Screening of key genes related to ferroptosis and a molecular interaction network analysis in colorectal cancer using machine learning and bioinformatics

Abstract

Background: This study sought to identify the key genes and molecular interactions related to ferroptosis in colorectal cancer (CRC) using machine-learning and bioinformatics analyses.

Methods: The Gene Expression Omnibus (National Institutes of Health, US) datasets for CRC were downloaded from the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/). The 291 ferroptosis genes were downloaded and screened from the FerrDb (http://www.zhounan.org/ferrdb) and GeneCards (https://www.genecards.org/) databases. The least absolute shrinkage and selection operator regression model and support vector machine model were constructed to identify the different ferroptosis-related hub genes. The immune infiltrates were identified and a survival curve analysis was conducted.

Results: We identified 11 ferroptosis-related differentially expressed genes (DEGs) from the COADREAD (Colon and Rectal Cancer) dataset. We found that angiopoietin-related protein 7 (ANGPTL7) gene expression was positively correlated to both the neuroglobin (NGB) (r=0.678) and ceruloplasmin (CP) (r=0.454) genes but was negatively correlated with transferrin receptor 2 (TFR2) expression (r=-0.426). In addition, TFR2 gene expression was positively correlated with the arachidonate lipoxygenase 3 (ALOXE3) (r=0.452) and carbonic anhydrase 9 (CA9) (r=0.411) genes. A total of 4 hub genes were identified by the machine-learning analysis [i.e., NADPH oxidase 4 (NOX4), TFR2, ALOXE3, and CA9]. The expression of the NOX4 gene was significantly positively correlated with neutrophil (r=0.543) and M0 macrophage (r=0.422) infiltration. In addition, a positive correlation between ALOXE3 and activated natural-killer cells (r=0.356) was found. Conversely, the NOX4, TFR2, and CA9 genes were negatively correlated with the resting mast cells. A strong negative correlation was observed between NOX4 and CD160 antigen (CD160) expression; however, a significant positive correlation was observed between NOX4 and transforming growth factor beta receptor 1 (TGFBR1) expression (r=0.397). The patients had a more favorable prognosis when the NOX4 expression levels were relatively low.

Conclusions: Our study identified 4 ferroptosis-related DEGs in CRC (i.e., NOX4, TFR2, ALOXE3, and CA9), and further validated their relationship with immune cell infiltration and the associated immune checkpoints. Our findings confirm the influence of the immune microenvironment on CRC. Low NOX4 levels were more favorable to patient outcomes. Our findings may facilitate future clinical diagnoses and outcome assessments of CRC.

Keywords: Ferroptosis-related genes; colorectal cancer (CRC); machine learning and bioinformatics; molecular interaction network.

Figure 1

The ferroptosis-related DEGs were found among the three datasets. (A) Volcano map and (B) heap map of the ferroptosis-related DEGs in the COADREAD dataset. ANGPTL7, angiopoietin-related protein 7; NGB, neuroglobin; CP, ceruloplasmin; NOX4, NADPH oxidase 4; CA9, carbonic anhydrase 9; TFR2, transferrin receptor 2; ALOXE3, arachidonate lipoxygenase 3; DEGs, differentially expressed genes; FC, fold change; Notsig, not significant; NA, not analyzed.

Figure 2

The relationships were identified among the ferroptosis-related DEGs. Correlation heat map (A) and correlation network (B) of the 11 ferroptosis-related DEGs in the COADREAD dataset. The colors in the graph range from red to blue, indicating correlation coefficients from –1 to 1. CP, ceruloplasmin; TFR2, transferrin receptor 2; ALOXE3, arachidonate lipoxygenase 3; CA9, carbonic anhydrase 9; NOX4, NADPH oxidase 4; ANGPTL7, angiopoietin-related protein 7; NGB, neuroglobin; Corr, correlation; DEGs, differentially expressed genes.

Figure 3

Correlation scatter plots of (A) ANGPTL7 vs. NGB genes; (B) ANGPTL7 vs. CP genes; (C) TFR2 vs. ALOXE3 genes; (D) NGB vs. CP genes; (E) TFR2 vs. CA9 genes; (F) ANGPTL7 vs. TFR2 genes. ANGPTL7, angiopoietin-related protein 7; NGB, neuroglobin; CP, ceruloplasmin; TFR2, transferrin receptor 2; ALOXE3, arachidonate lipoxygenase 3; CA9, carbonic anhydrase 9.

Figure 4

The machine learning was used to obtain the hub genes. (A) LASSO regression model; (B) SVM model showing that the optimum number of features was 4; (C) Venn diagram of the hub genes. CV, coefficient of variation; LASSO, least absolute shrinkage and selection operator; SVM, support vector machine.

Figure 5

Verification of the hub genes. (A) MLP neural network for verifying the hub genes; (B) ROC curve of the hub genes in GSE32323 dataset; (C) ROC curve of the hub genes in the GSE113513 dataset. NOX4, NADPH oxidase 4; TFR2, transferrin receptor 2; ALOXE3, arachidonate lipoxygenase 3; CA9, carbonic anhydrase 9; AUC, area under the curve; MLP, multi-layer perceptron; ROC, receiver operating characteristic.

Figure 6

Genetic interaction networks of the miRNAs, hub genes, and lncRNAs. lncRNAs, long non-coding RNAs

Figure 7

Comparison of the immunocyte infiltration profiles between the CRC and normal samples. NK, natural killer; CRC, colorectal cancer.

Figure 8

Heat map showing the correlations between the expression of the 4 hub genes and the immune-related gene set. *, P<0.05; **, P<0.01; ***, P<0.001. NK, natural killer; NOX4, NADPH oxidase 4; TFR2, transferrin receptor 2; ALOXE3, arachidonate lipoxygenase 3; CA9, carbonic anhydrase 9.

Figure 9

Heat map of the immune checkpoint analysis. *, P<0.05; **, P<0.01. TFR2, transferrin receptor 2; NOX4, NADPH oxidase 4; CA9, carbonic anhydrase 9; ALOXE3, arachidonate lipoxygenase 3.

Figure 10

Survival analysis of CRC patients. (A) Overall survival of NOX4; (B) disease-specific survival of NOX4. NOX4, NADPH oxidase 4; CRC, colorectal cancer.