development. Hence, with advances in high-throughput sequencing technologies, capturing and quantifying the functional alterations in cancers based on expression profiles to explore cancer malignant process is highlighted as one of the important topics among cancer researches. In this article, we propose an algorithm for quantifying biological processes by using gene expression profiles over a sample population, which involves the idea of constructing principal curves to condense information of each biological process by a novel scoring scheme on an individualized manner. After applying our method on several large-scale breast cancer datasets in survival analysis, a subset of these biological processes extracted from corresponding survival model is then found to have significant associations with clinical outcomes. Further analyses of these biological processes enable the study of the interplays between biological processes and cancer phenotypes of interest, provide us valuable insights into cancer biology in biological process level and guide the precision treatment for cancer patients. And notably, prognosis predictions based on our method are consistently superior to the existing state of art methods with the same intention.

AB - The aberrant alterations of biological functions are well known in tumorigenesis and cancer development. Hence, with advances in high-throughput sequencing technologies, capturing and quantifying the functional alterations in cancers based on expression profiles to explore cancer malignant process is highlighted as one of the important topics among cancer researches. In this article, we propose an algorithm for quantifying biological processes by using gene expression profiles over a sample population, which involves the idea of constructing principal curves to condense information of each biological process by a novel scoring scheme on an individualized manner. After applying our method on several large-scale breast cancer datasets in survival analysis, a subset of these biological processes extracted from corresponding survival model is then found to have significant associations with clinical outcomes. Further analyses of these biological processes enable the study of the interplays between biological processes and cancer phenotypes of interest, provide us valuable insights into cancer biology in biological process level and guide the precision treatment for cancer patients. And notably, prognosis predictions based on our method are consistently superior to the existing state of art methods with the same intention.

UR - http://www.scopus.com/inward/record.url?scp=85049685283&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049685283&partnerID=8YFLogxK

U2 - 10.1038/s41598-017-04961-6

DO - 10.1038/s41598-017-04961-6

M3 - Article

C2 - 28680165

AN - SCOPUS:85049685283

VL - 7

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 4671

ER -