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A Systematic Framework for Identifying Prognostic Genes in the Tumor Microenvironment of Colon Cancer.

作者信息

Liu Jinyang, Lan Yu, Tian Geng, Yang Jialiang

机构信息

Department of Sciences, Geneis Beijing Co., Ltd., Beijing, China.

Department of Data Mining,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.

出版信息

Front Oncol. 2022 May 19;12:899156. doi: 10.3389/fonc.2022.899156. eCollection 2022.


DOI:10.3389/fonc.2022.899156
PMID:35664768
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9161737/
Abstract

As one of the most common cancers of the digestive system, colon cancer is a predominant cause of cancer-related deaths worldwide. To investigate prognostic genes in the tumor microenvironment of colon cancer, we collected 461 colon adenocarcinoma (COAD) and 172 rectal adenocarcinoma (READ) samples from The Cancer Genome Atlas (TCGA) database, and calculated the stromal and immune scores of each sample. We demonstrated that stromal and immune scores were significantly associated with colon cancer stages. By analyzing differentially expressed genes (DEGs) between two stromal and immune score groups, we identified 952 common DEGs. The significantly enriched Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms for these DEGs were associated with T-cell activation, immune receptor activity, and cytokine-cytokine receptor interaction. Through univariate Cox regression analysis, we identified 22 prognostic genes. Furthermore, nine key prognostic genes, namely, , , 2, , , , , , and , were identified using the LASSO Cox regression analysis. The risk score of each sample was calculated using the gene expression of the nine genes. Patients with high-risk scores had a poorer prognosis than those with low-risk scores. The prognostic model established with the nine-gene signature was able to effectively predict the outcome of colon cancer patients. Our findings may help in the clinical decisions and improve the prognosis for colon cancer.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e736/9161737/a1e0c5467a79/fonc-12-899156-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e736/9161737/3908a6fa7c2f/fonc-12-899156-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e736/9161737/d60bc3cf8dfa/fonc-12-899156-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e736/9161737/5369e75c432a/fonc-12-899156-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e736/9161737/a2e21ab74079/fonc-12-899156-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e736/9161737/a1e0c5467a79/fonc-12-899156-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e736/9161737/3908a6fa7c2f/fonc-12-899156-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e736/9161737/d60bc3cf8dfa/fonc-12-899156-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e736/9161737/5369e75c432a/fonc-12-899156-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e736/9161737/a2e21ab74079/fonc-12-899156-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e736/9161737/a1e0c5467a79/fonc-12-899156-g005.jpg

相似文献

[1]
A Systematic Framework for Identifying Prognostic Genes in the Tumor Microenvironment of Colon Cancer.

Front Oncol. 2022-5-19

[2]
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Front Med (Lausanne). 2022-7-7

[3]
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[4]
The Interferon Gamma-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Colon Adenocarcinoma.

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[5]
Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma.

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[6]
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PeerJ. 2020-10-20

[7]
Identification of Hypoxia-Related Subtypes, Establishment of Prognostic Models, and Characteristics of Tumor Microenvironment Infiltration in Colon Cancer.

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[8]
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[9]
A glycolysis-related two-gene risk model that can effectively predict the prognosis of patients with rectal cancer.

Hum Genomics. 2022-2-2

[10]
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引用本文的文献

[1]
Identification of anoikis-related genes to develop a risk model and predict the prognosis and tumor microenvironment in rectal adenocarcinoma.

Front Genet. 2025-8-18

[2]
A lipid metabolism related gene signature predicts postoperative recurrence in pancreatic cancer through multicenter cohort validation.

Sci Rep. 2025-4-5

[3]
Evaluation of the Combinatory Anticancer Effect of Chemotherapeutic Compounds and Prodigiosin against HCT-116, LoVo, and A549 Cell lines.

ACS Omega. 2024-11-26

[4]
PCPE2: Expression of multifunctional extracellular glycoprotein associated with diverse cellular functions.

J Lipid Res. 2024-11

[5]
Bioinformatic Analyses and Integrated Machine Learning to Predict prognosis and therapeutic response Based on E3 Ligase-Related Genes in colon cancer.

J Cancer. 2024-8-19

[6]
An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence.

Front Oncol. 2024-2-9

[7]
Research progress on the biological basis of Traditional Chinese Medicine syndromes of gastrointestinal cancers.

Heliyon. 2023-10-20

[8]
CD72, a new immune checkpoint molecule, is a novel prognostic biomarker for kidney renal clear cell carcinoma.

Eur J Med Res. 2023-11-18

[9]
Establishment and validation of a ferroptosis-related signature predicting prognosis and immunotherapy effect in colon cancer.

Front Oncol. 2023-5-23

[10]
An epithelial-mesenchymal transition-related mRNA signature associated with the prognosis, immune infiltration and therapeutic response of colon adenocarcinoma.

Pathol Oncol Res. 2023

本文引用的文献

[1]
Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning.

Comput Struct Biotechnol J. 2021-12-23

[2]
CAR-NK Cells for Cancer Therapy: Molecular Redesign of the Innate Antineoplastic Response.

Curr Gene Ther. 2022

[3]
Therapeutic Approaches for the Management of Autoimmune Disorders via Gene Therapy: Prospects, Challenges and Opportunities.

Curr Gene Ther. 2022

[4]
gutMGene: a comprehensive database for target genes of gut microbes and microbial metabolites.

Nucleic Acids Res. 2022-1-7

[5]
Non-coding RNAs as Novel Biomarkers in Cancer Drug Resistance.

Curr Med Chem. 2022

[6]
A Five-gene Signature for Predicting the Prognosis of Colorectal Cancer.

Curr Gene Ther. 2021

[7]
Evaluating the Potential of T Cell Receptor Repertoires in Predicting the Prognosis of Resectable Non-Small Cell Lung Cancers.

Mol Ther Methods Clin Dev. 2020-5-22

[8]
Suppressing neutrophil-dependent angiogenesis abrogates resistance to anti-VEGF antibody in a genetic model of colorectal cancer.

Proc Natl Acad Sci U S A. 2020-8-19

[9]
Tumor microenvironment in gastric cancers.

Cancer Sci. 2020-7-11

[10]
The FBW7-MCL-1 axis is key in M1 and M2 macrophage-related colon cancer cell progression: validating the immunotherapeutic value of targeting PI3Kγ.

Exp Mol Med. 2020-5

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