Molecular characteristics of m6A regulators in SCLC
After reviewing the latest relevant literature [23,24,25,26], we finally identified 30 m6A regulators, including 11 methyltransferases (METTL3, METTL14, METTL16, METTL5, WTAP, VIRMA, RBM15, RBM15B, ZC3H13, CBLL1, and ZCCHC4), 2 demethylases (ALKBH5 and FTO), and 17 binding proteins (YTHDF1, YTHDF2, YTHDF3, YTHDC1, YTHDC2, HNRNPA2B1, HNRNPC, FMR1, EIF3A, IGF2BP1, IGF2BP2, IGF2BP3, ELAVL1, G3BP1, G3BP2, PRRC2A, and RBMX) (Fig. 1A; Additional file 1: Table S2). Firstly, we explored the incidence of somatic mutations for m6A regulators in LS-SCLC. Mutations were present in 20 of 88 samples (22.73%; Fig. 1B). FMR1 displayed the highest mutation frequency, while approximately 14 m6A regulators exhibited no mutations within the LS-SCLC samples, including demethylases ALKBH5 and FTO (Fig. 1B). We identified co-occurrent mutations between METT3 and YTHDC2 and between HNRNPC and IGF2BP3 (Additional file 2: Fig. S1).
To determine if this genetic alteration affected the expression pattern of m6A regulators in LS-SCLC samples, we explored the mRNA expression of regulators between LS-SCLC and normal lung specimens. Principal component analysis revealed markedly different expression patterns of m6A regulators in LS-SCLC and normal samples (Fig. 1C). We also generated a heatmap for different expression profiles of these m6A regulators in LS-SCLC and normal tissues (Fig. 1D). The regulators’ expression details—between LS-SCLC and normal groups—are shown in Fig. 1E. Notably, almost all methyltransferases and binding proteins were upregulated in LS-SCLC; however, the two demethylases tended to exhibit lower expression in LS-SCLC than their normal counterparts. These results suggested that there may be abundant m6A modifications in LS-SCLC and significant heterogeneity in the m6A regulator genetic profile expression between LS-SCLC and normal lung tissues. Disordered m6A regulator expression may be involved in tumorigenesis and SCLC development.
Association of various m6A regulators in SCLC
Various m6A regulators cooperatively promote tumour development. Therefore, we also tried to explore the expression relationships for the 30 m6A regulators in SCLC. Notably, we detected remarkable correlations among the expressions of methyltransferases, demethylase, and binding proteins (Additional file 2: Fig. S2). Several significant positive correlations were also identified, including a correlation coefficient between KIAA1429 and YTHDF3 as high as 0.820 (Fig. 2A). Our protein-protein interaction network analysis determined that these 30 m6A regulators were in frequent communication (Fig. 2B). Importantly, the methyltransferases exhibited the most frequent interactions. Various methyltransferases formed a protein complex to perform biological functions. Therefore, the crosstalk among multiple m6A regulators may be actively involved in the SCLC development and progression.
Clinical significance of m6A regulators in SCLC
Next, we investigated the clinical significance of m6A regulators in patients with LS-SCLC based on the optimal cut-off point derived from the international cohort. Most regulators were significantly associated with survival (Fig. 2C). Some regulators exhibited pro-carcinogenic properties, such as RBM15, RBM15B, ALKBH5, IGF2BP3, and PRRC2A. Some regulators functioned as tumour suppressors, including METTL5, YTHDC2, and G3BP1. Higher expression levels of these regulators often conveyed a favourable clinical prognosis. Given that most regulators exhibited clinical significance, we felt that an m6A regulator-based prognostic signature (m6A score) may be a useful molecular model for LS-SCLC. Therefore, using the LASSO Cox model, we included the above 22 regulators and determined five significant candidates—G3BP1, METTL5, ALKBH5, IGF2BP3, and RBM15B—for the subsequent m6A score creation (Fig. 2D, E).
Establishment of the m6A score for LS-SCLC
Based on the expression levels of these five regulators and corresponding coefficients, we constructed the m6A score system for patients with LS-SCLC. The formula is as follows: m6A score = (0.0906 × G3BP1 expression) + (0.4096 × METTL5 expression) − (0.6365 × ALKBH5 expression) − (0.0912 × IGF2BP3 expression) − (0.0660 × RBM15B expression). We calculated the m6A scores for each patient and classified them into high- (m6A score ≥ − 1.271) and low-score (m6A score < − 1.271) groups according to the optimal cut-off point (Fig. 3A). The principal component analysis revealed high heterogeneity between the high- and low-score groups (Fig. 3B). The Kaplan-Meier survival curve results indicated that high-score patients suffered significantly worse OS (Fig. 3C, hazard ratios (HR) 5.19, 95% confidence interval (CI) 2.75–9.77, P < 0.001). To evaluate the prognostic performance of the m6A score, a time-dependent ROC analysis was conducted. The m6A score achieved area under the curve (AUC) values of 0.672, 0.812, and 0.793 for predicting OS within the international cohort at 1, 3, and 5 years, respectively (Fig. 3D). Further ROC analysis revealed that the m6A score performed better than clinicopathological characteristics for predicting OS (Fig. 3E). The C-index of the m6A score reached 0.881. This indicated that the prognostic accuracy of the m6A score was also higher than other clinicopathological parameters (Fig. 3F).
Validation of the m6A score in multiple cohorts
To further assess the reliability and robustness of the classifier, we incorporated another two independent cohorts of 197 samples for validation, including the Shanghai cohort (N = 47) and the NCC cohort (N = 150). The cohorts’ clinicopathologic features are presented in Table 1. Using the same formula, the two cohorts were divided into low- and high-score groups. Firstly, we tested the m6A score in the Shanghai cohort. As expected, the signature showed excellent repeatability and stability during validation (Fig. 4A). The high-score patients in the Shanghai cohort suffered shorter OS than low-score patients (Fig. 4B, P = 0.006). The AUCs were 0.652, 0.733, and 0.731 for predicting 1-, 3-, and 5-year OS in the Shanghai cohort, respectively (Fig. 4C). In the Shanghai cohort, both the m6A score (C-index = 0.862) and SCLC staging (C-index = 0.880) accurately predicted OS for patients with LS-SCLC (Fig. 4D).
The clinical applicability of the m6A score was further evaluated in the FFPE specimens from the NCC cohort. Here, the low-score patients tended to significant better clinical outcomes in terms of OS (Fig. 4E, F, P < 0.001), and the m6A score achieved AUCs of 0.794, 0.691, 0.686 at 1-, 3-, and 5-year OS, respectively (Fig. 4G). Also, the C-index of the m6A score for OS was up to 0769 and higher than other factors in the NCC cohort (Fig. 4H).
We evaluated the predictive performance of the m6A score for RFS in patients with SCLC (Fig. 4I). The high m6A score was also predictive of poorer RFS in the NCC cohort (Fig. 4J, P < 0.001). The AUCs of m6A score for 1-, 3-, and 5-year RFS predictions were 0.713, 0.682, and 0.695, respectively, and the C-index was 0.748 in the NCC cohort (Fig. 4K, L). Thus, the m6A score was superior to the TNM system and sufficiently reliable to predict prognosis in patients with SCLC—both for OS and RFS.
We additionally explored the prognostic significance of the m6A score in relationship to various clinicopathological features—including sex, age, and smoking history. Because the sample size of the Shanghai cohort was small, we only performed clinical subgroup analyses on the international and NCC cohorts. As illustrated in Additional file 2: Fig. S3-S4, in the international cohort, low-score cases achieved longer OS and RFS across all clinical subtypes, including male, female, smoker, older (age ≥ 60), and younger (age < 60) (P < 0.05). The same results were obtained during the NCC cohort validation (Additional file 2: Fig. S3-S4, P < 0.05).
The m6A score was an independent prognostic predictor in LS-SCLC
To confirm whether the m6A score is an independent predictor of prognosis in SCLC, we carried out univariate and multivariate Cox regression analyses on three independent cohorts. Sex and the m6A score were significantly related to OS in the international cohort; staging and the m6A score were also correlated with the prognosis in the Shanghai cohort. Age and the m6A score were significantly associated with survival in the NCC cohort (Fig. 5A, P < 0.05). Moreover, after integrating these clinical parameters into the multivariate Cox regression analyses, the m6A score was the only stable, independent prognostic indicator for patients with SCLC across all three cohorts (Fig. 5B, P < 0.05). Additionally, after multivariable adjustment by clinicopathological features, the m6A score remained a significant independent prognostic factor for RFS in the NCC cohort (Additional file 1: Table S3).
The m6A score predicts the benefits of ACT
Considering the decisive role of m6A regulators in chemotherapy resistance, we further explored whether the m6A score could predict ACT treatment benefit. In the international and NCC cohorts, 42 and 129 cases underwent ACT, respectively. The m6A score divided 23 and 19 of 42 patients into high- and low-score groups in the international cohort (Fig. 6A), respectively, and divided 75 cases into the high-score group and 54 cases into the low-score group NCC cohort (Fig. 6D). Those low-score patients benefited considerably from ACT and achieved much longer OS than the high score cases in either cohort (Fig. 6A, D, both P < 0.001). Additionally, ROC curves showed that the AUCs of m6A score for predicting ACT OS benefit were 0.768, 0.901, and 0.82, and 0.807, 0.68, and 0.67 in the international cohort and NCC cohort for 1-, 3-, and 5-year, respectively (Fig. 6B, E). Meanwhile, the C-index of the m6A score for OS was also higher than other clinicopathological characteristics and as high as 0.956 and 0.750 in the two cohorts, respectively (Fig. 6C, F). In the NCC cohort, high-score cases suffered shorter RFS than the low-score ones (Fig. 6F, P < 0.001). The m6A score also achieved a reliable predictive ability to stratify different RFS statuses for patients with ACT. For AUCs of 0.708, 0.683, 0.66 at 1-, 3-, and 5-year RFS, the corresponding C-index was up to 0.734 (Fig. 6G, H). Collectively, the m6A score was able to identify those patients with SCLC most likely to benefit from ACT.
Relationship between the m6A score and the anti-PD-1 immunotherapy response
Previous studies have demonstrated that m6A regulators relate to anti-tumour immune effects and tumour immune microenvironment (TIME) characterizations [27]. Since the m6A score is based on various m6A regulators, we decided to probe the relationship between the m6A signature and TIME features. Considering the centrality of CD8+ T cells in TIME, we explored the relationship between CD8+ T cell infiltration and the signature in SCLC. Using strict quality controls, we finally incorporated 117 FFPE samples in the NCC cohort. The density of CD8+ T cells in the tumour regions of SCLC was detected and calculated using the HALO digital pathological platform. Each patient’s m6A score was also matched. Representative pictures of CD8+ T cell distribution from high- and low-score groups are displayed in Fig. 7A. Low-score patients tended to have more CD8+ T cell infiltration than high-score patients (Fig. 7B). In addition, the m6A score was negatively correlated with CD8+ T cell density in SCLCs (Fig. 7C, R = − 0.34, P < 0.001).
We collected the pre-treatment samples from 14 patients with SCLC who received anti-PD-1 treatment to investigate the relationship between the m6A score and responses to immunotherapy. The overall response rate was 35.71%. Interestingly, patients with low scores seemed to benefit more from immunotherapy, while those with high scores tended to be resistant to immunotherapy (Fig. 7D, E). Meanwhile, ROC analyses indicated that the m6A score could predict non-responders with an AUC of 0.8. The m6A score showed superior performance than age or sex for identifying non-responders to immunotherapy (Fig. 7F).