Identification of a nomogram based on an 8-lncRNA signature as a novel diagnostic biomarker for childhood acute lymphoblastic leukemia

Results

Preprocessing of the datasets

A matrix was downloaded from the TARGET database. 532 participants in total with cALL had lncRNA probe expression; after excluding 76 peripheral blood sources and 30 patients older than 15 years, 426 bone marrow-derived patients remained. Patients with cALL in the TARGET cohorts (n=426) were separated into training (n=300) and validation (n=126) sets, and their clinical features are presented in Table 1. The cohort from GSE34861 contains 194 ALL patients. After excluding 10 patients without survival time or status, a total of 184 samples remained. Matrix data were annotated based on GENCODE (Release 29), and 19754 mRNAs and 14847 lncRNAs were obtained in the TARGET cohort. After a two-step screening of probes and deleting those with zero expression and those with a standard deviation less than 1.2, we finally obtained 1280 lncRNAs and 4985 mRNAs.

Table 1. The clinical information of patients in the training dataset, internal validation dataset and entire validation dataset.

Characteristics	Training dataset target-ALL (n=300)	Validation dataset target-ALL (n=126)	Validation dataset target-ALL (n=426)
Age (y)
<10	171	80	251
≥10	129	46	175
Gender
Male	203	81	284
Female	97	45	142
Survival status
Alive	223	98	321
Dead	77	28	105
CNS Status
CNS1	222	95	317
CNS2	62	25	87
CNS3	16	6	22
BCR ABL1 Status
positive	2	3	5
negative	298	123	421
BMA Blasts Day 8
>20%	166	71	237
≤20%	134	55	189
BMA Blasts Day 29
>5%	6	3	9
≤5%	294	123	417
WBC (x103/mcL)
<100	92	37	129
100 to 200	50	23	73
200 to 300	93	38	131
>300	65	28	93
MRD of 29 days
≥1%	37	12	49
<1%	263	114	377
DNA Index
1 to 1.16	278	115	393
>1.16	22	11	33
Abbreviations: WBC counts, WBC count at diagnosis; CNS Status, CNS status at diagnosis.

Using 8-lncRNA markers to assess prognosis

A total of 1280 lncRNAs were applied to univariate Cox analysis, and 165 lncRNAs with P < 0.01 were filtered out and applied to subsequent analysis (Figure 1A). Then, 8 LncRNA (λ = 8) were screened out by lasso regression analysis, and multivariate Cox regression analysis was carried out on the 8 lncRNA (Figure 1B, 1C). In the end, 8 lncRNAs for prediction figured out: LINC00630, HDAC2-AS2, LINC01278, AL356599.1, AC114490.1, AL132639.3, FUT8-AS1, and TTC28-AS1. The signature score of these 8 lncRNAs was counted out according a formula: signature score = (0.22636 × exp of LINC01278) - (0.26743 × exp of LINC00630) - (0.21030 × exp of HDAC2-AS2) - (0.20700 × exp of AL356599.1) + (0.32054 × exp of AC114490.1) - (0.23411 × exp of AL132639.3) - (0.29040 × exp of FUT8.AS1) - (0.29665 × exp of TTC28.AS1). The cutoff point was the median signature score, participants in the training set were separated into a low-signature score group and a high signature-score group (Figure 1D). Participants with low-signature score were obviously better OS than patients with high-signature scores (Figure 1E). Also, ROC curve analysis compared whether prediction for survival was accurate in the 8_lncRNA-based signature (Figure 1F). The AUC values were assessed for 3-year (AUC = 0.860), 5-year (AUC = 0.874), and 7-year (AUC=0.856) survival.

Figure 1. Establishment and validation of the eight-lncRNA prognostic signature. (A–C) The procedure for establishing the prognostic signature. (D) Correlation between the prognostic signature and the overall survival of patients in the TCGA cohort. The distribution of signature scores (upper), survival time (middle) and lncRNA expression levels (lower). The black dotted lines represent the median signature score cutoff dividing patients into low- and high-signature score groups. The red dots and lines represent the patients in the high-score groups. The green dots and lines represent the patients in the low-score groups. (E) ROC curve analyses based on the 8-lncRNA signature. (F) Kaplan-Meier curves of OS based on the 8-lncRNA signature.

Construction of prognostic prediction nomogram according the 8-lncRNA signature and clinical characteristic

The prognostic variables were determined by univariate Cox regression analysis, and the candidate variables with P < 0.2 in univariate analysis were contained in multivariate COX regression analysis. (Figure 2A). Next, the multivariate Cox regression model screened out the following prognostic predictors: BCR ABL1 status; white blood cell (WBC) count at diagnosis; and signature score (Figure 2B). The nomogram is established by using the regression coefficients of the statistically significant variables in the multivariate Cox regression model. (Figure 2C). Surprisingly, the C-index of the nomogram reached 0.8 (95% CI, 0.757 to 0.843). The decision curve in training cohort showed that suppose the threshold possible of a patient and a doctor was >1% and <67%, respectively, the function the mentioned nomogram to predict prognosis of cALL would be more benefit than the scheme. It also indicated that the clinical benefit rate of the model containing the signature score was significantly higher than that without it (Figure 2D).

Figure 2. Construction of a nomogram for overall survival prediction in cALL (A, B). Univariate and multivariate Cox regression analyses of clinical factors associated with overall survival. (C). The nomogram consists of BCR ABL1 status, WBC count at diagnosis and the signature score. (D) Decision curve analysis for the cALL nomogram.

Furthermore, the predictive values in the calibration curve were consistent with observed values when thinking the probabilities of 3-year, 5-year, and 7-year OS (Figure 3A). The AUC values for 3-, 5-, and 7-year survival reached 0.819, 0.860, and 0.854, respectively (Figure 3B–3D). In the end, the total risk score was counted out according to the risk proportion of each predictor in the nomogram. Kaplan-Meier analysis indicated that the OS of patients with low risk score was vitally better than that of those with a high risk score. (Figure 3E).

Figure 3. Evaluated the predictive accuracy and discriminative ability of the nomogram (A) Calibration curves of the nomogram for the estimation of survival rates at 3, 5, and 7 years. (B–D) ROC curves at 3, 5, and 7 years according to the nomogram and lncRNA signature score. (E) Kaplan-Meier curves of OS according to the total risk score.

We evaluated BCR ABL1 fusion gene status and WBC at diagnosis by Kaplan-Meier analysis. In general, the subtype of patients who were positive for the BCR ABL1 fusion gene indicated worse OS than those who were negative for the BCR ABL1 fusion gene (Supplementary Figure 1A), and patients in the high-signature group had worse OS than those in the low- signature group in BCR ABL1 fusion negative segment (Supplementary Figure 1B). While in the BCR ABL1 fusion gene positive segment, all participants were in the high-signature group (Supplementary Figure 1C). Eventually, Kaplan-Meier analysis suggested that patients with a WBC count greater than 200 x10³/mcL at the first diagnosis have significantly worse OS than those with a WBC count less than 200 x10³/mcL (log-rank test P-value <0.0001, Supplementary Figure 1D).

Validation of the 8-lncRNA signature for prognostic evaluation

To validate the robustness of the nomogram, the similar analysis process in the validation set was conducted. According to the signature score formula in the internal validation set (n =126), patients were separated into groups of those with a high-signature score (n = 63) and a low-signature score (n = 63) with the median signature score as the cutoff point (Supplementary Figure 2A). The Kaplan-Meier curves of OS shows that patients with low-signature scores have substantially better OS than patients with higher signature scores (Supplementary Figure 2B). The AUC exhibited by the 8-lncRNA signature for 3-, 5-, and 7-year survival reached 0.909, 0.851, and 0.813, respectively (Supplementary Figure 2C). Next, the nomogram model was built by using the coefficients of the multivariate Cox regression model. The C-index of the nomogram achieved 0.806 (95% CI, 0.728 to 0.884). The calibration curve indicated that predictive values were consistent with observed values considering the probabilities of 3-year, 5-year, and 7-year OS (Supplementary Figure 2D). Moreover, using the similar total risk score formula in the internal validation set, the AUC values exhibited by the total risk score for 3-, 5-, and 7-year survival reached 0.837, 0.803, and 0.824, respectively (Supplementary Figure 2E). The Kaplan-Meier curves of OS indicated that patients with low risk scores have substantially better OS than patients with higher risk scores (Supplementary Figure 2F).

In addition, the above validation process was carried out on the entire TARGET-ALL set (n=426) and revealed a good result (Supplementary Figure 3).

The 8-lncRNA signature applied to adult ALL

To explore whether the 8-lncRNA signatures are equally applicable to adult ALL, we conducted the analysis process in the GSE34861 cohort. Taking the median score as the dividing point, participants were separated into two groups: high signature group (n = 92) and low signature group (n = 92). (Figure 4A). The AUC exhibited by the signature for 5- and 7-year survival reached 0.750 and 0.792, respectively (Figure 4B). The Kaplan Meier OS curve suggested that the OS of the patients in the low risk group was obviously better than that in the high risk group. (Figure 4C). The above indicated that this signature can also measure the prognosis of adult ALL in effective.

Figure 4. Apply the 8-lncRNA signature to adult ALL. (A) Distribution of 8-lncRNA-based signature scores, lncRNA expression levels and patient survival durations in the GSE34861 set. (B) ROC curve analyses based on the 8-lncRNA signature. (C) Kaplan-Meier curves of OS based on the 8-lncRNA signature.

Weighted gene co-expression network construction (WGCNA)

First of all, we used Pearson correlation coefficient > 0.90 and P < 0.001 as cutoff points to screen the genes co-expressed with lncRNA. Then, we used the exp matrix composed of the mentioned 8 lncRNAs and 4985 genes as the input files. To create a scale-free system, the soft threshold beta to 9 was set (Figure 5A). Genes with similar patterns were set in different modules through average linked clustering (Figure 5B). We calculated the module eigengenes (ME) and clustered them according to their correlation to explore the co-expression similarity of all modules (Figure 5C). To identify the relationship between gene modules and clinical factors, genes were calculated to assess the correlation with clinical factors. Finally, we determined the strong correlation among gene significance and grade and signature score (Figure 5D). The 13 modules were generally separated into two clusters (Figure 5E).

Figure 5. WGCNA analysis. (A) Analysis of the scale-free topology model fit index for various soft-thresholding powers (β) and the mean connectivity for various soft-thresholding powers. Overall, 9 was the best fitting power value. (B) Dendrogram of the genes and different clinical factors of cALL (survival time, WBC count at diagnosis, CNS status at diagnosis, BMA Blasts day 8, BMA Blasts day 29, BCR ABL1 status, MRD on day 29, DNA index, signature score). (C) Dendrogram of the gene modules based on a dissimilarity measure. The branches of the cluster dendrogram correspond to the different gene modules. Each leaf on the cluster dendrogram corresponds to a gene. (D) Module-trait relationships. Heatmap of the correlation between module eigengenes and clinical characteristics of cALL. (E) Hierarchical clustering and heatmap of the hub gene network.

Identification of hub lncRNAs in modules and function annotation

After analysis, we found that LINC01278, AC114490.1, AL132639.3, and TTC28.AS1 were all in the turquoise module, which had a strong positive correlation with first event of relapse (cor =0.8, P <1e − 200), BCR ABL1 status (cor =0.78, P <1e − 200), signature score (cor =0.74, P < 1e − 200), and WBC count at diagnosis (cor =0.67, P=4.4e − 154) (Figure 6A–6D). Furthermore, functional enrichment analysis was carried out to analyse the GO database terms and KEGG pathway associated with genes of the turquoise module (Figure 6E–6H). Finally, in the turquoise module, we generated a diagram of the mRNA-lncRNA network (weight > 0.1) of hub lncRNAs (Supplementary Figure 4).

Figure 6. The correlation between hub lncRNA-related genes in the turquoise module and clinical characteristics. Correlation between the turquoise module and factors including first event (A), BCR ABL1 status (B), signature score (C), WBC count at diagnosis (D). GO and KEGG pathway enrichment of genes in the turquoise module. GO enrichment contains three categories: biological process (E), cellular component (F), and molecular function (G). KEGG pathway enrichment analysis revealed lymphocyte proliferation- and differentiation-related terms (H).

Additionally, LINC00630, HDAC2-AS2, and AL356599.1 were together in the brown module, which had a positive correlation with WBC count at diagnosis (cor =0.4, P=3.7e − 45), first event of relapse (cor =0.37, P =12.1e − 38), BCR ABL1 status (cor =0.35, P = 2.8e − 34), and survival time (cor =0.31, P = 7e − 27) (Supplementary Figure 5A–5D). Functional enrichment analysis was also performed in the brown module (Supplementary Figure 5E–5H). Similarly, in the brown module, we constructed a diagram of the mRNA-lncRNA network (weight > 0.1) of hub lncRNAs (Supplementary Figure 5I).

LINC01278 inhibited the apoptosis of cALL cells through hsa-miR-500b-3p in vitro

To further clarify the mechanism of how the 8 lncRNAs affect the survival of cALL patients, LINC01278 was chosen to study the mechanism using in vitro experiments, because WGCNA suggested that it was closely related to the expression of multiple oncogenes, and enrichment analysis showed that the genes significantly related to LINC01278 were enriched in lymphocyte proliferation and differentiation. Predicted by online websites such as starBase2.0 [21] (http://starbase.sysu.edu.cn/) and miRcode (http://www.mircode.org/), we found multiple binding sites between LINC01278 and miR-500b-3p, which suggests that LINC01278 may play a role by acting on miR-500b-3p. In the CCRF-CEM cell model with LINC01278 knocked down, we found that the well-known apoptosis-promoting molecules BAX, Caspase1, and Cytc were more highly expressed in the group with miR-500b-3p than the NC group according to qRT-PCR and Western blot assays (Figure 7A, 7D, 7E, P<0.05). However, the expression of Bcl-2, a classic inhibitor of apoptosis, was significantly decreased. Moreover, CCK8 experiments suggested that, when taking the normal control group into consideration, transfection of miR-500b-3p inhibited the proliferation of CCRF-CEM cells (Figure 7C, P<0.05). Additionally, after the transfection of miR-500b-3p, flow cytometry analysis indicated that the fractions of cells in early apoptosis late apoptosis and total apoptosis were significantly higher (Figure 7F, 7G, P<0.05). These results suggested that LINC01278 may inhibit apoptosis and increase the proliferation of cALL cells via miR-500b-3p.

Figure 7. LINC01278 affects apoptosis in CCRF-CEM cells. (A) The BAX, Caspase1, Cytc and Bcl-2 mRNA expression of the CCRF-CEM cell model with LINC01278 knock down. (B) The miR-500b-3p expression of the CCRF-CEM cell model with LINC01278 knock down. (C) The proliferation of the CCRF-CEM cell model with LINC01278 knock down. (D, E) The protein expression of BAX, Caspase1, Cytc and Bcl-2 in the CCRF-CEM cell model with LINC01278 knock down. NC indicates normal control; drug indicates a control group that was exposed to a drug. (F, G) The apoptosis of the CCRF-CEM cell model with LINC01278 control group with positive drugs down; *P<0.05.

Abbreviations

cALL: Childhood acute lymphoblastic leukemia; LASSO: Least absolute shrinkage and selection operator; lncRNA: Long non-coding RNA; C-index: Concordance index; TARGET: Therapeutically Applicable Research to Generate Effective Treatment; MRD: Minimum residual disease; AUC: ROC curve and the area under the curve.

Author Contributions

The project was conceived and designed by C.-Z., M.-R. and Z.-T.T. while C.-Z. wrote the manuscript. Y.-F., L.-H., F.-F., Z.-J.H., C.-Y., Z.-X.X. and L.-Y.G. performed the statistical analysis. C.-Z., M.-R., W.-Y.X. and Z.-T.T. contributed to the revision of this work. Suggestions were provided by all and the final version was consented by the all.

Acknowledgments

The authors appreciate the support from the National Cancer Institute (NCI)’s Office of Cancer Genomics (OCG).

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Funding

The study here was supported by grants from the National Natural Science Foundation of China (81502075) and the Foundation of Science and Technology of Sichuan Province (2019YJ0635). The funders did not take part in the design and implementation.

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