Evidence map›Paper›PMID 39390264›Full record

ArticleDiscover oncology2024

Identification of novel diagnostic biomarkers associated with liver metastasis in colon adenocarcinoma by machine learning.

Long Yang, Ye Tian, Xiaofei Cao, Jiawei Wang, Baoyang Luo

Abstract read
In one paragraph

Article in Discover oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Long Yang *Department of Gastrointestinal Surgery, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou, 225300, China.
Ye Tian *Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, China.
Xiaofei CaoTaizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, China.
Jiawei WangTaizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, China. wjw1586@163.com.
Baoyang LuoTaizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, China. lby120307@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLiver metastasis is one of the primary causes of poor prognosis in colon adenocarcinoma (COAD) patients, but there are few studies on its biomarkers.

methodsThe Cancer Genome Atlas (TCGA)-COAD, GSE41258, and GSE49355 datasets were acquired from the public database. Differentially expressed genes (DEGs) between liver metastasis and primary tumor samples in COAD were identified by limma, and functional enrichment analysis were performed. MuTect2 and maftools were used to measure somatic mutation rates, while ADTEx was used to measure copy number variations (CNVs). The intersection of three machine learning methods, support vector machine (SVM), Random Forest, and least absolute shrinkage and selection operator (LASSO), is utilized to screen biomarkers, and their diagnostic performance is subsequently validated. The correlation between biomarkers and immune cells infiltration was analyzed by Spearman method.

results47 DEGs between liver metastasis and primary tumor samples in COAD were obtained, which were mainly enriched in the complement and coagulation, extracellular matrix (ECM), and peptidase regulator activity, etc. 38 out of 47 DEGs had mutations and exhibited a high frequency of CNV amplification or deletion. Furthermore, 3 biomarkers (MMP3, MAB21L2, and COLEC11) were screened, which showed good diagnostic performance. The proportion of multiple immune cells, such as B cells naive, T cells CD4 naive, Monocytes, and Dendritic cells resting, was higher in liver metastasis samples than that in primary tumor samples. Meanwhile, MMP3, MAB21L2, and COLEC11 exhibited an outstanding correlation with immune cells infiltration.

conclusionIn short, 3 biomarkers with good diagnostic efficacy were identified, providing a new perspective of therapeutic targets for liver metastasis in COAD.

Indexed as

Colon adenocarcinomaDiagnostic biomarkersImmune cells infiltrationLiver metastasisMachine learningPrimary tumor

Identifiers

PMID39390264
PMCPMC11467158

What Socratic holds

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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 Socratic graph.