Systematic construction and validation of an epithelial–mesenchymal transition risk model to predict prognosis of lung adenocarcinoma

Results

Development of an ERG signature using a training set

To examine the correlation of gene expression profiles with EMT scores, the expression data for ERGs and corresponding survival time were subjected to univariate Cox regression analysis. Eventually, 139 and 293 ERGs were found to be significantly correlated with the prognosis of patients based on The Cancer Genome Atlas (TCGA) and GSE31210 datasets, respectively (P < 0.05; Supplementary Tables 1, 2). Thereafter, 54 prognostic ERGs that overlapped between the two datasets were used to develop a prognostic gene signature (Figure 1A). Using LASSO-penalized Cox regression analysis of the training set, we identified 19 genes (Figure 1B, 1C). Subsequently, multivariate Cox regression analysis was used to establish the ERG signature, and nine genes were finally selected as predictors of overall survival (OS) in patients with LUAD (Figure 1D).

Figure 1. Construction of the ERG signature. (A) Identification of ERGs correlated with survival. (B) LASSO coefficients of the ERGs. Each curve represents an ERG. (C) Cross-validation of gene selection using 1-SE criteria in the LASSO regression analysis. (D) Forest plot of multivariate Cox regression analysis.

ERG expression and alterations in LUAD

The mRNA expression levels of the nine identified ERGs that were selected as the LUAD signature were examined using the training set. It was found that in the LUAD tissue samples, the adrenomedullin (ADM), cadherin 2 (CDH2), cathepsin L (CTSL), fascin-1 (FSCN1), fucosyltransferase 4 (FUT4), integrin beta-1 (ITGB1), and leucine-rich repeat-containing G-protein-coupled receptor 4 (LGR4) gene expression levels were significantly higher and the C-C chemokine receptor 2 (CCR2) and hepatocyte growth factor (HGF) gene expression levels were significantly lower than those in the normal lung tissues (Figure 2A). To further validate the data, the corresponding protein expression levels were analyzed using the Human Protein Atlas (HPA) database. It was found that the protein levels matched the mRNA expression levels of these ERGs (Figure 2B).

Figure 2. ERG expression and mutation landscape of LUAD. (A) Comparison of mRNA expression levels between LUAD and normal tissues in the training set. (B) Comparison of protein levels between LUAD and normal tissues in the HPA. (C) Mutational profiles of LUAD (obtained from the cBioPortal for Cancer Genomics).

The presence of potential genetic alterations in our signature was explored in patients with LUAD using the cBioPortal database, including four LUAD datasets. Among these datasets, the rates of gene alterations, including missense and truncating mutations, amplifications, and deep deletions, ranged from 1.1% to 7%, with amplifications being the most commonly observed alterations (Figure 2C).

Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA)

Further, we investigated and compared the potential functional mechanisms between high- and low-risk groups. GSEA was conducted on the training set, and a number of enriched terms were observed in the high-risk group, most of which were cancer-related biological functions and signaling pathways. The top five Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, which include the chromosomal region, chromosome segregation, condensed chromosome, cell cycle, DNA replication, and homologous recombination, are shown in Figure 3A, 3B. The GSVA results demonstrated that the EMT-related signaling pathways, such as E2F targets, G2M checkpoint, MYC targets v1, MYC targets v2, and Mtorc1 signaling, were significantly activated in the high-risk group (Figure 3C).

Figure 3. GSEA and GSVA data. (A) Top five representative GO annotation terms in the high-risk group. (B) Top five representative KEGG pathways in the high-risk group. (C) Comparison of the low- and high-risk groups using GSVA.

Prognostic value of the ERG signature in the training set

Based on the median risk score, the included 490 patients with LUAD from TCGA were equally divided into low-risk (n = 245) and high-risk (n = 245) groups. Figure 4A shows the coefficient of the formula and the risk scores [risk scores = (0.011794409 × ADM mRNA level) + (−0.142467012 × CCR2 mRNA level) + (0.024230703 × CDH2 mRNA level) + (0.001349971 × CTSL mRNA level) + (0.006464507 × FSCN1 mRNA level) + (0.113002543 × FUT4 mRNA level) + (−0.120528327 × HGF mRNA level) + (0.003726199 × ITGB1 mRNA level) + (0.012745463 × LGR4 mRNA level)]. Figure 4B–4D shows a heatmap of the expression profiles of the nine ERGs, the risk score distribution, and the vital statuses of the patients with LUAD from the high- and low-risk groups. Kaplan–Meier survival curves exhibited a significantly worse OS in the high-risk than in the low-risk group (P = 2.35e−8; Figure 4E). Next, receiver operating characteristic (ROC) curve analysis was carried out to assess the discrimination capacity of the nine-gene signature. The areas under the curves (AUCs) for 1-, 3-, and 5-year OS predictions were 0.763, 0.704, and 0.712, respectively (Figure 4F), which indicated a good performance of this nine-ERG signature for prognostic prediction. Univariate and multivariate Cox regression analyses suggested that the ERG signature could be regarded as an independent predictor of OS after adjustment for demographic and clinical features, including age, sex, tumor grade, and TNM stage (Figure 4G, 4H).

Figure 4. Predictive value of the ERG signature for LUAD in the training set (TCGA). (A) Value of each coefficient representing its relative contribution to the predictive signature. (B) Heatmap of the mRNA expression levels of the nine signature-comprising ERGs. (C) Rank of risk signature and score distribution. (D) Distribution of patients in the low- and high-risk groups based on their survival status (OS). (E) OS times of the patients between high- and low-risk groups. (F) Time-dependent ROC curve analysis for the prediction of 1-, 3-, and 5-year OS using the ERG signature. (G) Univariate Cox regression analysis of the ERG signature and clinical features of the patients. (H) Multivariate Cox regression analysis of the ERG signature and clinical features of the patients.

Validation of the ERG signature

To validate the reliability of the ERG signature, the risk score was calculated for each patient in the validation set (GSE31210) using the same formula that was used for the patients from TCGA. The samples were then separated into high- and low-risk groups based on the median value of the risk scores. In the validation set, the distribution of the risk scores, survival statuses of the patients, and a heatmap of expression profiles of the nine ERGs (Figure 5A–5D) showed similar trends to those in the training set. Further survival analysis indicated that the high-risk patients had a significantly worse OS and relapse-free survival (RFS) than did the low-risk patients (Figure 5E, 5F). Time-dependent ROC curves were generated to explore the prognostic values of the nine ERG-based risk scores. It was found that the ERG signature could accurately predict the prognosis of the patients (Figure 5G, 5H). Taken together, the ERG signature was capable of predicting the survival of patients with LUAD.

Figure 5. Verification of the predictive value of the ERG signature for LUAD in the validation set (GSE31210). (A) Risk signature rank and score distribution. (B) Heatmap of the mRNA expression levels of the nine genes included in the signature. (C) Distribution of patients in the high- and low-risk groups based on their survival status (OS). (D) Distribution of patients in the high- and low-risk groups based on their survival status (RFS). (E) OS of the patients in the low- and high-risk groups. (F) RFS of the patients in the low- and high-risk groups. (G) Time-dependent ROC curve analysis for the prediction of 1-, 3-, and 5-year OS using the ERG signature. (H) Time-dependent ROC curve analysis for the prediction of 1-, 3-, and 5-year RFS using the ERG signature.

Correlation between the ERG signature and clinical and demographic features of patients

Clinical and demographic features, including age, sex, pathological TNM stage, and pathological tumor stage, were analyzed in the training set, and the relationships between the screened genes and clinical indexes were explored. The results suggested a differential expression of CTSL, FSCN1, HGF, ITGB1, and FUT4 in patients with various clinical features (Figure 6A–6I).

Figure 6. Correlation of the mRNA expression levels of the prognostic ERG signature with demographic and clinicopathological characteristics of patients with LUAD. (A) CTSL and TNM stage. (B) FSCN1 and TNM stage. (C) HGF and T stage. (D) ITGB1 and T stage. (E) CTSL and N stage. (F) FSCN1 and N stage. (G) FSCN1 and age. (H) FUT4 and sex. (I) ITGB1 and sex.

Subgroup analysis of the ERG signature

The prognostic value of the ERG signature was further explored in subgroups of patients with LUAD with various clinical and demographic features. Stratification analysis was carried out according to the clinical and demographic features, including age, sex, T stage, N stage, M stage, and pathological tumor stage (Figure 7A–7K). It was found that the ERG signature was useful in most of the subgroups (Tables 1, 2).

Figure 7. Confirmation of the ERG signature via stratification of patients from the training set based on specific demographic and clinical features. (A) Age < 65 years; (B) age ≥ 65 years; (C) male; (D) female; (E) stage I; (F) stage II; (G) stage III; (H) T2; (I) T3; (J) M0; and (K) N0.

Table 1. Association between the ERG signature and OS of patients with LUAD in the training set (TCGA, n = 490), stratified by demographic and clinical characteristics.

Characteristics	Number (high-/low-risk group)	Proportion of patients	HR (95% CI)	P-value
Age (years)
≥ 65	124/147	55.3%	0.554 (0.445–0.688)	0.000
< 65	121/98	44.7%	0.509 (0.306–0.849)	0.010
Sex
Female	116/150	54.3%	0.450 (0.290–0.698)	0.000
Male	129/95	45.7%	0.592 (0.457–0.765)	0.000
Stage
I	107/154	53.3%	0.654 (0.499–0.858)	0.002
II	70/47	23.9%	0.731 (0.537–0.995)	0.046
III	51/28	16.1%	0.673 (0.470–0.962)	0.030
IV	16/9	5.1%	0.611 (0.322–1.159)	0.132
NA	1/7	1.6%	−	−
T stage
T1	63/103	21.6%	0.744 (0.545–1.016)	0.063
T2	142/116	52.7%	0.667 (0.534–0.833)	0.000
T3	29/16	9.2%	0.233 (0.084–0.643)	0.005
T4	10/8	3.7%	0.500 (0.224–1.114)	0.090
NA	½	0.6%	−	−
M stage
M0	158/164	65.7%	0.644 (0.527–0.787)	0.000
M1	16/8	4.9%	0.518 (0.243–1.103)	0.088
NA	71/73	29.4%	−	−
N stage
N0	141/176	64.7%	0.589 (0.463–0.749)	0.000
N1	57/35	18.8%	0.811 (0.600–1.096)	0.173
N2	45/23	13.9%	0.777 (0.531–1.138)	0.195
N3	0/2	0.4%	−	−
NA	2/9	2.2%	−	−
NA, not available; HR, hazard ratio; CI, confidence interval.

Table 2. Association between the ERG signature and survival of patients with LUAD in the validation set (GSE31210, n=226), stratified by demographic and clinical characteristics.

Characteristics	Number (high-/low-risk group)	Proportion of patients	OS		RFS
			HR (95% CI)	P-value	HR (95% CI)	P-value
Age (years)
≥ 65	87/77	72.6%	0.643 (0.371−1.112)	0.114	0.666 (0.429−1.005)	0.053
< 65	26/36	27.4%	0.103 (0.024−0.442)	0.002	0.504 (0.348−0.729)	0.000
Sex
Female	56/65	53.5%	0.300 (0.096−0.937)	0.038	0.348 (0.165−0.737)	0.006
Male	57/48	46.5%	0.437 (0.236−0.810)	0.009	0.559 (0.374−0.834)	0.004
Smoking status
Ever smoker	61/50	49.1%	0.434 (0.235−0.802)	0.008	0.506 (0.334−0.767)	0.001
Never smoker	52/63	50.9%	0.556 (0.313−0.988)	0.046	0.647 (0.446−0.940)	0.022
Stage
I	75/93	74.3%	0.415 (0.222−0.775)	0.006	0.518 (0.359−0.741)	0.000
II	38/20	25.7%	0.663 (0.380−1.158)	0.148	0.812 (0.537−1.228)	0.323
Mutation
ALK fusion	9/2	4.9%	0.189 (0.000−377.477)	0.668	0.187 (0.000−313.329)	0.658
EGFR mutation	37/31	30.1%	0.496 (0.264−0.932)	0.029	0.638 (0.420−0.968)	0.035
KRAS mutation	52/75	56.2%	0.523 (0.292−0.934)	0.029	0.558 (0.381−0.815)	0.003
Wild-type EGFR/KRAS/ALK	15/5	8.8%	0.175 (0.001−41.803)	0.532	0.172 (0.004−7.474)	0.360
HR, hazard ratio; CI, confidence interval.

Comparison of immune cell types between the low- and high-risk groups

Using the CIBERSORT algorithm, we assessed the proportions of 22 immune cell types between the high- and low-risk patients with LUAD. Figure 8A shows the results for the 490 patients with LUAD from the training set. Regarding specific differences, the high-risk group had significantly higher proportions of naïve B cells, resting natural killer (NK) cells, M0 macrophages, activated mast cells, and neutrophils but significantly lower proportions of plasma cells, resting memory CD4 T cells, monocytes, resting dendritic cells, and resting mast cells than those in the low-risk group (Figure 8B).

Figure 8. Differences in the immune landscape between low- and high-risk patients with LUAD. (A) Relative proportions of immune cell infiltration in the high- and low-risk patients. (B) Vioplot visualization of significantly different proportions of immune cells between low- and high-risk patients.

Construction and validation of a nomogram

As this novel ERG signature showed good predictive value for the LUAD prognosis, a more convenient and sensitive nomogram model, which included the ERG signature and pathological stage, age, and sex was developed based on the training set (Figure 9A). The AUC values for the 1-, 3-, and 5-year OS predictions using the nomogram were 0.774, 0.757, and 0.762, respectively (Figure 9B). In the validation set, the AUC values for 1-, 3-, and 5-year OS using the nomogram were 0.917, 0.752, and 0.745, respectively (Figure 9C), and those for 1-, 3-, and 5-year RFS were 0.914, 0.783, and 0.733, respectively (Figure 9D). To further test the nomogram, we performed survival analysis and ROC analysis by using another independent dataset, GSE41271 (test set). In the test group, survival analysis indicated that the high-risk patients had a significantly worse OS than the low-risk patients (Figure 9E). The AUC values for 1-, 3-, and 5-year OS using the nomogram were 0.709, 0.702, and 0.728, respectively (Figure 9F).

Figure 9. Construction and validation of a nomogram based on the ERG signature. (A) Nomogram based on the ERG signature and clinical information of patients with LUAD. (B) ROC curves of the nomogram for the prediction of OS in the training set. (C) ROC curves of the nomogram for the prediction of OS in the validation set (GSE31210). (D) ROC curves of the nomogram for the prediction of RFS in the validation set. (E) OS of the patients in the low- and high-risk scores based on the nomogram in the test set (GSE41271). (F) ROC curves of the nomogram for the prediction of OS in the test set.

Calibration plots based on the training set showed that the nomogram could accurately predict 1-, 3-, and 5-year OS (Figure 10A). In addition, decision curve analysis (DCA) was performed for the nomogram and TNM stage and indicated marked clinical usefulness of this model (Figure 10B).

Figure 10. Evaluation of the nomogram in the training set. (A) Calibration plot of the nomogram for the prediction of OS. (B) DCA of the nomogram for the prediction of OS.

Abbreviations

ADM: adrenomedullin; ALK: anaplastic lymphoma kinase; AUC: area under the curve; CDH2: cadherin 2; CI: confidence interval; CTSL: cathepsin L; DCA: decision curve analysis; EGFR: epidermal growth factor receptor; EMT: epithelial–mesenchymal transition; ERG: epithelial–mesenchymal transition-related gene; FSCN1: fascin-1; FUT4: fucosyltransferase 4; GEO: Gene Expression Omnibus; GO: Gene Ontology; GSEA: gene set enrichment analysis; GSVA: gene set variation analysis; HPA: Human Protein Atlas; HR: hazard ratio; ITGB1: integrin beta-1; KEGG: Kyoto Encyclopedia of Genes and Genomes; KRAS: Kirsten rat sarcoma viral oncogene homolog; LGR4: leucine-rich repeat-containing G-protein-coupled receptor 4; LUAD: lung adenocarcinoma; NK: natural killer; NSCLC: non-small-cell lung cancer; OS: overall survival; RFS: relapse-free survival; ROC: receiver operating characteristic; TCGA: The Cancer Genome Atlas.

Author Contributions

YT collected and analyzed the data. ZZ designed the study. JW and CQ performed statistical analysis of the data. YT and YJ prepared the manuscript, and ZZ secured funding for the project. All authors have read and approved the final manuscript.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Funding

This work was supported by the National Natural Science Foundation of China (Nos. 81760351 and 82060361).

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