A robust six-gene prognostic signature based on two prognostic subtypes constructed by chromatin regulators is correlated with immunological features and therapeutic response in lung adenocarcinoma

Results

This study was divided into 5 research modules sequentially and the flow chart was presented in Figure 1: (1) Data collection. LUAD-related gene expression datasets and clinical data were retrieved from the public databases The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The TCGA-LUAD dataset was used to construct prognostic subtypes and prognostic gene signature. The GEO-LUAD datasets were used to evaluate the validity of prognostic subtype and the robustness of prognostic gene signature. (2) Prognostic subtype construction. Two MPSs were constructed based on 27 differentially expressed chromatin regulators (DECRs) with significant prognostic value using the TCGA-LUAD dataset and the validity of MPSs was evaluated using 4 independent GEO-LUAD datasets. (3) Key prognostic gene identification. Six differentially expressed genes (DEGs) between two MPSs were identified to be key prognostic DEGs based on the univariate Cox regression analysis (UCRA), multivariate Cox regression analysis (MCRA) and least absolute shrinkage and selection operator (LASSO) analysis. (4) Prognostic signature establishment. A six-gene prognostic signature was established using the TCGA-LUAD dataset and its robustness was assessed using 8 independent GEO-LUAD datasets. Subsequently, the predictive ability of six-gene prognostic signature was evaluated by comparing with 19 reported prognostic signatures. (5) Clinical and immunological features analyses. The associations of MPS, key prognostic genes and risk score with clinical and immunological features were investigated. Potential responses of LUAD patients to immunotherapy and chemotherapy were predicted in different MPSs and in different risk subgroups, respectively.

Figure 1. Flow chart of the present study. This study was divided into 5 research modules sequentially. (1) Data collection. Lung adenocarcinoma (LUAD) gene expression datasets were retrieved from the public gene expression omnibus (GEO) and the cancer genome atlas (TCGA) databases, respectively. (2) Prognostic subtype construction. Prognostic subtypes were constructed based on prognostic chromatin regulators. (3) Key prognostic gene identification. Key prognostic genes were identified by Cox regression and the least absolute shrinkage and selection operator (LASSO) analyses. (4) Prognostic signature establishment. Prognostic signature was established and evaluated. (5) Clinical feature and immunological feature analyses. The relationships of prognostic subtypes, prognostic genes and signature with clinical characteristics and immunological features were analyzed.

Twenty-seven DECRs were correlated with the prognosis in LUAD

To identify key prognostic CRs, a DEGA and a UCRA were performed. The DEGA result showed that totals of 2346 upregulated and 1966 downregulated genes were identified in LUAD tissue compared to normal lung tissue (Figure 2A and Supplementary Table 1). Among 127 upregulated and 33 downregulated genes were DECRs (Figure 2B and Supplementary Table 2). These DECRs, especially 127 upregulated DECRs, mainly involved in chromatin organization (GO:0051276, p = 8.37e-49), cell cycle (hsa04110, p = 2.87e-07), chromatin modifying enzymes (HAS-3247509, p = 2.67e-15), etc. (Figure 2B). The disease ontology (DO) result showed that 127 upregulated DECRs were mainly associated with cancer (DOID:162, p = 0.0334) (Figure 2B). The UCRA result showed that 27 of 160 DECRs had significant prognostic values (p < 0.05, Figure 2C). The 27 prognostic DECRs include 16 histone modifiers, 4 chromatin remodelers, 2 DNA methylators and 5 unknown types, respectively (Table 1). Except for CBX6, CBX7, CIT and MOCS1, the other 23 DECRs were risk factors (Figure 2C) and their expressions showed strong positive correlations with each other (most p < 0.001, Figure 2D). Except for CBX7, CBX6 and MOCS1, the other 24 DECRs were significantly upregulated in LUAD tissues (adjusted p < 0.05, Figure 2E).

Figure 2. Identification of key differentially expressed chromatin regulators associated with survival. (A) Distribution of differentially expressed genes (DEGs) between lung adenocarcinoma (LUAD) and normal lung tissues. Totals of 2346 upregulated and 1966 downregulated DEGs were identified in LUAD tissue. (B) Identification of differentially expressed chromatin regulators (DECRs). Totals of 127 upregulated and 33 downregulated DECRs were identified in LUAD tissue. (C) Identification of key DECRs associated with overall survival (OS) rate of patients with LUAD. Univariate Cox regression analysis showed that 27 DECRs were associated with the OS of patients with LUAD. (D) Correlations between key prognostic DECRs in expression. Except three DECRs including MOCS1, CBX7 and CIT, the expressions of the other 24 DECRs showed strong positive correlations on the whole. (E) Heat map of key prognostic DECRs in expression. Except three DECRs including MOCS1, CBX7 and CBX6, the expression of the other 24 DECRs were upregulated in LUAD tissue.

Table 1. Basic functional information of 27 prognostic chromatin regulators.

Symbol	Type	Histone type	Methytor type	Function
AURKA	Histone Modifier	Writer	/	Histone modification write (Histone phosphorylation)
BUB1	Histone Modifier	Writer	/	Histone modification write (Histone phosphorylation)
PBK	Histone Modifier	Writer	/	Histone modification write (Histone phosphorylation)
PRKDC	Histone Modifier	Writer	/	Histone modification write (Histone phosphorylation)
TTK	Histone Modifier	Writer	/	Histone modification write cofactor (Histone phosphorylation)
CDK1	Histone Modifier	Writer	/	Histone modification write (Histone phosphorylation)
CHEK1	Histone Modifier	Writer	/	Histone modification write (Histone phosphorylation)
CIT	Histone Modifier	Writer	/	Histone modification write cofactor (Histone phosphorylation)
ANP32E	Histone Modifier	Reader	/	Histone chaperone, Histone modification read
CBX3	Histone Modifier	Reader	/	Histone modification read
CBX6	Histone Modifier	Reader	/	Histone modification read
CBX7	Histone Modifier	Reader	/	Histone modification read
YWHAZ	Histone Modifier	Reader	/	Histone modification read
RAD51	Histone Modifier	Eraser	/	Histone modification erase (Histone ubiquitination)
UCHL5	Histone Modifier	Eraser	/	Histone modification erase cofactor (Histone ubiquitination)
HJURP	Histone Modifier	/	/	Histone chaperone
ATAD2	Chromatin Remodeler	/	/	Chromatin remodelling
HELLS	Chromatin Remodeler	/	/	Chromatin remodelling
NPAS2	Chromatin Remodeler	/	/	Chromatin remodelling
TOP2A	Chromatin Remodeler	/	/	Chromatin remodelling
RMI1	DNA Methylator	/	/	DNA modification
UHRF1	DNA Methylator Histone Modifier	Reader, Writer	Reader	Histone modification read, Histone modification write cofactor, DNA modification
LMNB1	/	/	/	Global heterochromatic changes
ERCC6L	/	/	/	/
HMGA1	/	/	/	/
MOCS1	/	/	/	/
PRC1	/	/	/	/

To further investigate the expression levels of 27 prognostic DECRs between LUAD and normal lung tissues, the gene expressions of these 27 DECRs were compared between LUAD and normal lung tissues from 10 LUAD patients using a paired t-test method. The results showed that the expression levels of these 27 DECRs were highly consistent with the TCGA-LUAD results (Figure 3A and Supplementary Table 3).

Figure 3. Expression levels of 27 prognostic chromatin regulators between lung adenocarcinoma (LUAD) and normal lung tissues. The expression levels of 27 prognostic chromatin regulators were compared between LUAD and normal lung tissues by a paired t-test method using 10 pairs of samples from 10 LUAD patients. The result was highly consistent with the TCGA-LUAD result.

Two MPSs constructed by 27 prognostic DECRs were valid

To investigate the relationships of 27 prognostic DECRs with LUAD prognostic subtype, a consensus clustering for the TCGA-LUAD samples was performed based on the gene expression profiles of 27 prognostic DECRs. According to clustering stabilities increasing from k = 2 to 9 (Figure 4A), k = 2 was selected for sample cluster and LUAD samples were clustered into two clusters called Cluster1 (C1 subtype) and Cluster2 (C2 subtype) (Figure 4B). The LUAD patients in the C2 subtype had a higher OS rate than those in the C1 subtype (p = 0.016, Figure 4C).

Figure 4. Molecular prognostic subtypes construction based on 27 key prognostic differentially expressed chromatin regulators (DECRs) and the associations with overall survival (OS) of patients in lung adenocarcinoma (LUAD). (A–C) TCGA-LUAD. (D–F) GSE31210. (G–I) GSE50081. (J–L) GSE68465. (M–O) GSE72094. (A, D, G, J, M) Cumulative distribution function (CDF) curve and CDF delta area curve. Clustering stability increasing curves were plotted from k = 2 to 9 for five gene expression datasets including TCGA-LUAD, GSE31210, GSE50081, GSE68465 and GSE72094. (B, E, H, K, N) Consensus clustering heat map. The k = 2 was selected to cluster samples for each of five gene expression datasets, and LUAD patients were divided into two clusters. (C, F, I, L, O) Survival curve. Two clusters were significantly associated with the OS of LUAD patients in five independent datasets (p = 0.016, < 0.0001, = 0.008, = 0.00038, = 0.00055, respectively).

To evaluate the validity of two MPSs, four independent GEO-LUAD gene expression datasets including GSE31210, GSE50081, GSE68465 and GSE72094 were clustered using the same clustering method, respectively. The k = 2 was the optimal parameter for each of four datasets (Figure 4D, 4G, 4J, 4M) and LUAD samples were clearly divided into two clusters (Figure 4E, 4H, 4K, 4N). There was a significant difference in the OS rate between two clusters (p < 0.0001, = 0.008, = 0.00038, = 0.00055, respectively, Figure 4F, 4I, 4L, 4O).

The expression analysis showed that 26 of 27 prognostic DECRs had significant differences between two clusters except for NPAS2 (Figure 5A), and 4 DECRs including CBX6, CBX7, MOCS1 and CIT were highly expressed in the C2 subtype. Among the 26 DECRs, except for CIT, the expression levels of 25 DECRs between the C1 subtype and LUAD group showed the same trends, and those between the C2 subtype and normal lung tissue, respectively (Figures 2E, 5A).

Figure 5. Correlations of molecular prognostic subtype with clinical features and KEGG pathway enrichment. (A) Expression of 27 prognostic chromatin regulators between two clusters. (B) Comparison of clinical features between two clusters. Chi-square test showed the significant differences in T staging (p = 0.0083), N staging (p = 4e-04), age (p = 0.0021) and cancer status (p = 0.0273). (C) Gene set variation analysis. Totals of 184 KEGG pathways were significantly enriched between two clusters. (D) Gene set enrichment analysis. The top 26 significantly enriched KEGG pathways were shown between two clusters.

MPSs were correlated with clinical and biological features

To explore the correlations of MPSs with clinical features, clinical features between C1 and C2 subtypes were compared. The results showed that there were significant differences in the T staging (chi-square p = 0.0083), N staging (chi-square p = 4e-04), age (chi-square p = 0.0021) and cancer status (chi-square p = 0.0273) between these two MPSs (Figure 5B). The LUAD patients in the C2 subtype had a lower T staging and N staging than those in the C1 subtype.

To investigate the correlations of MPSs with biological features, functional enrichment analyses were performed to discover potential functional differences between these two MPSs by using GSVA and GSEA methods. The GSVA result showed that a total of 184 KEGG pathways was significantly enriched (p < 0.05, Figure 5C). Among these KEGG pathways, some key oncogenic signaling pathways (OSPs) in the C2 subtype were lower active than those in the C1 subtype, such as cell cycle and P53 signaling pathway (both p < 0.001, Figure 5C). The GSEA result was consistent with that obtained by the GSVA method (Figure 5D) and some OSPs including cell cycle (p = 4.60e-09) and P53 signaling pathway (p = 2.12e-04) were significantly downregulated in the C2 subtype (Figure 5D).

To further investigate the characteristics of genomic variations between two MPSs, a somatic mutation analysis was performed. The result showed that the aneuploidy score, non-silent mutation rate, fraction altered, number of segments and homologous recombination defects in the C2 subtype were significantly lower than those in the C1 subtype (Wilcoxon test all p < 0.0001, Figure 6A). The mutation rate and main mutation type of top 10 altered genes were significantly different between two MPSs (Figure 6B).

Figure 6. Genomes alterations between two clusters. (A) Molecular features of genome alterations. There was higher aneuploidy score, nonsilent mutation rate, fraction altered of genome, number of segments and homologous recombination defects in the cluster 1. *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001. (B) Gene mutation profiles between two clusters. The mutation frequencies of top 10 genes including TP53, TTN and MUC16 and so on had significant differences in two clusters, and there was higher mutation frequency in the cluster 1.

MPSs were correlated with immunological feature and therapeutic response

To elucidate the relationships of MPSs with immunological features, a comprehensive immunological analysis was implemented. A CIBERSORT-based analysis showed that the relative abundances of 14 of 22 immune cell types had significant differences between two MPSs (Wilcoxon test p < 0.05 or < 0.01 or < 0.001, Figure 7A). Among these 14 immune cells types, the abundances of 6 immune cell types including B cells memory (p < 0.001), T cells CD4 memory resting (p < 0.001), monocytes (p < 0.001), dendritic cells resting (p < 0.001), dendritic cells activated (p < 0.01) and mast cells resting (p < 0.001) were significantly higher in the C2 subtype than those in the C1 subtype (Figure 7A). Inversely, the abundances of 8 immune cell types including T cells CD8 (p < 0.05), T cells CD4 memory activated (p < 0.001), T cells follicular helper (p < 0.05), T cells gamma delta (p < 0.05), NK cells resting (p < 0.001), macrophages M0 (p < 0.001), macrophages M1 (p < 0.001) and mast cells activated (p < 0.001) were significantly lower in the C2 subtype (Figure 7A). The stromal score (p < 0.001), immune score (p < 0.01) and ESTIMAT score (p < 0.001) in the C2 subtype were significantly higher than those in the C1 subtype (Figure 7B). Two OSPs including cell cycle (p < 0.001) and MYC (p < 0.001) in the C2 subtype were less active than those in the C1 subtype (Figure 7C), and 4 OSPs containing NRF1 (p < 0.001), TGF-beta (p < 0.001), RAS (p < 0.001) and WNT (p < 0.001) in the C2 subtype were more active than those in the C1 subtype (Figure 7C). The TIDE result showed that the TIDE score (p < 0.001), IFNG score (p < 0.01), exclusion score (p < 0.001) and MDSC score (p < 0.001) were significantly lower in the C2 subtype, and the dysfunction score (p < 0.001) and TAM.M2 score (p < 0.001) were significantly higher in the C2 subtype (Figure 7D). The estimated IC50 values for 6 conventional chemotherapy agents including erlotinib (p < 0.0001), sunitinib (p < 0.0001), paclitaxel (p < 0.001), VX-680 (p < 0.0001), TAE684 (p < 0.05) and crizotinib (p < 0.001) in the C2 subtype were significantly higher than those in the C1 subtype (Figure 7E).

Figure 7. Correlations of molecular prognostic subtypes with immunological features. (A) Infiltration comparisons of 22 immune cell types. The scores of some immune cell types had significant differences between two clusters, such as T cells CD4 memory resting, mast cells resting and dendritic cells resting with higher scores in the cluster 2. *p < 0.05, **p < 0.01 and ***p < 0.001. (B) Infiltration comparison of stromal and immune cells. The cluster 2 had higher stromal score, immune score and ESTIMATE score. *p < 0.05, **p < 0.01 and ***p < 0.001. (C) Comparisons of activities of 10 oncogenic signaling pathways. The scores of cell cycle, MYC, NRF1, TGF-beta, RAS and WNT pathways had significant differences between two clusters, and these oncogenic signaling pathways had lower activities in the cluster 2. *p < 0.05, **p < 0.01, ***p < 0.001. (D) Comparisons of tumor immune dysfunction and exclusion (TIDE) score, interferon-gamma (IFNG), T cell dysfunction (Dysfunction), T cell exclusion (Exclusion), tumor-associated macrophages M2 (TAM.M2) and myeloid-derived suppressor cells (MDSC). These terms had significant differences between two clusters, and TIDE, IFNG, Exclusion and MDSC in the cluster 1 were significantly higher than those in the cluster 2. Dysfunction and TAM. M2 in the cluster 1 were significantly lower than those in the cluster 2. *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001. (E) Comparisons of estimated IC50 values for 6 conventional chemotherapy agents. The estimated IC50 values for erlotinib, sunitinib, paclitaxel, VX-680, TAE684 and crizotinib in the cluster 1 were significantly lower than those in the cluster 2. *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001.

Six key DEGs between two MPSs were correlated with the prognosis in LUAD

To identify key DEGs associated with the survival of LUAD patients between two MPSs, a comprehensive analysis including DEGA, UCRA, LASSO and MCRA was performed. The DEGA result showed that totals of 476 upregulated and 557 downregulated genes were identified in the C1 subtype compared with the C2 subtype (Figure 8A and Supplementary Table 4). Compared with the DEGs obtained from LUAD and normal lung tissues, a total of 821 dysregulated DEGs was common (Figure 8B and Supplementary Table 5). The UCRA showed that 258 of 821 DEGs had significant prognostic values (Supplementary Table 6). The LASSO analysis showed that 10 (CENPH, RHOV, ANLN, MDFI, TPSB2, CPS1, ANGPTL4, CCL20, CENPK, GJB3) of 258 DEGs with UCRA prognostic value had the most important feature when lambda was equal to 0.0606 (Figure 8C). The MCRA by AIC criterion showed that 6 (TPSB2, CPS1, ANGPTL4, CCL20, CENPK, GJB3) of 10 DEGs with the most important feature had the greatest fitting degree. Except for TPSB2, the expressions of the other five prognostic DEGs (CPS1, ANGPTL4, CCL20, CENPK, GJB3) were significantly negatively correlated with the survival of LUAD patients (all p < 0.001, Figure 8D) and their expressions were significantly upregulated in LUAD tissues (all p < 0.001, Figure 8E) and in the C1 subtype (all p < 0.001, Figure 8F). The expression analysis based on the GEPIA (gene expression profiling interactive analysis), real transcriptome data and GSE19804 datasets showed that the expression levels of 6 prognostic DEGs were consistent with those from the TCGA dataset between LUAD and normal lung tissues (Figure 8G–8I and Supplementary Table 3). Except for CPS1, each of the other five prognostic DEGs had a low mutation rate (Figure 9A) and a lower co-occurrence and mutually exclusive (Figure 9B).

Figure 8. Identification of key differentially expressed genes (DEGs) between two clusters. (A) Distribution of DEGs between two clusters. Totals of 466 upregulated and 557 downregulated DEGs were identified in the cluster 1. (B) Identification of key DEGs between two clusters. An overlap analysis showed that totals of 821 dysregulated DEGs were important between two clusters. (C) LASSO Cox analysis. A 10-round cross validation was performed to prevent overfitting and 10 DEGs (CENPH, RHOV, ANLN, MDFI, TPSB2, CPS1, ANGPTL4, CCL20, CENPK, GJB3) had the most important feature when lambda was equal to 0.0606. (D) Univariate Cox regression analysis. Six DEGs including TPSB2, CPS1, ANGPTL4, CCL20, CENPK and GJB3 had significant prognostic values (all p < 0.001). (E) Heat map of expressions of six DEGs between lung adenocarcinoma (LUAD) and normal lung tissues. Five DEGs including CPS1, ANGPTL4, CCL20, CENPK and GJB3 were upregulated expressed and TPSB2 was downregulated expressed in LUAD tissue. (F) Heat map of expressions of six DEGs between two clusters. Five DEGs including CPS1, ANGPTL4, CCL20, CENPK and GJB3 were upregulated expressed and TPSB2 was downregulated expressed in the cluster 1. (G–I) The expression levels of 6 prognostic genes between LUAD and normal lung tissues based on GEPIA (gene expression profiling interactive analysis), real RNA-seq and GSE19804 datasets.

Figure 9. Correlation of six differentially expressed genes (DEGs) with immunity and establishment of six-gene prognostic signature. (A) Gene mutation profiles of six DEGs. Only 12.35% of LUAD samples had one or more mutations in six DEGs (CPS1, ANGPTL4, CCL20, CENPK, GJB3, TPSB2). (B) Co-occurrence and mutually exclusive of gene mutation. Six DEGs had lower co-occurrence and are mutually exclusive. (C) Correlation of six DEGs with stromal and immune cell infiltration. Two genes including CPS1 and TPSB2 were strongly negatively and positively correlated with three infiltration scores of two cells including stromal and immune cells, respectively. *p < 0.05, **p < 0.01 and ***p < 0.001. (D) Correlations of six DEGs with infiltrations of six immune cells. The expressions of six DEGs had significant correlations with one or more types of immune cells. In particular, CPS1 and TPSB2 were strongly negatively and positively correlated with immune infiltrations of six types of immune cells, respectively. *p < 0.05, **p < 0.01 and ***p < 0.001. (E) Correlations of six DEGs with 22 immune cell types. In general, the expressions of 6 DEGs (CPS1, ANGPTL4, CCL20, CENPK, GJB3, TPSB2) were strongly correlated with 22 immune cell types. *p < 0.05, **p < 0.01 and ***p < 0.001. (F) Correlations of six DEGs with 29 immune cell types. The expressions of six genes had very strong correlations with multiple immune cell types. In particular, CPS1 and TPSB2 were strongly negatively and positively correlated with immune infiltrations of 29 types of immune cells, respectively. *p < 0.05, **p < 0.01 and ***p < 0.001. (G) Correlations of six DEGs with 44 immune checkpoint genes. The expressions of six DEGs had very strong correlations with the expressions of multiple checkpoint genes. Especially, CPS1 and TPSB2 were strongly negatively and positively correlated with most checkpoint genes, respectively. *p < 0.05, **p < 0.01 and ***p < 0.001. (H) Risk score distribution and survival overview of LUAD patients. According to the median risk score, LUAD patients were divided into high- and low-risk subgroups. (I, J) Expression levels of six prognostic genes between two risk subgroups. Five genes including CENPK, ANGPTL4, CPS1, CCL20 and GJB3 were highly expressed and TPSB2 gene were lowly expressed in the high-risk subgroup. (K) Survival curve. LUAD patients in the low-risk subgroup had a higher OS rate than that in the high-risk subgroup (p < 0.0001). (L) Receiver operating characteristic (ROC) curve. The areas under the curve (AUCs) associated with 1-year, 3-year and 5-year survival were 0.72, 0.71 and 0.65, respectively.

Six key prognostic DEGs were correlated with immunological features

To investigate the relationships of six key prognostic DEGs with immunological features, a comprehensive immunological analysis was performed. Among six key prognostic DEGs, CPS1 and TPSB2 were negatively and positively correlated with the stromal score, immune score and ESTIMATE score, respectively (all p < 0.001, Figure 9C). Similarly, CPS1 and TPSB2 were negatively and positively correlated with five immune cell types including B cells, CD4 T cells, neutrophils, macrophages and dendritic cells (all p < 0.001, Figure 9D). Furthermore, a CIBERSORT-based analysis showed that there were generally stronger correlations of six key prognostic DEGs with 22 immune cell types, especially T cells CD4 memory activated, monocytes and mast cells resting (Figure 9E). An ssGSEA-based analysis showed that six key prognostic DEGs were strongly correlated with 29 immune cell types overall, especially CPS1 and TPSB2 (most p < 0.001, Figure 9F). As a whole, six key prognostic DEGs were strongly correlated with 44 immune checkpoints in expression (Figure 9G). In particular, CPS1 and TPSB2 were strongly negatively and positively correlated with these immune checkpoints in expression, respectively (most p < 0.001, Figure 9G).

Six-gene prognostic signature is robust in predicting the prognosis in LUAD

To further explore the correlation of six key prognostic DEGs with the survival, a six-gene prognostic model was constructed using the TCGA-LUAD dataset. In the light of the MCRA method by AIC criterion, the regression coefficient of each of six DEGs was calculated and the risk score was formulated:

$$\begin{array}{l}\text{Risk score}=0.235\ast \text{Exp}(CENPK)\\ +0.104\ast \text{Exp}(ANGPTL4)+0.088\ast \text{Exp}(CCL20)\\ +0.061\ast \text{Exp}(CPS1)+0.138\ast \text{Exp}(GJB3)\\ -0.134\ast \text{Exp}(TPSB2).\end{array}$$

The risk score of each patient was calculated by the risk score formula. According to the median risk score, LUAD patients were divided into high-risk and low-risk subgroups (Figure 9H). Except for TPSB2, the other five prognostic DEGs including CENPK, ANGPTL4, CPS1, CCL20 and GJB3 were significantly upregulated in the high-risk subgroup (all p < 0.05, Figure 9I, 9J). The LUAD patients in the high-risk subgroup had a poorer OS rate than those in the low-risk subgroup (p < 0.0001, Figure 9K), and the AUCs of 1-year, 3-year and 5-year correlated with the survival were separately 0.72, 0.71 and 0.65 in the ROC curve (Figure 9L). Survival analysis based on eight independent GEO-LUAD datasets showed that the OS rate of LUAD patients in the low-risk subgroup was significantly higher than that in the high-risk subgroup for each GEO-LUAD dataset (GSE3141, p = 0.0045, Figure 10A; GSE72094, p < 0.0001, Figure 10B; GSE26939, p = 0.0038, Figure 10C; GSE30219, p = 0.021, Figure 10D; GSE31210, p = 0.016, Figure 10E; GSE37745, p = 0.0099, Figure 10F; GSE50081, p < 0.0001, Figure 10G; GSE29016, p = 0.00013, Figure 10H). The AUCs of 5-year associated with the survival were 0.67 (GSE3141, Figure 10A), 0.87 (GSE72094, Figure 10B), 0.65 (GSE26939, Figure 10C), 0.7 (GSE30219, Figure 10D), 0.67 (GSE31210, Figure 10E), 0.64 (GSE37745, Figure 10F), 0.7 (GSE50081, Figure 10G) and 0.78 (GSE29016, Figure 10H) for eight independent datasets, respectively.

Figure 10. Correlations of six-gene signature with overall survival (OS) of patients in lung adenocarcinoma (LUAD). (A–H) Survival analysis based on eight independent LUAD datasets from the gene expression omnibus (GEO) database. Eight GEO-LUAD datasets were GSE3141, GSE72094, GSE26939, GSE30219, GSE31210, GSE37745, GSE50081 and GSE29016. For each dataset, LUAD patients in the low-risk subgroup had a higher OS rate than those in high-risk subgroup (p = 0.0045, < 0.0001, = 0.0038, = 0.021, = 0.016, = 0.0099, < 0.0001, = 0.00013, respectively), and the AUCs associated with 5-year survival were 0.67, 0.87, 0.65, 0.7, 0.67, 0.64, 0.7 and 0.78, respectively.

To further evaluate the robustness of six-gene prognostic signature in predicting the prognosis, we compared the prognostic significance and predictive performance of six-gene prognostic signature with 19 reported prognostic signatures [26–45] (Supplementary Figures 1–3). The results showed that the six-gene prognostic signature in this study had the highest AUC values associated with 1-year and 3-year survival, and the next highest AUC value associated with 5-year survival (Figure 11A). Six-gene prognostic signature had the highest concordance index (C-index) 0.673 (Figure 11B) and hazard ratio (HR) 2.718 of restricted mean survival (RMS) time (Figure 11C).

Figure 11. Predictive performances of 20 prognostic signatures. (A) AUC (area under the curve of receiver operating characteristic) change curve. Six-gene prognostic signature had the highest AUC values associated with 1-year and 3-year survivals and the next highest AUC value associated with 5-year survival. (B) Concordance index (C-index). Six-gene prognostic signature had the highest C-index 0.673 among 20 prognostic signatures. (C) Restricted mean survival (RMS) curve. Six-gene prognostic signature we developed had the highest hazard rate among 20 prognostic signatures.

Risk score was correlated with clinical and biological features

To investigate the relationships of risk score with clinical and biological features, these features between the high- and low-risk subgroups were compared. The results showed that there were significant differences in the MPS (chi-square p = 0), T staging (chi-square p = 1e-04), N staging (chi-square p = 1e-04), survival status (chi-square p = 0) and cancer status (chi-square p = 0.0023) between the two risk subgroups (Figure 12A). The risk scores for LUAD patients in the C2 subtype were significantly lower than those in the C1 subtype (p < 0.0001, Figure 12B). The risk scores for LUAD patients with stage N0 were significantly lower than those for patients with stage N1 and patients with stage N2 (p < 0.001 and < 0.0001, respectively, Figure 12C). Similarly, the risk scores for LUAD patients with stage T1 were significantly lower than those for patients with stage T2 and patients with stage T3 (both p < 0.0001, Figure 12F). The risk scores for LUAD patients in the living subgroup were significantly lower than those in the dead subgroup (p < 0.0001, Figure 12G). The risk scores for patients with tumor free were significantly lower than those for patients with tumor and patients with discrepancy (p < 0.0001 and < 0.05, respectively, Figure 12H).

Figure 12. Correlations of risk score with clinical characteristics and biological pathways. (A) Correlations of risk score with clinical characteristics. Risk score was significantly correlated with prognostic cluster (p = 0), T staging (p = 1e-04), N staging (p = 1e-04), survival status (p = 0) and cancer status (p = 0.0023). (B) Comparisons of risk scores for patients between two clusters. Risk score in the cluster 1 was significantly higher than that in the cluster 2. ****p < 0.0001. (C) Comparisons of risk scores for patients between four N stages. Risk scores of patients with stage N0 were significantly lower than those with stage N1 and those with stage N2. **p < 0.01 and ***p < 0.001. (D) Comparisons of risk scores for patients between three M stages. The risk scores had no significant differences between three M stages. (E) Comparison of risk scores for patients between <= 60 and > 60 age subgroups. The risk scores had no significant differences between two age subgroups. (F) Comparisons of risk scores for patients between four T stages. Risk scores of patients with stage T1 were significantly lower than those with stage T2 and those with stage T3. ****p < 0.0001. (G) Comparison of risk scores for patients between two survival statuses. The risk scores for the living LUAD patients were significantly lower than those for dead patients. ****p < 0.0001. (H) Comparisons of risk scores for patients between three cancer statuses. The risk scores for patients with tumor free were significantly lower than those with discrepancy tumor. *p < 0.05 and ***p < 0.001. (I) Gene set variation analysis. At the correlation of risk score with KEGG pathway > 0.4 or < -0.4 and p < 0.001, thirteen KEGG pathways were positively correlated with risk score. (J) Correlations of risk score with KEGG pathways. There was a strong positive correlation between risk score and 13 KEGG pathways. ***p < 0.001. (K) Top 5 KEGG pathways enriched in the high-risk group. Five KEGG pathways were separately cell cycle, DNA replication, homologous recombination, oocyte meiosis and P53 signaling pathway. (L) Top 5 KEGG pathways enriched in the low-risk group. Five KEGG pathways were separately ALPHA linolenic acid metabolism, cell adhesion molecules (CAMs), Fc epsilon RI signaling pathway, long term depression and viral myocarditis.

The GSVA result showed that 121 KEGG pathways associated with risk score were significantly enriched (p < 0.01, Supplementary Table 7). Thirteen KEGG pathways with a |cor| > 0.4 were found to be more active in the high-risk subgroups, including two key OSPs cell cycle (p = 0 and cor = 0.5975) and P53 signaling pathway (p = 0 and cor = 0.5202, Figure 12I). There were extremely strong positive correlations between the risk score and the activities of 13 KEGG pathways (all p < 0.001, Figure 12J). The GSEA result showed that 17 KEGG pathways were significantly enriched (adjusted p < 0.05, Supplementary Table 8). Among these pathways, 9 and 8 KEGG pathways in the high-risk subgroup were significantly upregulated and downregulated, respectively (Supplementary Table 8). Except for upregulated cytokine-cytokine receptor interaction pathway, the other 16 pathways were observed in the pathway list enriched by the GSVA method (Supplementary Tables 7, 8). Top 5 upregulated pathways were cell cycle (adjusted p = 1.82e-08), DNA replication (adjusted p = 0.001377), homologous recombination (adjusted p = 0.023061), oocyte meiosis (adjusted p = 0.004126) and P53 signaling pathway (adjusted p = 0.023061) (Figure 12K), and the top 5 downregulated pathways were alpha-linolenic acid metabolism (adjusted p = 0.030427), cell adhesion molecules (CAMs, adjusted p = 0.030427), Fc epsilon RI signaling pathway (adjusted p = 0.030427), long-term depression (adjusted p = 0.030855) and viral myocarditis (adjusted p = 0.022938) (Figure 12L).

Risk score is an independent prognostic factor in LUAD

To evaluate the independent predictive capability of six-gene signature in predicting the survival of LUAD patients, an independent prognostic analysis was performed. A UCRA-based analysis showed that the risk score (p = 0.000, HR = 2.631, 95% CI = 1.927-3.541), cluster (subtype) (p = 0.016, HR = 0.698, 95% CI = 0.521-0.936), T staging (p = 0.002, HR = 1.904, 95% CI = 1.262-2.872) and N staging (p = 0.001, HR = 1.638, 95% CI = 1.217-2.205) had significant prognostic values (Figure 13A). Furthermore, a MCRA-based analysis showed risk score (p = 0.000, HR = 2.57, 95% CI = 1.825-3.621), T staging (p = 0.041, HR = 1.548, 95% CI = 1.018-2.352) and N staging (p = 0.048, HR = 1.366, 95% CI = 1.033-1.862) had significant prognostic values (Figure 13B).

Figure 13. Independent prognostic analysis and correlations of risk score with immunological features. (A) Independent prognostic analysis for risk score using univariate Cox regression analysis. Risk score was significantly correlated with the overall survival of LUAD patients, and could serve as an independent prognosticator in predicting the survival of LUAD patients. (B) Independent prognostic analysis for risk score using multivariate Cox regression analysis. Risk score was significantly correlated with the overall survival of LUAD patients, and could serve as an independent prognosticator in predicting the survival of LUAD patients. (C) Construction of a nomogram predicting the survival. A nomogram for predicting 1-year, 3-year and 5-year overall survival was constructed. (D) Comparisons of 22 immune cell types between high- and low-risk subgroups. Some immune cell types had significant differences between two risk subgroups. *p < 0.05, **p < 0.01 and ***p < 0.001. (E) Comparison of immune infiltration score. LUAD patients in the low-risk subgroup had higher immune scores than those in the high-risk subgroup. *p < 0.05. (F) Comparisons of activities of 10 oncogenic signaling pathways between two risk subgroups. The activities of cell cycle, MYC, NOTCH and NRF1 pathways had significant differences, and three oncogenic signaling pathways (cell cycle, MYC, NOTCH) had higher activities in the high-risk subgroup. *p < 0.05, **p < 0.01 and ***p < 0.001. (G) Comparisons of tumor immune dysfunction and exclusion (TIDE) score, interferon-gamma (IFNG), T cell dysfunction (Dysfunction), T cell exclusion (Exclusion), tumor-associated macrophages M2 (TAM.M2) and myeloid-derived suppressor cells (MDSC). Except IFNG, those terms had significant differences between two risk subgroups, and TIDE, Exclusion and MDSC in the high-risk subgroup were significantly higher than those in the low-risk subgroup. Dysfunction and TAM. M2 in the high-risk subgroup were significantly lower than those in the low-risk subgroup. *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001. (H) Comparisons of estimated IC50 values for 6 conventional chemotherapy agents. The estimated IC50 values for 6 chemotherapy agents including erlotinib, sunitinib, paclitaxel, VX-680, TAE684 and crizotinib in the high-risk subgroup were significantly lower than those in the low-risk subgroup. *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001.

To better assess the survival of LUAD patients by incorporating various prognostic factors, a nomogram including the risk score, T staging and N staging was constructed to predict the 1-year, 3-year and 5-year OS rate of LUAD patients (Figure 13C). The calibration curve showed that the actual OS rate was basically in line with the predicted value (Figure 13C). The decision curve showed that the nomogram model had a better predictive ability in predicting the survival of LUAD patients (Figure 13C).

Risk score was correlated with immunological features in LUAD

To investigate the correlations of the risk score with immunological features, a comprehensive immunological analysis was performed. A CIBERSORT-based analysis showed that the relative abundances of 11 of 22 immune cell types had significant differences between the high- and low-risk subgroups (p < 0.05 or < 0.01 or < 0.001, Figure 13D). Among these 11 immune cell types, the abundances of 6 immune cell types including B cells memory (p < 0.001), T cells CD4 memory resting (p < 0.01), monocytes (p < 0.001), macrophages M2 (p < 0.001), dendritic cells resting (p < 0.001) and mast cells resting (p < 0.001) were significantly higher in the low-risk subgroup than those in the high-risk subgroup (Figure 13D). Inversely, the abundances of 5 immune cell types including T cells CD4 memory activated (p < 0.001), NK cells resting (p < 0.01), macrophages M0 (p < 0.001), mast cells activated (p < 0.01) and neutrophils (p < 0.05) were significantly lower in the low-risk subgroup than those in the high-risk subgroup (Figure 13D). The ESTIMATE result showed that the immune score in the low-risk subgroup was significantly higher than that in the high-risk subgroup (p < 0.05, Figure 13E) but no significant differences in the stromal score and ESTIMATE score (both p > 0.05, Figure 13E). The activities of 4 of 10 OSPs were significantly different, including cell cycle, MYC, NOTCH and NRF1 (Figure 13F). Among the four OSPs, three OSPs including cell cycle (p < 0.001), MYC (p < 0.01) and NOTCH (p < 0.05) in the high-risk subgroup were more active than those in the low-risk subgroup, and one OSP NRF1 (p < 0.01) in the low-risk subgroup was more active than that in the high-risk subgroup (Figure 13F). The TIDE result showed that the TIDE score (p < 0.001), exclusion score (p < 0.0001) and MDSC score (p < 0.0001) were significantly higher in the high-risk subgroup, and the dysfunction score (p < 0.0001) and TAM.M2 score (p < 0.0001) were significant higher in the low-risk subgroup (Figure 13G). The estimated IC50 values for 6 conventional chemotherapy agents including erlotinib, sunitinib, paclitaxel, VX-680, TAE684 and crizotinib in the high-risk subgroup were significantly lower than those in the low-risk subgroup (all p < 0.0001, Figure 13H).

Quantitative real-time PCR validated the expression of prognostic genes

To validate the expression of prognostic genes between LUAD and normal lung tissues, three prognostic genes including CIT, CPS1 and TPSB2 were selected to perform the expression analysis using quantitative real-time PCR (qPCR) method. The result showed that the expression of three genes had a consistent trend with RNA-seq result (Figures 3, 8H, 14A).

Figure 14. Expression of prognostic genes and underlying prognostic mechanism. (A) Expression of prognostic genes. CPS1 and TPSB2 genes were lowly expressed in LUAD tissues by a quantitative real-time PCR method. (B) Survival curve. The high expression of CIT gene resulted in a better overall survival rate in patients with lung adenocarcinoma. (C) Prognostic results and potential prognostic mechanism based on prognostic chromatin regulators. According to the prognostic results and existing literature, the potential prognostic mechanism was delineated.

Abbreviations

AIC: Akaike information criterion; ANGPTL4: angiopoietin like 4; AUC: area under curve; C1: cluster 1; C2: cluster 2; CCL20: C-C motif chemokine ligand 20; CENPK: centromere protein K; cfDNA: circulating free DNA; CI: confidence interval; CIBERSORT: cell-type identification by estimating relative subsets of RNA transcript; CPS1: carbamoyl-phosphate synthase 1; CR: chromatin regulator; CTC: circulating tumor cells; DECR: differentially expressed chromatin regulator; DEG: differentially expressed gene; DEGA: differentially expressed gene analysis; GEO: gene expression omnibus; GJB3: gap junction protein beta 3; GSEA: gene set enrichment analysis; GSVA: gene set variation analysis; HDI: human development index; HR: hazard rate; IC50: 50% inhibiting concentration; ICB: immune checkpoint blockade; ICG: immune checkpoint gene; KEGG: Kyoto encyclopedia of genes and genomes; KM: Kaplan-Meier; LASSO: least absolute shrinkage and selection operator; LC: lung cancer; LUAD: lung adenocarcinoma; MCRA: multivariate Cox regression analysis; NSCLC: non-small cell lung cancer; OS: overall survival; OSP: oncogenic signaling pathway; PAM: partitioning around medoid; ROC: receiver operating characteristic; ssGSEA: single sample gene set enrichment analysis; TCGA: the cancer genome atlas; TIDE: tumor immune dysfunction and exclusion; TIME: tumor immune microenvironment; TIMER: tumor immune estimation resource; TME: tumor microenvironment; TNM: tumor node metastasis; TPSB2: tryptase beta 2; UCRA: univariate Cox regression analysis.

Author Contributions

QC and JH conceived and designed the study. QC and HZ analyzed the data. JH provided experimental samples. QC wrote the paper. All authors have read and approved the final manuscript.

Acknowledgments

The authors acknowledge the GEO and TCGA databases for providing their platforms and contributors for providing their LUAD-related datasets.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Ethical Statement and Consent

The studies involving human participants were reviewed and approved by the Institutional Review Board of First People’s Hospital of Yunnan Province (No. 2017YY227) and informed consents were obtained from all LUAD patients. All datasets have been approved by the Institutional Review Board of relevant participating institutions.

Funding

This work was partially supported by the Yunnan Province Applied Basic Research Project (2016FB146) and the Fund from Health Commission of Yunnan Province (2017NS261). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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