Predicting the survival of patients with lung adenocarcinoma using a four‑gene prognosis risk model

Abstract

Lung adenocarcinoma (LAD) is difficult to diagnose as it tends to be small in size and metastasize early. The aim of the present study was to investigate prognostic factors for patients with LAD and establish a prognosis risk model. A training set consisting of clinical and RNA sequencing data from 503 patients with LAD, as well as expression data from a further 59 LAD and adjacent tissues, was obtained from The Cancer Genome Atlas. Additionally, a validation dataset was acquired from the Gene Expression Omnibus database (GSE26939), which included clinical and gene expression data from 115 patients. Using the DESeq2 package to compare expression between LAD and adjacent tissues, differentially expressed genes (DEGs) were identified. On the basis of survival and the random forests for survival, regression and classification package, genes for constructing the prognosis risk model were selected. The prognosis risk model was constructed and validated using the survival package. Subsequently, high- and low-risk groups were compared using the Limma package to identify DEGs, and enrichment analysis was performed using the web-based gene set analysis toolkit. A protein-protein interaction network was visualized using Cytoscape software. There were 18,567 DEGs between the LAD samples and the adjacent tissues, and 363 DEGs between the high- and low-risk groups. Of these, four genes were selected for constructing the prognosis risk model, myosin IE ( MYO1E ), endoplasmic reticulum oxidoreductase 1α ( ERO1L ), C1q and tumor necrosis factor-related protein 6 ( C1QTNF6 ) and family with sequence similarity 83, member A ( FAM83A ). The survival time of high- and low-risk groups in the validation set were significantly different. Functional enrichment revealed that the genes that interacted with MYO1E, ERO1L, C1QTNF6 and FAM83A separately were enriched in ‘cell cycle regulation’, ‘synthesis and assembly of nucleic acids’, ‘histone modification and cell cycle progression’ and ‘cell secretion process’. The four-gene prognosis risk model could potentially be used for predicting the survival of patients with LAD.

ERO1L promotes IL6/sIL6R signaling and regulates MUC16 expression to promote CA125 secretion and the metastasis of lung cancer cells

“…However, little research has been done on the relationship between ERO1L and tumors, and the existing research was limited to prognostic correlations 31,32 . In our study, ERO1L was closely related to the prognosis of lung adenocarcinoma which was consistent with literature reports 33 . The use of multiple markers for diagnosis can avoid the shortcomings of a single marker.…”

“…It has been reported that high expression of ERO1L in a variety of tumors including breast, gastric, esophageal, and pancreatic cancer is associated with poor prognosis [16][17][18][19][20][21] . ERO1L is included in various prognostic models of lung adenocarcinoma 22,23 . However, there are few studies on the mechanism by which ERO1L affects prognosis, and its relationship with specific secreted proteins needs further study.…”

Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer

“…Later, additional biological functions, such as insulin biogenesis and glucose homeostasis, were demonstrated [51]. In relation to lung cancer, ERO1B has been recently identified as a gene that, together with an additional three genes identified using TCGA LUAD dataset, is able to predict patient prognosis [52] and has been suggested to be a biomarker for pancreatic cancer [53,54]. DPY19L1 was firstly identified as an unclassified gene from human brain cDNA libraries in 1998 [55].…”

Involvement of MicroRNA-1-FAM83A Axis Dysfunction in the Growth and Motility of Lung Cancer Cells

“…Zhang et al studied the expression of 4 genes, MYO1E , ERO1L , C1QTNF6 , and FAM83A , to build a prognostic panel for lung cancer. Functional enrichment analysis indicated that dysfunction in these genes are involved in regulation and progression of the cell cycle, synthesis and assembly of nucleic acids, and histone modification [ 23 ]. Lee et al suggested a positive correlation of FAM83A with therapeutic resistance of tyrosine kinase inhibitors in breast cancer.…”