Co-stimulatory molecule clusters correlate with survival, immune infiltration, and tumor mutation burden in non-small cell lung cancer

Figures & data

Figure 1. Identification of co-stimulatory molecules clusters. Samples of LUAD and LUSC before (A) and after (B) eliminating batch effect. C, clustering analysis resulted in four clusters based on expression of 12 immune-related prognostic co-stimulatory molecules. D, K–M, Differences in survival of patients in four clusters.

Table 1. Clinical characteristics within these four subtypes.

		Subtypes
Characteristics	Samples (n = 996)	Subtype 1 (N = 302)	Subtype 2 (N = 178)	Subtype 3 (N = 310)	Subtype 4 (N = 206)	P value
Age(years)
≤60	264	68	36	111	49	1.153E−04
>60	717	232	141	193	151
Geneder
Male	598	182	114	192	110	1.456E−01
Female	398	120	64	118	96
Pathologic M
M0	739	227	137	224	151	1.081E−01
M1	31	8	4	16	3
Pathologic N
N0	642	204	105	189	144	5.983E−02
N1	221	60	47	72	42
N2	109	31	23	44	11
N3	7	3	1	1	2
Pathologic T
T1	282	85	38	67	92	3.691E−07
T2	555	167	109	184	95
T3	115	30	24	44	17
T4	41	20	7	12	2
Pathologic stage
Stage I	512	165	75	142	130	1.515E−04
Stage II	277	74	64	89	50
Stage III	163	51	33	59	20
Stage IV	32	8	4	17	3
Ratiotherapy
Yes	110	33	16	41	20	2.811E−01
No	766	227	130	239	170
Recurrence
Yes	252	70	45	86	51	8.038E−01
No	562	166	94	177	125

Figure 2. Functional enrichment analysis. Bar charts show significantly enriched KEGG pathways and top 20 biological processes for feature genes of clusters 1 (A), 2 (B), 3 (C), and 4 (D). Length of bars, gene count in each function terms. Color change from red to blue, P values from low to high.

Figure 3. Protein–protein interaction (PPI) network and functional enrichment. A, PPI network of feature genes in four clusters. Node size, degree of each node in network. Bar charts show 20 genes in PPI network that are significantly enriched in biological processes (B) and KEGG pathways (C). Lengths of bars, gene count in each function terms. Color change from red to blue, P values from low to high.

Figure 4. Tumor-infiltrating immune cells differ among clusters. Boxplots show significant differences in tumor-infiltrating memory B cells (A), M2 macrophages (B), regulatory T cells (C), and activated memory CD4+ T cells (D) among four clusters.

Figure 5. Expression of immune checkpoints genes among clusters. Boxplots shows significant differences in expression of CTLA4 (A), BTLA (B), LAG3 (C), PDCD1 (D), TIGIT (E), and TNFRSF4 (F) among four clusters.

Figure 6. Tumor mutation burden (TMB) and genes with high-frequency mutations among four clusters. A, Top 20 genes ranked by mutation frequency. B, Top 20 genes with significant differences in mutation frequency among four clusters, C, K–M Survival curves of patients with high and low TMB. D, significant differences in TMB among four clusters.

Data availability statement

The data that support the findings of this study are available in The Cancer Genome Atlas (TCGA) database at https://gdc-portal.nci.nih.gov/, including the standardized log (FPKM+1, 2) expression data, clinical phenotype and mutation data of lung adenocarcinoma (TCGA-LUAD) and lung squamous carcinoma (TCGA-LUSC).