Construction and evaluation of a nomogram for predicting survival in patients with lung cancer

Materials and Methods

Dataset preparation and samples information collection

As shown in Figure 1, four sets of subjects were enrolled for preliminary and further verification of screened prognostic biomarkers. In this study, a total of 2325 lung cancer patients who had clinical and follow-up annotations were included in a training set and three validation sets. Of these, 651 patients in the training set came from GSE30219, GSE37745 and GSE50081 (Affymetrix HG-U133 Plus 2.0 Array). These microarray datasets were downloaded from the gene expression omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) and normalized using a robust multichip average (RMA) algorithm. After batch effects were removed using the combined association test (COMBAT) empirical Bayes method in the surrogate variable analysis (SVA) package, these datasets containing 651 qualified lung cancer patients that were further integrated into a new cohort as the training set. Moreover, similar datasets were processed on the same platform using the identical normalization method and log2 transformation. Validation set I contained a total of 259 lung cancer patients from GSE29013 and GSE31210, and validation set II included 441 lung cancer patients from GSE41271 and GSE42127. The fragments per kilobase per million (FPKM)-normalized RNA-seq data of 494 LUAD and 480 lung squamous cell carcinoma (LUSC) patients retrieved from The Cancer Genome Atlas (TCGA) were integrated as validation set III. We excluded patients with an overall survival (OS) of less than 30 days or with a vague or absent vital status.

Figure 1. A schematic flowchart for analyzing prognosis-related risk score in lung cancer.

In addition, we also established the specificity validation sets of four other cancers, including liver hepatocellular carcinoma (TCGA-LIHC), colon adenocarcinoma (TCGA-COAD), stomach adenocarcinoma (TCGA-STAD), and breast invasive carcinoma (TCGA-BRCA). Supplementary Table 1 shows the dataset information within each cohort.

Identification of survival-related gene and functional enrichment analysis

The average OS period of the patients in the training set was approximately 3 years, which is a critical point in time. DEGs were identified between alive and deceased subjects within 3 years using the linear models for microarray (LIMMA) package, with difference multiples >1.2 and p-value < 0.05 and were selected for further analysis. Furthermore, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using the R package clusterpro filer at the level of p-value < 0.05 and false discovery rate <0.05. Additionally, the gene set enrichment analysis (GSEA) algorithm in R package gene set variation analysis (GSVA) was used to evaluate the biomarker performance in the training sets retrieved from the Molecular Signature Database (MSigDB) [13].

Candidate selection and signature establishment

The least absolute shrinkage selection operator (LASSO) algorithm was used to identify the 306 DEG candidate genes with the best survival prediction features in the training sets. Subsequently, we performed multivariate Cox regression analysis based on the results of LASSO analysis. Cox proportional risk regression models were used to assess the importance of each candidate for OS. PRS were calculated as follows: PRS = exp_gene1 × β_gene1 + exp_gene2 × β_gene2 + ⋯ + exp_geneN × β_geneN by weighting normalized gene expression values according to their Cox coefficients.

Study subjects in each dataset were divided into high- and low-risk groups according to the cut-off points of median risk scores. Kaplan-Meier (K-M) survival curves and time-dependent receiver operating characteristic (survival-ROC) were conducted to evaluate the prognostic value of the risk score model. The higher the calculated C-index, the more precise the prediction.

Validation of the prognostic signature

In the training set, a stratification analysis was performed to determine whether the prognostic signature could accurately predict patient survival in different clinical factor subgroups. The model's performance was further evaluated using the independent validation set. In addition, the specificity of the model was tested in four other vital cancers. Gene expression data from different sets were adjusted individually by subtracting the median expression value after log2 transformation.

The PRS was combined with clinically informative variables to create a multivariate cox regression model (complex model) and a nomogram to visualize the predicted outcome for each patient. Additionally, the Hosmer-Lemeshow test was used to validate calibration curves that were established to improve the accuracy of nomogram prediction [14]. To accomplish this, we calculated the total score derived from the established nomogram for each patient in the validation set and generated a calibration curve with Cox regression [15].

Statistical analysis

IBM SPSS Statistics 26 (IBM Corp., Armonk, NY, USA), GraphPad Prism 7.0 (GraphPad Software Inc, San Diego, CA, USA), the EmpowerStats software (http://www.empowerstats.com, X&Y solutions, Inc. Boston MA, USA) and R software (version 4.1.0, http://www.r-project.org) were used to analyze data and plot graphs. LASSO logistic regression analysis was conducted using the glmnet package in R. Nomogram plots were established by the root mean squares (RMS) package. The pROC and survival-ROC packages were applied to analyze ROC and time-dependent ROC (tROC) curves. Independent sample t-tests or Mann-Whitney U-tests were used to compare continuous variables, and chi-square tests were used to compare categorical variables. Statistical significance was defined as a p-value < 0.05.

Results

Identification of the DEGs and the hub markers associated with prognosis in the training set

A total of 651 lung cancer samples with the OS time over 30 days were analyzed. The LIMMA software was used to identify the DEGs between samples from alive or deceased patients with 3-year survival in lung cancer. Out of 14,052 genes, 306 met the threshold set (adjusted p-value < 0.05, |log2FC| >0.26), with 146 DEGs being down-expression and 160 being up-expression in the deceased group (Figure 2A, 2B).

Figure 2. Differential expression and LASSO-Cox regression results of DEGs. (A, B) Heatmap plot and volcano plot represents the expression of 306 DEGs between samples from alive and deceased patients based on 3-year survival data, satisfying the criteria of adjusted p-value < 0.05, |log2FoldChange| >0.26. (C, D) 26 genes considered the more correlated with prognosis were identified by LASSO regression method. (E) Coefficients of eight genes estimated by multivariate Cox regression. (F) Expression profiles of eight hub genes between samples from alive and deceased patients with 3-year survival data.

To determine the most robust prognostic indicators, LASSO-Cox regression models were used. To overcome over-fitting, tenfold cross-validation was used on the 306 DEGs associated with 3-year survival. The more robust prognostic candidates were investigated using the LASSO regression method with an optimal value of 0.1618 (Figure 2C). The results showed that all 26 prognosis-related candidates had non-zero LASSO coefficients (Figure 2D). Subsequently, multiple stepwise Cox regression was used to determine the impact of the candidate genes, and eight hub markers were chosen to construct the risk model in lung cancer patients (Table 1). The expression profiles of these eight genes showed that elevated expression of secreted phosphoprotein 1 (SPP1), sodium-dependent phosphate transporter 1 (SLC20A1), and centromere protein H (CENPH) in lung cancer samples were risk factors for prognosis. In contrast, high-expression of MAGE family member E1 (MAGEE1), chromosome 16 open reading frame 54 (C16ORF54), potassium voltage-gated channel subfamily S member 3 (KCNS3), tripartite motif containing 68 (TRIM68) and cytochrome B5 domain containing 2 (CYB5D2) were protective factors for prognosis (Figure 2E). In addition, the expression of the eight genes was significantly different between samples from alive or deceased patients with 3-year survival in lung cancer (Figure 2F). The K-M survival curve analysis revealed that the expression of these eight genes is significantly associated with lung cancer prognosis (Supplementary Figure 1).

Table 1. The eight hub markers identified distinguish alive from deceased patients based on 3-year survival data.

Gene symbol	Protein name (UniProt accession)	Log2FC (dead/alive)	Known functions	Relation to cancer
SPP1	secreted phosphoprotein 1	0.43	Functions to control the survival, growth, differentiation and effector function of tissues and cells	Implicated in tumorigenesis in various cancer types [16, 17] Over expressed in lung neoplasms [18, 19]
SLC20A1	Sodium-dependent phosphate transporter 1	0.27	High-affinity inorganic phosphate:sodium symporter activity	High expression of SLC20A1 mRNA inhibits the progress of lung cancer [20]
KCNS3	Potassium voltage-gated channel subfamily S member 3	−0.47	Involved in energy metabolism	The expression is related to breast cancer and lung cancer, promoted metastasis [21, 22]
TRIM68	Tripartite motif containing 68	−0.31	Enhances the transcriptional activity of the AR [23]	Preferentially expressed in prostate cancer cells [23]
CYB5D2	Cytochrome b5 domain containing 2	−0.29	Interacting selectively and non-covalently with heme	Downregulation of CYB5D2 is associated with breast cancer progression [24]
CENPH	Centromere protein H	0.39	Negative regulation of cysteine-type endopeptidase activity	Higher expression levels of CENPH tended to have worse OS in lung cancer [25]
MAGEE1	MAGE family member E1	−0.26	Participating in specific biological processes	The expression of MAGEE1 is correlated with tumor-cell proliferation of NSCLC [26]
C16ORF54	Chromosome 16 open reading frame 54	−0.37	Protein amino acid binding and integral component of membrane	Tobacco Smoke Pollution results in decreased expression of C16ORF54 mRNA in lung cancer [27]

Construction and validation of the PRS in lung cancer

The predictive model was constructed using the eight hub markers identified using the multiple Cox regression method. The risk score of each patient was calculated based on the cox coefficients: PRS = 0.1223 × expression level of SPP1 + 0.1862 × expression level of SLC20A1 – 0.0909 × expression level of KCNS3 –0.1693 × expression level of TRIM68 – 0.2490 × expression level of CYB5D2 + 0.1954 × expression level of CENPH – 0.2869 × expression level of MAGEE1 – 0.1069 × expression level of C16ORF54.

The cut-off value was determined automatically based on the median risk score, and lung cancer patients were divided into the low- (n = 390) and high-risk (n = 259) groups using the cut-off value of −2.39. As illustrated in Figure 3A, the distribution of the PRS, OS time, and heatmap for the eight-gene signature in the training set is shown from top to bottom. Furthermore, the tROC analysis revealed that PRS was the most accurate predictor of OS (Supplementary Figure 2). The AUC of this PRS model in 1-year, 3-year, 5-year was 0.72, 0.75, and 0.71, respectively (Figure 3B). Moreover, the K-M survival analysis revealed that the patients had worse OS in the high-risk group than in the low-risk group (HR = 2.72; 95% confidence interval (CI), 2.26 to 3.27, p < 0.0001), and the C-index of the PRS for predicting survival was 0.67; 95% CI, 0.65 to 0.70 (Figure 3C).

Figure 3. Characteristics of PRS signature in the training cohort. (A) Risk scores distribution, survival status, and gene expression patterns of patients in high- and low-risk groups in the training cohort. (B) Time-dependent ROC analysis for predicting OS. (C) Survival curves and C-index for high- and low-risk groups.

Validation of PRS signature in the subgroup and independent lung cancer validation sets

To confirm the prognostic robustness of PRS features and complex models across cohorts, we further validated it in the three independent external cohorts described earlier. Similarly, in each of the three validation sets, patients in the high-risk group had poorer outcomes, while those in the low-risk group had a higher survival rate (Figure 4A–4C). K-M analysis confirmed that the predicted high-risk group had a significantly shorter time to death (Figure 4D–4F), indicating good predictive performance of the PRS, with a HR of 2.38; 95% CI, 1.61 to 3.53 in validation set I, 1.35; 95% CI, 1.06 to 1.71 in validation set II, and 2.71; 95% CI, 1.77 to 4.18 in validation set III.

Figure 4. Evaluating PRS signatures in validation sets. (A–C) Risk score distribution and survival status of patients in high- and low-risk groups in validation sets. (D–F) Survival curves in validation sets.

Subsequently, a stratified analysis was performed to assess whether PRS characteristics could predict the probability of patient survival in the same subgroup of clinical factors. Patients in the training cohort were classified clinically by p-stage (I/II/III-IV) (Figure 5A), histological type (glandular/squamous) (Figure 5B), gender (female/male) (Figure 5C, 5D) and age (<65/≥65) (Figure 5E, 5F). The results showed that PRS characteristics could divide patients with the same age, sex, p-stage, and histological type into high-risk and low-risk groups. In each tier, OS was shorter in patients with high-risk scores than in patients with low-risk scores (p < 0.001) (Figure 5). As a result of the above analysis, the HR does not change significantly across subgroups, and the PRS is an independent risk factor of lung cancer prognosis. It has predictive value in different people. In addition, to further prove the specificity of PRS as a prognostic factor of lung cancer in the clinic, we tested it in four other primary global cancers. The results showed that PRS signature was not associated with the prognosis of liver cancer, bowel cancer, gastric cancer, or breast cancer (Supplementary Figure 3), indicating that the PRS signature is only related to the prognosis of lung cancer.

Figure 5. PRS as a valuable predictor for OS in subgroups. PRS discriminated high-risk patients with different clinicopathological characteristics, including (A) p-stage, (B) histological type, (C, D) gender, and (E, F) age.

Prognostic nomogram for OS

A total of 614, 259, 438, and 740 patients with full-scale five clinical annotations including age, sex (male or female), histology (LUAD, LUSC or other), p-stage (I, II, III or IV) and PRS (low or high) were extracted from the training, validation I, II, and III sets, respectively (Table 2). In the training set, multivariate Cox regression analysis revealed that these five variables were correlated with the prognosis of lung cancer, with PRS being the most significant predictor of overall survival in the Cox model (complex model) (Figure 6A). These variables were used to construct a decision tree to improve risk stratification for overall survival. As shown in Figure 6B, only p-stage and PRS remained in the decision tree, with three different risk subgroups identified. To quantify the risk assessment and survival probability for individual patients, a nomogram incorporating PRS and other clinicopathological features was constructed (Figure 6C). Furthermore, we calculated the value of each covariate of patient No. 350 (GSM1213824) and mapped it to the corresponding score, calculated the total score, and its probability at 3-year and 5-year survival. The calculated values were 0.757 and 0.881. For 3- or 5-year survival, the probability calibration plot revealed the best agreement between nomogram prediction and actual observation (45-degree dotted line) (Figure 6D), indicating that the nomogram is highly accurate. When compared to other features, the nomogram exhibited the most powerful and stable ability for survival prediction, with an average AUC greater than 0.7, significantly better than the pathological p-stage (Figure 6E).

Table 2. Clinical characteristics and PRS model of lung cancer patients in the training and validation sets.

Exposure	Training set	Validation set I	Validation set II	Validation set III
	N = 614	N = 259	N = 438	N = 740
Age	64.29 ± 10.55	60.4 ± 7.9	65.0 ± 9.9	66.1 ± 9.5
Gender	–	–	–	–
Male	404 (65.8%)	133 (51.4%)	230 (52.5%)	453 (61.2%)
Female	210 (34.2%)	126 (48.6%)	208 (47.5%)	287 (38.8%)
Histology	–	–	–	–
LUAD	308 (50.2%)	234 (90.3%)	309 (70.5%)	476 (64.3%)
LUSC	164 (26.7%)	25 (9.7%)	120 (27.4%)	264 (35.7%)
Other	142 (23.1%)	0	9 (2.1%)	0
P-stage	–	–	–	–
I	402 (65.5%)	186 (71.8%)	237 (54.1%)	383 (51.8%)
II	125 (20.4%)	56 (21.6%)	81 (18.5%)	192 (26.0%)
III	77 (12.5%)	17 (6.6%)	114 (26.0%)	135 (18.2%)
IV	10 (1.6%)	0	6 (1.4%)	30 (4.0%)
PRS	–	–	–	–
Low-risk	369 (60.0%)	129 (49.8%)	220 (49.8%)	487 (50.0%)
High-risk	245 (40.0%)	130 (50.2%)	221 (50.2%)	487 (50.0%)

Figure 6. Combination of the PRS signature and clinical features improves survival prediction in training sets. (A) A decision tree was constructed to improve risk stratification. (B) Multivariate Cox regression model (complex model). (C) Survival nomogram for quantifying risk assessment for individual patients. (D) Calibration analysis revealed a high degree of accuracy in predicting survival at 3 or 5 years. (E) Among all clinical variables, tROC analysis demonstrated that the nomogram was the most stable and powerful predictor of OS.

At the same time, the complex model combining PRS, and clinical risk factors also had a good predictive performance of 3-year survival, namely 0.788, 0.709 and 0.614, respectively in the three validation sets (Supplementary Figure 4).

Functional analysis of the survival-related DEGs

To further understand the underlying mechanism of the survival-related DEGs, we analyzed 306 DEGs between samples from alive or deceased patients with 3-year survival in the training set. Enrichment analyses involved the KEGG, GO functional enrichment and GSEA of hallmark in MSigDB. The genes were divided into 3 categories: biological process, cellular component, and molecular function according to the GO terms (Figure 7A–7C). The most abundant groups were nuclear division, chromosomal region, and cofactor binding, respectively, in the three categories. We discovered that pathways involving the cell cycle, cellular senescence, oocyte meiosis, and the p53 signaling pathway were enriched in KEGG (Figure 7D). Additionally, the GSEA results based on the hallmark gene sets in MSigDB indicated that these DEGs were primarily associated with not only HALLMARK G2M CHECKPOINT (normalized enrichment score (NES) = −1.30, p < 0.001), but also HALLMARK E2F TARGETS (NES = 2.024, p < 0.001) (Figure 7E–7G). The top pathway and hallmark:cell cycle and E2F TARGETS clarified the division of activity in lung cancer cells. We summarized a working model of the activated pathways in Figure 8.

Figure 7. Enrichment analyses of DEGs. (A) Biological process. (B) Cellular component. (C) Molecular function. (D) KEGG pathway analysis. (E–G) GSEA analysis using hallmark gene sets from MSigDB.

Figure 8. Working model of major enrichment pathways in lung cancer. Proteins activated by CDC7, DBF4, CYCE, MCM, and CDC6 promote DNA replication, in addition to promoting cell amplification, particularly transcription factor E2F, which is regulated by numerous genes. In addition, upregulation of the transcription activity of E2F promotes Skp2 regulation of the PI3K/AKT pathway, thereby potentially promoting the occurrence of lung cancer.

Abbreviations

LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; PGSs: progression gene signatures; AUC: area under the curve; SCLC: squamous cell lung cancer; GEO: gene expression omnibus; OS: overall survival; PRS: prognosis-related risk score; TCGA: The Cancer Genome Atlas; DEGs: Differentially expressed genes; GSEA: Gene Set Enrichment Analysis; K-M: Kaplan-Meier; CI: confidence interval; NSCLC: non-small cell lung carcinoma; tROC: time-dependent ROC.

Author Contributions

JOY, SXH, and XW conceived and designed the present study. TL and SQY drafted the initial manuscript. ZJH, JLT and YY analyzed the data. All authors have read and approved the final manuscript. JW and XC confirm the authenticity of all the raw data.

Conflicts of Interest

The authors declare no conflicts of interest related to this study.

Funding

This work was supported by the China Postdoctoral Science Foundation (2019M652334), the Natural Science Foundation of Jiangxi (2020BAB206067), the National Natural Science Foundation of China (NO.81360447) and the Natural Science Foundation of Jiangxi (20192ACB20019).

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