Identification and Validation of a Five-gene Prognostic Signature Based on Bioinformatics Analyses in Breast Cancer

Overview

Journal Heliyon

Specialty Social Sciences

Date 2023 Feb 7

PMID 36747547

Authors

Xin-Jie Du

Xian-Rong Yang

Qi-Cai Wang

Guo-Liang Lin

Peng-Fei Li

Wei-Feng Zhang

Affiliations

Soon will be listed here.

Abstract

Background: This study aimed to identify prognostic signatures to predict the prognosis of breast cancer (BRCA) patients based on a series of comprehensive analyses of gene expression data.

Methods: The RNA-sequencing expression data and corresponding BRCA patient clinical data were collected from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. Firstly, the differently expressed genes (DEGs) related to prognosis between tumor tissues and normal tissues were ascertained by performing R package "limma". Secondly, the DEGs were used to construct a polygenic risk scoring model by the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator Cox regression (Lasso-cox) analysis method. Thirdly, survival analysis was performed to investigate the risk score values in the TCGA cohort. And the enrichment analysis, immune cell infiltration levels analysis, and protein-protein internet (PPI) analysis were performed. Simultaneously, the GEO cohort was used to validate the model. Lastly, we constructed a nomogram to explore the influence of polygenic risk score and other clinical factors on the survival probability of patients with BRCA.

Results: A total of 1000 DEGs including 396 upregulated genes and 604 downregulated genes were identified from the TCGA-BRCA dataset. We obtained 5 prognosis-related genes, as the key biomarkers by Lasso-cox analysis (, , , , and ), all of which were significantly upregulated in breast tumors. The prognostic prediction of the 5 genes model was great in training and validation cohorts. Moreover, the high-risk group had a poorer prognosis. The Cox regression analysis showed that the comprehensive risk score for 5 genes was an independent prognosis factor.

Conclusion: The 5 genes risk model constructed in this study had an independent predictive ability to distinguish patients with a high risk of death from those with a low-risk score, and it can be used as a practical and reliable prognostic tool for BRCA.

Citing Articles

Exploring the molecular interface of gene expression dynamics and prostate cancer susceptibility in response to HBCD exposure.

Ni Y, Wang W, Jiang L, Shao Q Toxicol Res (Camb). 2025; 14(1):tfaf016.

PMID: 39906184 PMC: 11788417. DOI: 10.1093/toxres/tfaf016.

Construction of a prognostic model via WGCNA combined with the LASSO algorithm for stomach adenocarcinoma patients.

Huang Z, Ran W, Wang G Front Genet. 2024; 15:1418818.

PMID: 39170694 PMC: 11335515. DOI: 10.3389/fgene.2024.1418818.

IL-6 significantly correlated with the prognosis in low-grade glioma and the mediating effect of immune microenvironment.

Yang S, Chen M, Yang D, Hu S, Zheng D Medicine (Baltimore). 2024; 103(19):e38091.

PMID: 38728467 PMC: 11081577. DOI: 10.1097/MD.0000000000038091.

FBXL19 promotes malignant behaviours by activating MAPK signalling and negatively correlates with prognosis in hepatocellular carcinoma.

Xun M, Wang J, Xie Q, Peng B, Li Z, Guo Z Heliyon. 2023; 9(11):e21771.

PMID: 38027627 PMC: 10651507. DOI: 10.1016/j.heliyon.2023.e21771.

References

Liu Y, Qi J, Dou Z, Hu J, Lu L, Dai H . Systematic expression analysis of WEE family kinases reveals the importance of PKMYT1 in breast carcinogenesis. Cell Prolif. 2019; 53(2):e12741. PMC: 7046476. DOI: 10.1111/cpr.12741. View

Zhang Q, Zhao X, Zhang C, Wang W, Li F, Liu D . Overexpressed PKMYT1 promotes tumor progression and associates with poor survival in esophageal squamous cell carcinoma. Cancer Manag Res. 2019; 11:7813-7824. PMC: 6707438. DOI: 10.2147/CMAR.S214243. View

Xu Y, Peng Y, Shen M, Liu L, Lei J, Gao S . Construction and Validation of Angiogenesis-Related Prognostic Risk Signature to Facilitate Survival Prediction and Biomarker Excavation of Breast Cancer Patients. J Oncol. 2022; 2022:1525245. PMC: 9045999. DOI: 10.1155/2022/1525245. View

Xiao F, Kim Y, Snyder C, Wen H, Chen P, Luo J . Genome instability in blood cells of a BRCA1+ breast cancer family. BMC Cancer. 2014; 14:342. PMC: 4031489. DOI: 10.1186/1471-2407-14-342. View

Siegel R, Miller K, Jemal A . Cancer statistics, 2020. CA Cancer J Clin. 2020; 70(1):7-30. DOI: 10.3322/caac.21590. View

Wang D, Wei G, Ma J, Cheng S, Jia L, Song X . Identification of the prognostic value of ferroptosis-related gene signature in breast cancer patients. BMC Cancer. 2021; 21(1):645. PMC: 8165796. DOI: 10.1186/s12885-021-08341-2. View

Toledo C, Ding Y, Hoellerbauer P, Davis R, Basom R, Girard E . Genome-wide CRISPR-Cas9 Screens Reveal Loss of Redundancy between PKMYT1 and WEE1 in Glioblastoma Stem-like Cells. Cell Rep. 2015; 13(11):2425-2439. PMC: 4691575. DOI: 10.1016/j.celrep.2015.11.021. View

Zhang Z, Li J, He T, Ding J . Bioinformatics Identified 17 Immune Genes as Prognostic Biomarkers for Breast Cancer: Application Study Based on Artificial Intelligence Algorithms. Front Oncol. 2020; 10:330. PMC: 7137378. DOI: 10.3389/fonc.2020.00330. View

Wallerius M, Wallmann T, Bartish M, Ostling J, Mezheyeuski A, Tobin N . Guidance Molecule SEMA3A Restricts Tumor Growth by Differentially Regulating the Proliferation of Tumor-Associated Macrophages. Cancer Res. 2016; 76(11):3166-78. DOI: 10.1158/0008-5472.CAN-15-2596. View

10.

Jeong W, Yoon H, Lee S, Rhee S . Identification and characterization of TRP14, a thioredoxin-related protein of 14 kDa. New insights into the specificity of thioredoxin function. J Biol Chem. 2003; 279(5):3142-50. DOI: 10.1074/jbc.M307932200. View

11.

Mueller P, Coleman T, Kumagai A, Dunphy W . Myt1: a membrane-associated inhibitory kinase that phosphorylates Cdc2 on both threonine-14 and tyrosine-15. Science. 1995; 270(5233):86-90. DOI: 10.1126/science.270.5233.86. View

12.

Shao Y, Li T, Jiang M, Xu J, Huang Y, Li X . A very rare case report of glycogen storage disease type IXc with novel PHKG2 variants. BMC Pediatr. 2022; 22(1):267. PMC: 9097106. DOI: 10.1186/s12887-021-03055-7. View

13.

Im C, Li N, Moon W, Liu Q, Morton L, Leisenring W . Genome-wide Association Studies Reveal Novel Locus With Sex-/Therapy-Specific Fracture Risk Effects in Childhood Cancer Survivors. J Bone Miner Res. 2020; 36(4):685-695. PMC: 8044050. DOI: 10.1002/jbmr.4234. View

14.

Harbeck N, Penault-Llorca F, Cortes J, Gnant M, Houssami N, Poortmans P . Breast cancer. Nat Rev Dis Primers. 2019; 5(1):66. DOI: 10.1038/s41572-019-0111-2. View

15.

Liang C, Zhang X, Yang M, Dong X . Recent Progress in Ferroptosis Inducers for Cancer Therapy. Adv Mater. 2019; 31(51):e1904197. DOI: 10.1002/adma.201904197. View

16.

Luo D, Yao W, Wang Q, Yang Q, Liu X, Yang Y . The nomogram based on the 6-lncRNA model can promote the prognosis prediction of patients with breast invasive carcinoma. Sci Rep. 2021; 11(1):20863. PMC: 8531445. DOI: 10.1038/s41598-021-00364-w. View

17.

Liu L, Wu J, Wang S, Luo X, Du Y, Huang D . PKMYT1 promoted the growth and motility of hepatocellular carcinoma cells by activating beta-catenin/TCF signaling. Exp Cell Res. 2017; 358(2):209-216. DOI: 10.1016/j.yexcr.2017.06.014. View

18.

Larionova I, Kazakova E, Gerashchenko T, Kzhyshkowska J . New Angiogenic Regulators Produced by TAMs: Perspective for Targeting Tumor Angiogenesis. Cancers (Basel). 2021; 13(13). PMC: 8268686. DOI: 10.3390/cancers13133253. View

19.

Middleton G, Robbins H, Andre F, Swanton C . A state-of-the-art review of stratified medicine in cancer: towards a future precision medicine strategy in cancer. Ann Oncol. 2021; 33(2):143-157. DOI: 10.1016/j.annonc.2021.11.004. View

20.

Yu L, Xiang L, Feng J, Li B, Zhou Z, Li J . miRNA-21 and miRNA-223 expression signature as a predictor for lymph node metastasis, distant metastasis and survival in kidney renal clear cell carcinoma. J Cancer. 2018; 9(20):3651-3659. PMC: 6216006. DOI: 10.7150/jca.27117. View