Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin β family-related genes

Abstract

Background: Integrin β (ITGB) superfamily plays an essential role in the intercellular connection and signal transmission. It was exhibited that overexpressing of ITGB family members promotes the malignant progression of lung adenocarcinoma (LUAD), but the relationship between ITGB superfamily and the LUAD prognosis remains unclear.

Methods: In this study, the samples were assigned to different subgroups utilizing non-negative matrix factorization clustering according to the expression of ITGB family members in LUAD. Kaplan-Meier (K-M) survival analysis revealed the significant differences in the prognosis between different ITGB subgroups. Subsequently, we screened differentially expressed genes among different subgroups and conducted univariate Cox analysis, random forest feature selection, and multivariate Cox analysis. 9-feature genes (FAM83A, AKAP12, PKP2, CYP17A1, GJB3, TMPRSS11F, KRT81, MARCH4, and STC1) in the ITGB superfamily were selected to establish a prognostic assessment model for LAUD.

Results: In accordance with the median risk score, LUAD samples were divided into high- and low-risk groups. The receiver operating characteristic (ROC) curve of LUAD patients' survival was predicted via K-M survival curve and principal component analysis dimensionality reduction. This model was found to have a favorable performance in LUAD prognostic assessment. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of differentially expressed genes between groups and Gene Set Enrichment Analysis (GSEA) of intergroup samples confirmed that the high- and low-risk groups had evident differences mainly in the function of extracellular matrix (ECM) interaction. Risk score and univariate and multivariate Cox regression analyses of clinical factors showed that the prognostic model could be applied as an independent prognostic factor for LUAD. Then, we draw the nomogram of 1-, 3-, and 5-year survival of LUAD patients predicted with the risk score and clinical factors. Calibration curve and clinical decision curve proved the favorable predictive ability of nomogram.

Conclusion: We constructed a LUAD prognostic risk model based on the ITGB superfamily, which can provide guidance for clinicians on their prognostic judgment.

Keywords: ITGB; NMF; lung adenocarcinoma; nomogram; prognostic model.

FIGURE 1

LUAD is divided into ITGB‐related subgroups based on the NMF model. (A) Area curve of NMF cophenetic at different k values; (B) 493 LUAD patients were divided into 2 ITGB‐related subgroups; (C) PCA dimensionality reduction analysis among ITGB‐related subgroups; (D) K‐M survival analysis among ITGB‐related subgroups (*p < 0.05)

FIGURE 2

Differentially expressed genes and their involved functional pathways among ITGB‐related subgroups. (A) Volcano plot of differentially expressed genes in different ITGB subgroups (red: upregulated genes, green: downregulated genes); Bubble diagram of (B) GO enrichment analysis and (C) KEGG enrichment analysis of differentially expressed genes in ITGB subgroups

FIGURE 3

Construction of ITGB‐related prognostic model. (A) The relationship between error rate and the number of feature genes in random forest feature selection; (B) The importance sequencing of the screened 9 prognostic genes; (C) Forest map of multivariate Cox regression of 9‐feature genes (*p < 0.05, **p < 0.01)

FIGURE 4

Survival of samples and expression of feature genes in high‐ and low‐risk groups. Risk score distribution chart of LUAD patients in the (A) training set and (D) validation set, with red displaying samples in the high‐risk group and green displaying samples in the low‐risk group; survival curves of LUAD patients in the (B) training set and (E) validation set obtained by risk score, with red representing the dead samples and green representing the living samples; Heatmap of 9‐feature gene expression in two risk groups in the (C) training set and (F) validation set

FIGURE 5

Assessment of predictive capacity of 9‐feature gene‐based prognostic model. PCA dimensionality reduction analysis of samples in the high‐ and low‐risk groups in the (A) training set and (B) validation set, with red indicating the high‐risk group and cyan indicating the low‐risk group; K‐M survival curves of patients in two risk groups in the (C) training set and (D) validation set, with red representing the high‐risk group and blue representing the low‐risk group; ROC curves of 9‐feature gene‐based prognostic model in the (E) training set and (F) validation set

FIGURE 6

Differentially activated signaling pathways between high‐ and low‐risk groups. (A) Bar chart of p‐value distribution of biological processes and functional enrichment of differentially expressed genes in two risk groups in the training set. The horizontal axis represents the number of enriched genes; (B) ID cluster diagram and (C) p‐value cluster diagram of functional enrichment items of differentially expressed genes in the high‐ and low‐risk groups in the training set; Enrichment of two risk groups in (D) FOCAL_ADHESION gene set, (E) ECM_RECEPTOR_INTERACTION gene set and (F) CELL_CYCLE gene set

FIGURE 7

Construction of ITGB‐related nomogram and assessment of predictive ability. Forest map of (A) univariate Cox analysis and (B) multivariate Cox analysis on the risk score of 9‐feature gene model and clinical factors; (C) The constructed nomogram of risk score of 9‐feature gene and clinical factors; (D–F) Calibration curves of 1‐ (D), 3‐ (E), and 5‐year (F) survival of LUAD patients predicted by nomogram; (G–I) Decision curves of 1‐ (G), 3‐ (H), and 5‐year (I) survival of LUAD patients predicted by nomogram