PMID- 37434679
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 2219-6803 (Electronic)
IS  - 2218-676X (Print)
IS  - 2218-676X (Linking)
VI  - 12
IP  - 6
DP  - 2023 Jun 30
TI  - Screening of novel biomarkers for breast cancer based on WGCNA and multiple 
      machine learning algorithms.
PG  - 1466-1489
LID - 10.21037/tcr-23-3 [doi]
AB  - BACKGROUND: Breast cancer (BC) ranks first in incidence among women, with 
      approximately 2 million new cases per year. Therefore, it is essential to 
      investigate emerging targets for BC patients' diagnosis and prognosis. METHODS: 
      We analyzed gene expression data from 99 normal and 1,081 BC tissues in The 
      Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were 
      identified using "limma" R package, and relevant modules were chosen through 
      Weighted Gene Coexpression Network Analysis (WGCNA). Intersection genes were 
      obtained by matching DEGs to WGCNA module genes. Functional enrichment studies 
      were performed on these genes using Gene Ontology (GO), Disease Ontology (DO), 
      and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Biomarkers were 
      screened via Protein-Protein Interaction (PPI) networks and multiple 
      machine-learning algorithms. The Gene Expression Profiling Interactive Analysis 
      (GEPIA), The University of ALabama at Birmingham CANcer (UALCAN), and Human 
      Protein Atlas (HPA) databases were employed to examine mRNA and protein 
      expression of eight biomarkers. Kaplan-Meier mapper tool assessed their 
      prognostic capabilities. Key biomarkers were analyzed via single-cell sequencing, 
      and their relationship with immune infiltration was examined using Tumor Immune 
      Estimation Resource (TIMER) database and "xCell" R package. Lastly, drug 
      prediction was conducted based on the identified biomarkers. RESULTS: We 
      identified 1,673 DEGs and 542 important genes through differential analysis and 
      WGCNA, respectively. Intersection analysis revealed 76 genes, which play 
      significant roles in immune-related viral infection and IL-17 signaling pathways. 
      DIX domain containing 1 (DIXDC1), Dual specificity phosphatase 6 (DUSP6), 
      Pyruvate dehydrogenase kinase 4 (PDK4), C-X-C motif chemokine ligand 12 (CXCL12), 
      Interferon regulatory factor 7 (IRF7), Integrin subunit alpha 7 (ITGA7), NIMA 
      related kinase 2 (NEK2), and Nuclear receptor subfamily 3 group C member 1 
      (NR3C1) were selected as BC biomarkers using machine-learning algorithms. NEK2 
      was the most critical gene for diagnosis. Prospective drugs targeting NEK2 
      include etoposide and lukasunone. CONCLUSIONS: Our study identified DIXDC1, 
      DUSP6, PDK4, CXCL12, IRF7, ITGA7, NEK2, and NR3C1 as potential diagnostic 
      biomarkers for BC, with NEK2 having the highest potential to aid in diagnosis and 
      prognosis in clinical settings.
CI  - 2023 Translational Cancer Research. All rights reserved.
FAU - Jin, Xiaohu
AU  - Jin X
AD  - Department of Thyroid and Breast Surgery, Nantong City No. 1 People's Hospital 
      and Second Affiliated Hospital of Nantong University, Nantong, China.
FAU - Huang, Zhiqi
AU  - Huang Z
AD  - Department of Thyroid and Breast Surgery, Nantong City No. 1 People's Hospital 
      and Second Affiliated Hospital of Nantong University, Nantong, China.
FAU - Guo, Peng
AU  - Guo P
AD  - Department of Gastrointestinal Surgery, Nantong City No. 1 People's Hospital and 
      Second Affiliated Hospital of Nantong University, Nantong, China.
FAU - Yuan, Ronghua
AU  - Yuan R
AD  - Department of Thyroid and Breast Surgery, Nantong City No. 1 People's Hospital 
      and Second Affiliated Hospital of Nantong University, Nantong, China.
LA  - eng
PT  - Journal Article
DEP - 20230620
PL  - China
TA  - Transl Cancer Res
JT  - Translational cancer research
JID - 101585958
PMC - PMC10331707
OTO - NOTNLM
OT  - Machine learning
OT  - NEK2
OT  - biomarkers
OT  - breast cancer (BC)
OT  - weighted correlation network analysis
COIS- Conflicts of Interest: All authors have completed the ICMJE uniform disclosure 
      form (available at 
      https://tcr.amegroups.com/article/view/10.21037/tcr-23-3/coif). The authors have 
      no conflicts of interest to declare.
EDAT- 2023/07/12 06:42
MHDA- 2023/07/12 06:43
PMCR- 2023/06/30
CRDT- 2023/07/12 03:45
PHST- 2023/01/01 00:00 [received]
PHST- 2023/05/11 00:00 [accepted]
PHST- 2023/07/12 06:43 [medline]
PHST- 2023/07/12 06:42 [pubmed]
PHST- 2023/07/12 03:45 [entrez]
PHST- 2023/06/30 00:00 [pmc-release]
AID - tcr-12-06-1466 [pii]
AID - 10.21037/tcr-23-3 [doi]
PST - ppublish
SO  - Transl Cancer Res. 2023 Jun 30;12(6):1466-1489. doi: 10.21037/tcr-23-3. Epub 2023 
      Jun 20.