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pmid:33282950

A Novel Seventeen-Gene Metabolic Signature for Predicting Prognosis in Colon Cancer.

Dakui Luo · Zezhi Shan · Qi Liu · Sanjun Cai · Qingguo Li · Xinxiang Li

BioMed research international · 2020 doi:10.1155/2020/4845360

Paper

complete

Experiment

partial

Data

partial

Analysis

complete

Execution

absent

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title
A Novel Seventeen-Gene Metabolic Signature for Predicting Prognosis in Colon Cancer.
doi
10.1155/2020/4845360
pmid
33282950
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[0]
Dakui Luo
[1]
Zezhi Shan
[2]
Qi Liu
[3]
Sanjun Cai
[4]
Qingguo Li
[5]
Xinxiang Li
journal
BioMed research international
year
2020
abstract
A metabolic disorder is considered one of the hallmarks of cancer. Multiple differentially expressed metabolic genes have been identified in colon cancer (CC), and their biological functions and prognostic values have been well explored. The purpose of the present study was to establish a metabolic signature to optimize the prognostic prediction in CC. The related data were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, and Gene Expression Omnibus (GEO) combined with GSE39582 set, GSE17538 set, GSE33113 set, and GSE37892 set. The differentially expressed metabolic genes were selected for univariate Cox regression and lasso Cox regression analysis using TCGA and GTEx datasets. Finally, a seventeen-gene metabolic signature was developed to divide patients into a high-risk group and a low-risk group. Patients in the high-risk group presented poorer prognosis compared to the low-risk group in both TCGA and GEO datasets. Moreover, gene set enrichment analyses demonstrated multiple significantly enriched metabolism-related pathways. To sum up, our study described a novel seventeen-gene metabolic signature for prognostic prediction of colon cancer.
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Survival differences were evaluated using the Kaplan–Meier estimate with the log-rank test.
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The datasets included in our current study are available in Genotype-tissue expression (GTEx) (https://www.gtexportal.org/home/index.html), TCGA-COAD (https://portal.gdc.cancer.gov), GSE39582 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39582), GSE17538 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17538), GSE33113 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33113), and GSE37892 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37892).
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The datasets included in our current study are available in Genotype-tissue expression (GTEx) (https://www.gtexportal.org/home/index.html), TCGA-COAD (https://portal.gdc.cancer.gov), GSE39582 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39582), GSE17538 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17538), GSE33113 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33113), and GSE37892 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37892).
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