Development and validation of a nomogram with an autophagy-related gene signature for predicting survival in patients with glioblastoma

Results

Identification of DE-ATGs and enrichment analysis

Following analysis of the TCGA GBM dataset using edgeR, a total of 13625 DEGs were identified in GBM and normal samples, and these genes are displayed in the volcano plot (Figure 1A). As shown by the Venn diagrams in Figure 1B, the seventy-two significant DE-ATGs (the intersection of the DEGs and ATGs) were selected for further analysis.

Figure 1. Identification of differentially expressed autophagy-related genes (DE-ATGs) in glioblastoma (GBM) and enrichment analysis. (A) Volcano plot of DEGs in tumor and normal samples of The Cancer Genome Atlas (TCGA) dataset. The vertical axis indicates the -log (adjusted P value [adj. P value]), and the horizontal axis indicates the log2 (fold change [FC]). The red dots represent upregulated genes, and the green dots represent downregulated genes (adj. P value <0.01 and |log2(FC)|>1). (B) Venn diagram showing the 72 DE-ATGs (the intersection of the DEGs and ATGs). (C) Biological processes enriched in the DE-ATGs. (D) Cellular components enriched in the DE-ATGs. (E) Molecular functions enriched in the DE-ATGs. (F) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched in the DE-ATGs.

GO analysis, including the biological process (BP), cellular component (CC) and molecular function (MF) categories, was performed on the DE-ATGs. In the BP category, the DE-ATGs were significantly enriched in the terms autophagy, mitophagy and autophagosome assembly (Figure 1C). In the CC category, the DE-ATGs were significantly enriched in the terms cytosol, extracellular exosome, mitochondrion and autophagosome membrane (Figure 1D). In the MF category, the DE-ATGs were significantly enriched in the terms ATP binding, protein serine/threonine kinase activity, and cysteine-type endopeptidase activity (Figure 1E). In addition, KEGG pathway analysis revealed that the DE-ATGs were mainly enriched in pathways in cancer, insulin signaling pathway and proteoglycans in cancer.

Identification of prognosis-related ATGs

By performing univariate Cox regression analysis on the 72 candidate genes in the TCGA cohort consisting of 155 GBM patients, we identified 9 prognosis-related genes, which were indicated to have significant prognostic value (P<0.05). Subsequent multivariate Cox regression analysis indicated that only 3 genes—Neuregulin 1 (NRG1, HR 1.142, P=0.008), Integrin Subunit Alpha 3 (ITGA3, HR 1.149, P=0.043), and Microtubule-Associated Protein 1 Light Chain 3 Alpha (MAP1LC3A, HR 1.308, P=0.014)—exhibited significant prognostic value for GBM (Supplementary Table 1). The differential expression of the above three genes in tumor and normal tissues was further validated in the Gene Expression Profiling Interactive Analysis (GEPIA) database, which included 163 GBM samples and 207 normal samples, revealing that ITGA3 was significantly upregulated but NRG1 and MAP1LC3A were significantly downregulated in GBM tissues (Figure 2A–2C) [20]. Then, we analyzed the expression of the proteins encoded by the three genes using clinical specimens from the Human Protein Profiles (https://www.proteinatlas.org) [21]. MAP1LC3A was moderately positive but ITGA3 and NRG1 were weakly positive in GBM tissue relative to their expression levels in normal tissue (Figure 2D–2F). In addition, K-M survival curves were constructed to assess the associations between the expression levels of the prognosis-related genes and OS, and the results indicated that the ITGA3, NRG1 and MAP1LC3A low-expression group (log-rank P = 0.012, 0.012, and 0.047) had a better prognosis (Figure 2G–2I).

Figure 2. Expression and survival analysis for ITGA3, MAP1LC3A and NRG1 in GBM. The expression levels of ITGA3 (A), MAP1LC3A (B) and NRG1 (C) in tumor and normal tissues were validated in the GEPIA database, which included 163 GBM samples and 207 normal samples. The red box on the left was tumor group, and the gray box on the right was normal group. The expression profiles of the proteins encoded by ITGA3 (D), MAP1LC3A (E) and NRG1 (F) in normal and tumor tissues using clinical specimens from the Human Protein Profiles. K-M OS curves based on the expression levels of ITGA3 (G), MAP1LC3A (H) and NRG1 (I) in patients with GBM in the TCGA dataset.

Construction of the ATG-based prognostic risk score model (autophagy signature)

The prognostic risk score model was established with the following formula: risk score = expression level of NRG1 × 0.132 + expression level of ITGA3 × 0.139 + expression level of MAP1LC3A × 0.269. Subsequently, we calculated the prognostic risk score for each patient in the TCGA training set. All patients were divided into the high-risk (high risk score) or the low-risk (low risk score) group using the median risk score as the cutoff (Figure 3A). In addition, K-M survival analysis demonstrated that patients with high risk scores had significantly poorer OS than patients with low risk scores (log-rank P = 6.955×10^-5). The 6-month OS rates of the high-risk and low-risk groups were 64.3% and 84.2%, respectively. The 1-year OS rates of the high-risk and low-risk groups were 39.5% and 73.4%, respectively. The 3-year OS rates of the high-risk and low-risk groups were 3.9% and 13.3%, respectively (Figure 4A). The C-index of the ATG-based prognostic model for OS prediction was 0.782 (95% CI, 0.743 to 0.821; P = 5.13×10^-11). Furthermore, the autophagy signature showed favorable predictive ability of the 0.5-, 1- and 3-year OS rates, with AUC values of 0.89, 0.84 and 0.78, respectively, in the TCGA training set (Figure 4B). In addition, when patients were stratified by different clinicopathologic parameters, the autophagy signature remained a significant prognostic factor in the TCGA training set and in the CGGA validation sets (Supplementary Figure 1).

Figure 3. Risk score analysis of the GBM autophagy signature in the TCGA training cohort (A), CGGA Batch-1 validation cohort (B), and CGGA Batch-2 validation cohort (C). Upper panel: patient survival status and time distributed by risk score. Middle panel: risk score curve of the autophagy signature. Bottom panel: heatmap of NRG1, ITGA3, and MAP1LC3A expression in GBM samples. The colors from green to red indicate the expression level from low to high. The dotted line indicates the individual inflection point of the risk score curve, by which the patients were categorized into low-risk and high-risk groups.

Figure 4. Survival analysis and prognostic performance of the autophagy-related risk score model in GBM. K-M survival curve of the risk score for patient OS in the TCGA training cohort (A), CGGA Batch-1 validation cohort (C), and CGGA Batch-2 validation cohort (E). The high-risk groups had significantly poorer OS rates than the low-risk groups. The prognostic performance of the autophagy signature demonstrated by the time-dependent ROC curve for predicting the 0.5-, 1-, and 3-year OS rates in the TCGA training cohort (B), CGGA Batch-1 validation cohort (D), and CGGA Batch-2 validation cohort (F).

Evaluation of the prognostic autophagy signature in external validation cohorts

To confirm that the prognostic autophagy signature had similar predictive values in different populations, we then used it to predict OS in two independent external validation cohorts using the median risk score as the cutoff. As shown in Figure 3B, a total of 83 patients in the CGGA Batch-1 set (validation set-1) were classified into a low-risk group (n = 42) and a high-risk group (n = 41), and the OS of the GBM patients in the high-risk group was significantly lower than that of the patients in the low-risk group (log-rank P = 8.413×10^-4; Figure 4C). The autophagy signature constructed with the TCGA training set also showed a favorable predictive ability for the 0.5-, 1- and 3-year OS rates, with AUC values of 0.78, 0.69 and 0.73, respectively, in the CGGA validation set-1 (Figure 4D). In addition, as shown in Figure 3C, a total of 133 patients in the CGGA Batch-2 set (validation set-2) were classified into a low-risk group (n = 67) and a high-risk group (n = 66), and the OS of the GBM patients in the high-risk group was significantly lower than that of patients in the low-risk group (log-rank P = 1.112×10^-2; Figure 4E). The autophagy signature generated by the TCGA training set also showed a favorable predictive ability for the 0.5-, 1- and 3-year OS rates, with AUC values of 0.76, 0.72 and 0.69, respectively, in the CGGA validation set-2 (Figure 4F).

Determination of the autophagy signature as an independent prognostic factor

Table 1 shows the demographics and clinicopathologic characteristics of GBM patients in the TCGA training cohort and CGGA validation cohorts based on the autophagy signature. Then, we performed univariate and multivariate Cox regression analyses to evaluate the prognostic significance of the autophagy signature combined with various clinicopathologic parameters (Table 2). In the TCGA training cohort, univariate analysis indicated that the autophagy signature (P = 9.54×10^-5), age (P = 9.67×10^-3), new event occurrence (P = 4.59×10^-3), pharmacotherapy (P = 1.25×10^-4), radiotherapy (P = 1.55×10^-6) and IDH status (P = 8.39×10^-3) were significantly associated with OS. Subsequent multivariate analysis indicated that the autophagy signature (P = 1.24×10^-4), age (P = 0.028), pharmacotherapy (P = 0.030), radiotherapy (P = 6.36×10^-5) and IDH status (P = 0.029) were significantly correlated with OS. Therefore, the prognostic autophagy signature constructed by the TCGA training set was an independent prognostic factor for GBM. In addition, following the univariate and multivariate analyses, the autophagy signature was also proven to be an independent prognostic factor in both the CGGA Batch-1 and Batch-2 validation cohorts (Table 2). The K-M survival curves and log-rank test for all these clinicopathological variables in the TCGA training set and CGGA validation sets are shown in Supplementary Figure 2.

Table 1. Demographics and clinicopathologic characteristics of GBM patients in the TCGA training cohort and CGGA validation cohort based on the autophagy signature.

Variables	TCGA (Training set)			CGGA Batch 1 (Validation set-1)			CGGA Batch 2 (Validation set-2)
	Total (n=155)	Low risk (n=78)	High risk (n=77)	Total (n=83)	Low risk (n=42)	High risk (n=41)	Total (n=133)	Low risk (n=67)	High risk (n=66)
Age
<= 50 years	38	24	14	40	22	18	54	28	26
> 50 years	117	54	63	43	20	23	79	39	40
Sex
Female	54	26	28	32	19	13	54	29	25
Male	101	52	49	51	23	28	79	38	41
New event
None or NA	66	30	36	NA			NA
Yes	89	48	41	NA			NA
KPS
< 80	33	19	14	NA			NA
>= 80	83	41	42	NA			NA
NA	39	18	21	NA			NA
Pharmaceutical therapy
CT only	65	34	31	23 (No)	8	15	17 (No)	11	6
CT + TMT	27	13	14	57 (Yes)	32	25	111(Yes)	54	57
CT + HT	21	14	7	3 (NA)	2	1	5 (NA)	2	3
Others	5	4	1	-			-
No or NA	37	13	24	-			-
Radiation therapy
No	23	7	16	10	6	4	17	9	7
Yes	125	66	59	70	34	36	113	56	57
NA	7	5	2	3	2	1	4	2	2
Surgery
Biopsy only	16	10	6	NA			NA
Tumor resection	139	68	71	NA			NA
IDH status
Wildtype	147	70	77	72	33	39	103	50	53
Mutant	8	8	0	11	9	2	30	17	13
1p/19q status
Non-codeletion	NA			82	41	41	103	63	40
Codeletion	NA			0	0	0	5	4	1
NA	NA			1	1	0	25	0	25
NA, not available; KPS, Karnofsky performance score; CT, chemotherapy; TMT, targeted molecular therapy; HT, hormone therapy.
“New event” included progression and recurrence. “Others” in pharmaceutical therapy included CT + TMT + HT, CT + TMT + Immunotherapy, and CT + Immunotherapy.

Table 2. Univariate and multivariate cox proportional hazards analysis of clinical parameters and risk score of GBM patients in the TCGA training cohort and CGGA validation cohorts.

Variables

TCGA (Training set)

CGGA Batch 1 (Validation set-1)

CGGA Batch 2 (Validation set-2)

Univariate Analysis

Multivariate analysis

Univariate Analysis

Multivariate analysis

Univariate Analysis

Multivariate analysis

HR (95% CI)

P

HR (95% CI)

P

HR (95% CI)

P

HR (95% CI)

P

HR (95% CI)

P

HR (95% CI)

P

Age

<= 50 years

Reference

Reference

Reference

Reference

Reference

> 50 years

1.80 (1.15–2.82)

9.67e-3

1.31 (1.03–2.64)

0.028

1.27 (1.09–2.04)

0.032

1.28 (1.07–2.09)

0.033

1.32 (0.88–1.98)

0.18

Sex

Female

Reference

Reference

Reference

Male

0.96 (0.66–1.40)

0.835

1.25 (0.76–2.05)

0.389

0.83 (0.56–1.23)

0.357

New event

None or NA

Reference

Reference

NA

NA

Yes

0.59 (0.40–0.85)

4.59e-3

0.78 (0.51–1.21)

0.272

NA

NA

KPS

< 80

Reference

NA

NA

>= 80

0.77 (0.49–1.23)

0.276

NA

NA

NA

0.89 (0.53–1.52)

0.684

NA

NA

Pharmaceutical therapy

CT only

Reference

Reference

(No) Reference

Reference

(No) Reference

Reference

CT + TMT

0.92 (0.54–1.55)

0.743

0.94 (0.55–1.62)

0.828

(Yes) 0.39 (0.23–0.67)

6.19e-4

0.41 (0.24–0.70)

1.31e-3

(Yes) 0.51 (0.28–0.92)

0.026

0.81 (0.30–0.96)

0.037

CT + HT

1.43 (0.84–2.44)

0.190

1.41 (0.82-2.43)

0.216

(NA) 0.69 (0.20–2.36)

0.558

0.85 (0.20–3.59)

0.827

(NA) 1.18 (0.33–4.20)

0.794

2.06 (0.22–8.97)

0.523

Others

1.14 (0.45-2.89)

0.784

1.37 (0.75–2.50)

0.260

-

-

No or NA

2.47 (1.56–3.91)

1.25e-4

1.73 (1.67–4.45)

0.030

-

-

Radiation therapy

No

Reference

Reference

Reference

Reference

Reference

Reference

Yes

0.31 (0.19–0.50)

1.55e-6

0.26 (0.13–0.50)

6.36e-5

0.81 (0.50–0.94)

0.049

0.78 (0.34–0.97)

0.045

0.33 (0.18–0.61)

4.72e-4

0.29 (0.10–0.82)

0.019

NA

0.60 (0.24–1.50)

0.275

0.44 (0.17–1.10)

0.079

0.58 (0.40–6.19)

0.513

0.58 (0.40–6.19)

0.513

0.69 (0.15–3.08)

0.622

0.31 (0.02–4.21)

0.376

Surgery

Biopsy only

Reference

NA

NA

Tumor resection

0.95 (0.53–1.69)

0.852

NA

NA

IDH status

Wildtype

Reference

Reference

Reference

Reference

Reference

Mutant

0.26 (0.09–0.71)

8.39e-3

0.28 (0.09–0.88)

0.029

0.64 (0.32–1.30)

0.218

0.43 (0.25–0.74)

2.36e-3

0.40 (0.23–0.68)

8.33e-4

1p/19q status

Non-codeletion

NA

NA

Reference

Codeletion

NA

NA

0.77 (0.24–2.43)

0.650

NA

NA

NA

1.11 (0.66–1.87)

0.704

Autophagy signature

Low risk

Reference

Reference

Reference

Reference

Reference

Reference

High risk

2.06 (1.43–2.96)

9.54e-5

2.12 (1.45–3.12)

1.24e-4

2.27 (1.39–3.72)

1.12e-3

2.28 (1.35–3.86)

0.002

1.66 (1.12–2.46)

0.012

1.75 (1.17–2.61)

0.006

HR, hazard ratio; CI, confidence interval; NA, not available; KPS, Karnofsky performance score; CT, chemotherapy; TMT, targeted molecular therapy; HT, hormone therapy.

“New event” included progression and recurrence. “Others” in pharmaceutical therapy included CT + TMT + HT, CT + TMT + Immunotherapy, and CT + Immunotherapy.

All statistical tests were two-sided.

GSEA

GSEA revealed that the DE-ATGs in the high ITGA3, MAP1LC3A, and NRG1 expression groups in the TCGA GBM cohort were mainly enriched in KEGG pathways related to autophagy and cancer. The significantly enriched autophagy-related pathways included regulation of autophagy, MAPK signaling pathway, endocytosis, and insulin signaling pathway. The significantly enriched cancer-related pathways included pathways in cancer, MAPK signaling pathway, mTOR signaling pathway, and glioma (Figure 5, Supplementary Table 2). In addition, GSEA was performed in the ATG-based high-risk and low-risk groups in the TCGA GBM cohort, and the DE-ATGs were also significantly enriched in the pathways related to autophagy and cancer. The DE-ATGs in the high-risk group were enriched mainly in the lysosome, cytokine-cytokine receptor interaction, and focal adhesion pathways. The DE-ATGs in the low-risk group were enriched mainly in MAPK signaling pathway, endocytosis, and insulin signaling pathway (Supplementary Figure 3, Supplementary Table 3). In summary, the defined DE-ATGs contribute to vital cancer and autophagy-related KEGG pathways, which can provide strong evidence for a cancer-targeted treatment for GBM.

Figure 5. GSEA of NRG1, ITGA3, and MAP1LC3A in the TCGA GBM cohort. Red box: regulation of autophagy and autophagy-related KEGG pathways (A–C, F–H, and K–M). Blue box: pathways in cancer and their related KEGG pathways, including glioma (B–E, G–J, and L–O).

Construction and validation of the nomogram

To establish a clinically applicable method for predicting the prognosis of GBM patients, we established a prognostic nomogram to predict the survival probability at 0.5, 1, and 3 years based on the TCGA training set. Five independent prognostic parameters, including age, autophagy signature, pharmacotherapy, radiotherapy and IDH status, were enrolled in the prediction model (Figure 6A). The C-index of the nomogram was 0.832 (95% CI, 0.793 to 0.871; P = 3.013×10^-10). The calibration plots (Figure 6B–6D) show excellent agreement between the nomogram prediction and actual observation in terms of the 0.5-, 1- and 3-year survival rates in the TCGA cohort. The nomogram also showed a favorable predictive ability for the 0.5-, 1- and 3-year OS rates, with AUC values of 0.807, 0.739 and 0.787, respectively (Supplementary Figure 4). These findings suggest the appreciable reliability of the nomogram. In addition, in the two CGGA external validation cohorts, the C-indexes of the nomogram for predicting OS were 0.737 and 0.721. The calibration plots also demonstrate excellent agreement between prediction and observation for the 0.5-, 1- and 3-year OS probabilities of the patients in CGGA Batch-1 (Figure 6E–6G) and Batch-2 (Figure 6H–6J). The AUC values demonstrated that the nomogram also has favorable predictive ability for the OS rates in the two CGGA validation cohorts (Supplementary Figure 4).

Figure 6. Nomogram to predict the 0.5-, 1-, and 3-year survival probability of patients with GBM. (A) Prognostic nomogram to predict the survival of GBM patients based on the TCGA training set. Calibration curves of the nomogram for predicting survival at 0.5, 1, and 3 years in the TCGA training cohort (B–D), CGGA Batch-1 validation cohort (E–G), and CGGA Batch-2 validation cohort (H–J). The actual survival is plotted on the y-axis; the nomogram-predicted probability is plotted on the x-axis.

Abbreviations

WHO: World Health Organization; GBM: glioblastoma; OS: overall survival; ATGs: autophagy-related genes; TERT: telomerase reverse transcriptase; TP53: tumor protein 53; IDH: isocitrate dehydrogenase; TCGA: The Cancer Genome Atlas; CGGA: Chinese Glioma Genome Atlas; DEG: differentially expressed gene; FC: fold change; DAVID: Database for Annotation: Visualization and Integrated Discovery; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; AIC: Akaike information criterion; K-M: Kaplan-Meier; C-index: Harrell's concordance index; ROC: receiver operating characteristic; AUC: area under the ROC curve; KPS: Karnofsky performance score; GSEA: gene set enrichment analysis; FDR: false discovery rate; HR: hazard radio; CI: confidence interval; BP: biological process; CC: cellular component; MF: molecular function; NRG1: Neuregulin 1; ITGA3: Integrin Subunit Alpha 3; MAP1LC3A: Microtubule-Associated Protein 1 Light Chain 3 Alpha; TMZ: temozolomide; EGF: epidermal growth factor.

Author Contributions

LG, XG, and CF performed the data curation and analysis. KD and WL analyzed and interpreted the results. ZW and BX drafted and reviewed the manuscript. All authors read and approved the final manuscript.

Acknowledgments

Dr. Wang is grateful for the invaluable support received from his parents and, in particular, Prof. Bing Xing over the years.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This study was supported by the Chinese Academy of Medical Sciences (CAMS) Initiative for Innovative Medicine (CAMS-I2M) 2017-I2M-1-001.

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