Skip to main content
Download PDF

m6A regulator expression profile predicts the prognosis, benefit of adjuvant chemotherapy, and response to anti-PD-1 immunotherapy in patients with small-cell lung cancer

BMC Medicine volume 19, Article number: 284 (2021) Cite this article

A Commentary to this article was published on 01 December 2021

Abstract

Background

Small cell lung cancer (SCLC) is lethal and possesses limited therapeutic options. Platinum-based chemotherapy—with or without immune checkpoint inhibitors (anti-PDs)—is the current first-line therapy for SCLCs; however, its associated outcomes are heterogeneous. N6-methyladenosine (m6A) is a novel and decisive factor in tumour progression, chemotherapy resistance, and immunotherapy response. However, m6A modification in SCLC remains poorly understood.

Methods

We systematically explored the molecular features and clinical significance of m6A regulators in SCLC. We then constructed an m6A regulator-based prognostic signature (m6A score) based on our examination of 256 cases with limited-stage SCLC (LS-SCLC) from three different cohorts—including an independent cohort that contained 150 cases with qPCR data. We additionally evaluated the relationships between the m6A score and adjuvant chemotherapy (ACT) benefits and the patients’ responses to anti-PD-1 treatment. Immunohistochemical (IHC) staining and the HALO digital pathological platform were used to calculate CD8+ T cell density.

Results

We observed abnormal somatic mutations and expressions of m6A regulators. Using the LASSO Cox model, a five-regulator-based (G3BP1, METTL5, ALKBH5, IGF2BP3, and RBM15B) m6A score was generated from the significant regulators to classify patients into high- and low-score groups. In the training cohort, patients with high scores had shorter overall survival (HR, 5.19; 2.75–9.77; P < 0.001). The prognostic accuracy of the m6A score was well validated in two independent cohorts (HR 4.6, P = 0.006 and HR 3.07, P < 0.001). Time-dependent ROC and C-index analyses found the m6A score to possess superior predictive power than other clinicopathological parameters. A multicentre multivariate analysis revealed the m6A score to be an independent prognostic indicator. Additionally, patients with low scores received a greater survival benefit from ACT, exhibited more CD8+ T cell infiltration, and were more responsive to cancer immunotherapy.

Conclusions

Our results, for the first time, affirm the significance of m6A regulators in LS-SCLC. Our multicentre analysis found that the m6A score was a reliable prognostic tool for guiding chemotherapy and immunotherapy selections for patients with SCLC.

Peer Review reports

Background

Small cell lung cancer (SCLC) is the most lethal high-grade neuroendocrine malignancy and features fast growth, early metastasis, and drug resistance. SCLC accounts for about 15% of all lung cancers; however, it has the highest mortality and worst outcomes—with a 5-year survival of < 7% [1, 2]. Regrettably, therapeutic strategies for SCLC have not significantly improved over recent decades. Conventional platinum-based chemotherapy remains the first-line treatment for patients with SCLC. Meanwhile, there have been few improvements in our ability to combat chemotherapy resistance for patients with SCLC [3]. Given the favourable achievements of immune checkpoint blockade (ICB) therapy for various tumours, this type of immunotherapy may be useful for SCLC treatment [4, 5]. Notably, a significant proportion of patients with ICB therapy resistance cannot benefit from such novel treatment [6,7,8]. Because of this, accurate and timely screening for patients who are more likely to benefit from immunotherapy is important.

PD-L1 expression is a classical biomarker for immunotherapy in various tumours, which is usually low or absent in SCLC. Consequently, it may fail to function as an immunotherapeutic biomarker [9, 10]. Therefore, there is an urgent and unmet need for a reliable predictive biomarker to guide the clinical application of chemotherapy and immunotherapy in patients with SCLC.

Dysregulation of epigenetic modifications relates to progression and treatment resistance in SCLC [11]. N6-methyladenosine (m6A) is the most prevalent type of RNA modification in eukaryotic cells [12], is responsible for various RNA-related biological processes—including RNA decay, stabilization, translation, splicing, and exportation—and ultimately regulates target gene expression [13]. Modification of m6A is a dynamic, multi-layered, and reversible process regulated by m6A methyltransferases, demethylases, and binding proteins [14]. Aberrant expression of m6A regulators appears closely related to carcinogenesis, tumour development, and immunological abnormalities [15, 16]. Multiple studies have revealed that m6A dysregulation dramatically enhances chemotherapy resistance in various tumours [17, 18]. Moreover, some m6A regulators can affect the response to immunotherapy [19, 20]. Increasing evidence suggests that m6A regulators are promising prognostic biomarkers which help determine chemotherapy and immunotherapy resistance. As the relevant research continues, these regulators’ relevance to a variety of tumours has been gradually confirmed [21, 22]. However, to the best of our knowledge, almost nothing is known about the roles of these m6A regulators in SCLC.

We examined the expression profiles, molecular characteristics, and prognostic significance of m6A regulators in SCLC. As early screening for lung cancer continues, the proportion of patients with limited-stage SCLC (LS-SCLC) is expected to similarly increase. We examined 265 cases with LS-SCLC from three independent cohorts and constructed an m6A regulator-based prognostic risk stratification score (m6A score) for patients with LS-SCLC. We additionally investigated the relationship between m6A score and adjuvant chemotherapy (ACT) benefit and response to anti-PD-1 treatment. Our findings may advance our ability to create individualized therapy regimens and guide SCLC prognostication.

Methods

Clinical samples and SCLC tissue specimens

We downloaded the training cohort somatic mutations and expression profiles (the international cohort) from Cbioportal (https://www.cbioportal.org/study/summary?id=sclc_ucologne_2015). During data processing, all RNA-seq data was subjected to log2 transformation. The mean expression values were selected when targeting genes that had more than one probe. We chose the GSE40275 database to explore the expression profile of m6A regulators, both in normal and LS-SCLC tissues. This dataset was downloaded from the Gene Expression Omnibus (GEO) dataset (https://www.ncbi.nlm.nih.gov/geo/) via the GPL15974 platform. Two validation cohorts—including the Shanghai cohort (GSE60052) downloaded from the GEO dataset and the National Cancer Centre (NCC) cohort, collected from the National Cancer Centre of China from January 2009 to November 2017—were collected. The NCC cohort included 150 LS-SCLC samples with formalin-fixed paraffin-embedded (FFPE) archived tissues were collected during surgeries. All enrolled patients from the NCC cohort were pathologically reconfirmed, had no other tumours, and carried clinically confirmed diagnoses of LS-SCLC. The relapse-free survival (RFS) and overall survival (OS) were defined as the day of surgical removal to recurrence, metastasis, or latest follow-up and the day of surgical removal to the date of death or latest follow-up. The study was approved by the Ethics Committee Board of our institute. The demographic and clinicopathologic parameters of the 150 LS-SCLC samples are displayed in Table 1.

Table 1 Clinical characteristics of the patients from multiple institutions
Full size table

Collection of samples with anti-PD-1 treatment

We included 14 patients with SCLC who received sequential chemotherapy and anti-PD-1 treatment in our hospital from April 2019 to January 2021. Their baseline biopsy FFPE specimens before immunotherapy were collected. The RECIST V1.1 Criteria were used to evaluate the response to therapy.

Immunochemistry and quantification of CD8+ T cells

The 4-μm FFPE slides were subjected to immunochemical staining. After deparaffinization and rehydration with high-concentration ethanol and pure water, the slides were incubated in 3% H2O2 or 15 min to block endogenous peroxidase activity. Then, the slides were subjected to heat antigen retrieval and non-specific site blocking using an EDTA buffer (PD 9.0) and 10% standard serum, respectively. Next, the slides were incubated overnight at 4 °C. The final counterstaining was performed using secondary antibodies (CD8, Abcam, ab17147, 1:100) and the 3, 3′-diaminobenzidine (DAB, Dako, Glostrup, Denmark) and haematoxylin.

The digital pathological system (HALO) was utilized to quantify the density of CD8+ T cells on the whole slides. We scan the slides images at high resolution (× 400) using the Pannoramic MIDI II slide scanner (3DHISTECH). The tumour regions were identified by a trained pathologist (LYX). The “Membrane IHC Quantification” module was selected for absolute counting of CD8+ T cells on the CaseViewer_2.3.

RNA isolation and quantitative RT-PCR

Only biopsies with at least 70% tumour cells were collected, and ~ 30-μm sections were cut from the FFPE samples. The Ambion RecoverAll Total Nucleic Acid Isolation Kit for FFPE (Thermo Fisher, Waltham, MA, USA) was used to isolate the FFPE tissue total RNA. The NanoDrop 2000C spectrophotometer (Thermo Scientific, Waltham, MA, USA) was used to quantify the extracted RNA. Meanwhile, the extracted RNA with an A260/A280 ratio between 1.8 and 2.2 were chosen for the quantitative RT-PCR (qRT-PCR) analysis. We used 200 ng RNA of a 20-μL reaction to reverse transcription through the FastKing Reverse Transcription Kit (Tiangen Biotech, Beijing, China). Next, we used 1 μL cDNA for PCR reaction with quantiNova PCR Kits (Qiagen, Dusseldorf, Germany) using 7900HT Fast Real-Time PCR system (Applied Biosystems, Carlsbad, USA; Indianapolis, IN). The qRT-PCR analysis was performed on all validation and independent cohort samples. The 2−ΔΔCt method was used to calculate the expressions of interested m6A regulators. The details of the target m6A regulators primer sequences for qRT-PCR are shown in Additional file 1: Table S1.

Construction of m6A regulator-based signature and statistical analysis

Firstly, we screened out the m6A regulators with prognostic significance based on the optimal cut-off point in the international cohort. We used the least shrinkage and selection operator (LASSO) Cox model to determine the most prognostic m6A regulators; the minimum criteria were chosen during the analysis process. Lastly, the final m6A score equation was accomplished based on the expression of the five chosen m6A regulators and corresponding Cox regression coefficients.

R version 3.5.1 (https://www.r-project.org) was used for all data analysis and image generation. The 30 m6A regulator protein-protein interaction analysis was conducted using the STRING interaction database and visualized using the Cytoscape software. The optimum cut-off survival analysis was completed using the “surv_cutpoint” function of the “survminer” R package. The Kaplan-Meier curve model was used to determine the prognostic value of the m6A regulator-based signature in the training and validation sets. The R package “survival” was employed to determine if the m6A score was an independent prognostic predictor for SCLC. The time-dependent receiver operating characteristic (ROC) curve analysis was created with the “survivalROC” R package. The Mann–Whitney U test was chosen to calculate the CD8+ T cell density between high- and low-score patients. A P-value less than 0.05 was considered statistically significant.

Results

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).

Fig. 1
figure 1

Molecular characteristics of m6A regulators in LS-SCLC. A Summary of the m6A regulators incorporated in this study. B The mutation frequency of m6A regulators in LS-SCLC. C Principal component analysis of SCLC and normal lung tissues based on the expression profile of 30 m6A regulators from GSE40275. D The heatmap of 30 m6A regulator expression from GSE40275. E The expression detail of m6A regulators in SCLC and normal lung tissues from GSE40275

Full size image

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.

Fig. 2
figure 2

The clinical significance of m6A regulators in LS-SCLC. A Correlation between the expression of each m6A regulator in LS-SCLC. Negative correlations are marked with blue, and positive correlations are marked with red. The scatter plot indicates the highest correlation coefficient group (YTHDF3 and KIAA1429, Pearson R = 0.820). B Protein-protein interactions among the m6A regulators. C Forest plot of the association between m6A regulators and prognosis in SCLC. D 100-fold cross-validation for tuning parameter selection in the LASSO model. E LASSO coefficient profiles of the most useful prognostic regulators

Full size image

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).

Fig. 3
figure 3

m6A score distribution and survival of patients in the international cohort. A m6A score distribution with patient survival status in the international cohort. The red colour indicates deceased patients while blue indicates survivors. Expression distribution of the five regulators in the international cohort, with red colour indicating higher expression and blue indicating lower expression. B Principal component analysis of high- and low-score patients based on the expression profile of five m6A regulators in the international cohort. C Kaplan-Meier curves of OS in 68 patients of the international cohort based on the m6A score. D ROC analysis of m6A score for the prediction of survival at 1, 3, and 5 years. E ROC analysis of m6A score and clinicopathological parameters for the prediction of survival at 5 years. F C-index values of m6A score and clinicopathological parameters for OS in the training cohort

Full size image

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).

Fig. 4
figure 4

m6A score distribution and survival of patients in multiple validation cohorts. A The m6A score distribution with patient survival status in the Shanghai cohort. The red colour indicates deceased patients while blue indicates survivors. Expression distribution of the five regulators, with red colour indicating higher expression and blue indicating lower expression. B Kaplan-Meier curves of OS in 47 patients from the Shanghai cohort based on the m6A score. C ROC analysis of m6A score for survival prediction at 1, 3, and 5 years in the Shanghai cohort. D C-index values of the m6A score and clinicopathological parameters for OS in the Shanghai cohort. E The m6A score distribution with patient survival status in the NCC cohort. Expression distribution of the five regulators in the NCC cohort. F Kaplan-Meier curves of OS in 150 patients of the NCC cohort based on the m6A score. G ROC analysis of the m6A score for predicting survival at 1, 3, and 5 years in the NCC cohort. H C-index values of m6A score and clinicopathological parameters for OS in the NCC cohort. I The m6A score distribution with patient recurrence status in the NCC cohort, red indicating tumour recurrence while blue indicates no-recurrence. Expression distribution of the five regulators in the NCC cohort, with red colour indicating higher expression and blue indicating lower expression. J Kaplan-Meier curves of RFS in 150 patients of the NCC cohort based on m6A score. K ROC analysis of m6A score for the prediction of RFS at 1, 3, and 5 years in the NCC cohort. H C-index values of m6A score and clinicopathological parameters for RFS in the NCC cohort

Full size image

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).

Fig. 5
figure 5

Cox regression analyses of the m6A score across multiple centres. A Univariate Cox regression analyses of m6A score and clinicopathological in three independent cohorts. B Multivariate Cox regression analyses of m6A score and clinicopathological in three independent cohorts

Full size image

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.

Fig. 6
figure 6

The predictive value of the m6A score for the benefit of adjuvant chemotherapy in different cohorts. A Kaplan-Meier curves of OS in patients with adjuvant chemotherapy in the international cohort. B ROC analysis of m6A score for the prediction of OS at 1, 3, and 5 years in patients with adjuvant chemotherapy in the international cohort. C C-index values of m6A score and clinicopathological parameters for OS in patients with adjuvant chemotherapy in the international cohort. D Kaplan-Meier curves of OS in patients with adjuvant chemotherapy in the NCC cohort. E ROC analysis of m6A score for predicting survival at 1, 3, and 5 years in patients with adjuvant chemotherapy in the NCC cohort. F C-index values of m6A score and clinicopathological parameters for OS in patients with adjuvant chemotherapy in the NCC cohort. G Kaplan-Meier curves of RFS in patients with adjuvant chemotherapy in the NCC cohort. H ROC analysis of m6A score for the prediction of RFS at 1, 3, and 5 years in patients with adjuvant chemotherapy in the NCC cohort. I C-index values of m6A score and clinicopathological parameters for RFS in patients with adjuvant chemotherapy in the NCC cohort

Full size image

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).

Fig. 7
figure 7

The m6A score predicts anti-PD-1 immunotherapy response in SCLC. A Representative immunohistochemical images of CD8+ T cell infiltration in SCLCs from the low- (case 1) and high-score (case 2) groups, respectively. The black arrows indicate the CD8 markers. B Pearson correlation analysis the CD8+ T cell density (log2 transformed) and the m6A score. C The density of CD8+ T cells (log2 transformed) in the low- and high-score groups. D A waterfall plot of m6A score distribution in patients with anti-PD-1 immunotherapy. Blue, pink, and red represent partial response (PR), stable disease (SD), and progressive disease (PD), respectively. E Differences of m6A scores in responders (PR) and non-responders (SD and PD). F ROC curves for the performance of the m6A score in predicting non-responders of immunotherapy in SCLC

Full size image

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).

Discussion

Recent studies have indicated that m6A modification and multiple regulators play pivotal roles in tumorigenesis, tumour progression, and the anti-tumour immune response [11]. We also know that m6A regulators actively participate in mediating responses to chemotherapy and immunotherapy. Some proof-of-concept preclinical data have found that various m6A regulators inhibitors exhibit significant antitumor therapeutic potential, especially enabling dramatic increases in immunotherapy efficacy [19, 20, 28]. Therefore, the relevant mechanisms and clinical significance of m6A regulators are extremely important.

Although the functions of m6A modification and regulators in various tumours have been elucidated [21, 22], their roles and clinical values in SCLC were unknown. As our ability to detect and diagnose early-stage lung cancer increases, the proportion of LS-SCLC cases has similarly increased. We constructed an m6A regulator-based signature to predict prognosis for patients with LS-SCLC. We also explored the signature’s predictive role for chemotherapy and immunotherapy in SCLC. Our findings should enhance our understanding of tumorigenesis and help inform the clinical management of this disease.

Various epigenetic abnormalities are closely associated with the malignant phenotype, aggressiveness, metastasis, and therapeutic resistance of SCLC [11]. The m6A modification is the most essential RNA modification in eukaryotic cells; however, the m6A modification is poorly explored in SCLC. In the present study, we comprehensively revealed the m6A modification patterns in SCLC and identified that aberrant expression of m6A regulators was closely involved in SCLC tumorigenesis. We also found that most m6A methyltransferases and binding proteins were remarkably upregulated, while m6A demethylases were downregulated. Thus, abundant m6A modification may play a dominant role in SCLC progression.

We additionally excluded over 22 m6A regulators closely associated with SCLC prognosis and then established a five-regulator-based m6A score to effectively divide patients with SCLC into low- and high-score groups. During this process, the LASSO model was chosen because the collinearity relationships were found among the regulators. The low-score patients exhibited a more favourable prognosis than their high-score counterparts for OS and RFS. The signature was well-validated in various validation cohorts and was identified as an independent prognostic indicator for patients with SCLC. Moreover, we have also confirmed that our signature possesses significantly superior stratification ability for multiple clinical parameters among the three multicentre cohorts.

The m6A regulator-based signature included protective (ALKBH5, IGF2BP3, and RBM15B) and risk-enhancing (G3BP1 and METTL5) factors. ALKBH5, one of the classical m6A demethylases, decreases m6A modification in the target RNA. ALKBH5 is involved in the progression of multiple cancers, playing an oncogenic role in glioblastoma while suppressing the tumour proliferation and development in pancreatic cancer and NSCLC [29,30,31]. Meanwhile, a higher expression of ALKBH5 was also positively correlated with a favourable prognosis in gastric cancer; however, it was associated with worse clinical outcomes in colorectal cancer and NSCLC [32,33,34]. IGF2BP3 is a member of the IGF2 mRNA binding protein family—also known as the m6A binding protein—which exerts its biological functions in various human cancers [35]. IGF2BP3 functions as an oncofoetal factor in multiple tumour types, facilitating tumorigenesis by regulating the cell cycle, proliferation, and angiogenesis [36, 37]. In the previous studies, IGF2BP3 was considered a poor prognostic factor for NSCLC, prostate cancer, and endocrine system tumours [38,39,40]. RBM15B was classified into the m6A methyltransferases type, responsible for confirming that the m6A classical methyltransferase complex can function in specific regions. Higher expression levels of RBM15B tend to confer better clinical outcomes for patients with kidney renal cell carcinoma [41].

Among the risky candidates, G3BP1 was a novel m6A-binding protein that affects mRNA stability via an m6A modification manner. This further regulated some essential oncogenic signal pathways to promote tumour metastasis and aggressiveness [42]. The elevated expression of G3BP1 confers a worse prognosis for patients with lung cancer after surgery [43]. METTL5 is a novel m6A methyltransferase, mainly adding m6A modification for ribosomal RNA [44]. Our earlier work found that METTL5 was significantly associated with a worse prognosis in NSCLC [45]. One small-scale study sought to determine the function of METTL5 in carcinogenesis; however, additional studies are needed.

We could also use the m6A score to identify patients with SCLC who were more likely to benefit from ACT. Our novel m6A score possessed a better predictive capacity of ACT efficacy than TNM staging. This may be useful for the individualized application of ACT in patients with SCLC. Additionally, some m6A regulators in the signature appeared closely associated with chemotherapy resistance. ALKBH5 can induce cisplatin resistance by decreasing the m6A modification on the FOXM1 and NANOG transcripts and increasing their expression [46]. Also, upregulating ALKBH5 expression sensitizes pancreatic ductal adenocarcinoma cells to chemotherapy treatment, indicating that ALKBH5 may play the same role in SCLC [30]. Chen et al. reported that IGF2BP3 sustained the pluripotency in hepatocellular carcinoma (HCC) cells and triggered chemoresistance in HCC [47]. Lower expression of G3BP1 increases the chemotherapy sensitivity in gastric cancer cells and predicts favourable benefits of chemotherapy and prognosis for patients with gastric cancer. This is in accordance with the potential role of G3BP1 in our m6A score system in SCLC [48]. Collectively, we speculate that the regulators in the m6A score may help regulate ACT sensitivity and resistance in SCLC. Future studies are necessary to uncover the underlying relationships between these regulators and chemotherapy resistance in SCLC.

We discovered a relationship between the m6A score and immunotherapy responses in SCLC. PD-L1 expression and CD8+ T cells are closely associated with the efficacy of immunotherapy on various malignancies. Notably, PD-L1 expression is typically low or absent in SCLC. Given the obvious subjectivity and uncertainty in interpreting PD-L1 expression, we finally explored the relationship between CD8+ T cells and m6A score in SCLC [49]. As expected, the m6A score was closely correlated with CD8+ T cells in SCLC, and patients with low scores exhibited higher CD8+ T infiltration.

Then, we investigated the potential role of the m6A score in predicting the immunotherapy response in patients with SCLC. Consistent with the above observations, low-score patients were more likely to benefit from immunotherapy. We also noted that some signature members appeared to relate to immunotherapy efficacy, especially demethylase ALKBH5. ALKBH5 regulates the immunotherapy responses by manipulating the accumulation of suppressive immune cells in TIME, actively modulating the infiltration of Tregs and myeloid-derived suppressor cells [50]. ALKBH5 may participate in the composition and function of CD8+ T cells in the TIME, ultimately affecting the response to immunotherapy in SCLC, while other regulators may also function in the same way. Further exploring the functions of these five m6A regulators may help us understand the nature of SCLC and provide some clues to further personalize immunotherapy application in patients with SCLC.

To our best knowledge, this is the first systematic examination of m6A modification patterns in LS-SCLC. We established a comprehensive m6A regulator prognostic signature based on data obtained from over 265 patients with LS-SCLC from three centres. Large-scale retrospective SCLC analyses are exceptionally rare due to challenges in obtaining available tumour specimens within the context of standardized treatment regimens.

Our innovative signature has certain advantages. Firstly, the large size of our study cohort increases the reliability and robustness of our model. Additionally, our signature is the first molecular model to predict chemotherapy and immunotherapy efficacy for patients with SCLC based on tissue samples. This signature may therefore be useful in treating and clinically managing patients with SCLC.

In addition to these advantages, our study also possesses some limitations which warrant consideration. Firstly, we validated the NCC cohort using retrospective FFPE specimens. Future, studies should collect and examine fresh specimens in a prospective manner. Secondly, despite we did our best efforts to collect the immunotherapy samples for validation, we only included 14 patients with SCLC who received immunotherapy. This is likely insufficient for conducting a comprehensive analysis. Thirdly, given that this was a retrospective study, there is likely to be unavoidable bias and error in the analysis. Prospective, well-powered studies are needed to further validate the reliability of the signature.

Conclusions

In conclusion, we demonstrated the significance of m6A modification in SCLC and developed the first and most comprehensive multicentre m6A regulator-based prognostic signature for patients with LS-SCLC. This m6A signature can accurately predict OS, RFS, chemotherapy benefit, and immunotherapy response in patients with SCLC. The m6A signature can therefore serve as both a prognostic and predictive tool for SCLC. Further prospective validation of the predictive ability of the m6A score will aid our ability to effectively treat patients with SCLC.

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ACT:

Adjuvant chemotherapy

AUC:

Area under the curve

CI:

Confidence interval

FFPE:

Formalin-fixed paraffin-embedded

GEO:

Gene Expression Omnibus

HCC:

Hepatocellular carcinomas

HR:

Hazard ratios

ICB:

Immune checkpoint blockade

LASSO:

Least shrinkage and selection operator

LS-SCLC:

limited-stage SCLC

m6A:

N6-methyladenosine

NCC:

National Cancer Centre

OS:

Overall survival

RFS:

Relapse-free survival

ROC:

Receiver operating characteristic

SCLC:

Small cell lung cancer

TIME:

Tumour immune microenvironment

References

  1. van Meerbeeck JP, Fennell DA, De Ruysscher DK. Small-cell lung cancer. Lancet (London, England). 2011;378(9804):1741–55.

    Article  Google Scholar 

  2. Gazdar AF, Minna JD. Developing new, rational therapies for recalcitrant small cell lung cancer. J Natl Cancer Inst. 2016;108(10):djw119. https://doi.org/10.1093/jnci/djw119.

    Article  PubMed  Google Scholar 

  3. Alvarado-Luna G, Morales-Espinosa D. Treatment for small cell lung cancer, where are we now?-a review. Transl Lung Cancer Res. 2016;5(1):26–38. https://doi.org/10.3978/j.issn.2218-6751.2016.01.13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Welsh JW, Heymach JV, Guo C, Menon H, Klein K, Cushman TR, et al. Phase 1/2 trial of pembrolizumab and concurrent chemoradiation therapy for limited-stage SCLC. J Thorac Oncol. 2020;15(12):1919–27.

    Article  CAS  PubMed  Google Scholar 

  5. Gong J, Chehrazi-Raffle A, Reddi S, Salgia R. Development of PD-1 and PD-L1 inhibitors as a form of cancer immunotherapy: a comprehensive review of registration trials and future considerations. J Immunother Cancer. 2018;6(1):8. https://doi.org/10.1186/s40425-018-0316-z.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Roviello G, Corona SP, Nesi G, Mini E. Results from a meta-analysis of immune checkpoint inhibitors in first-line renal cancer patients: does PD-L1 matter? Ther Adv Med Oncol. 2019;11:1758835919861905. https://doi.org/10.1177/1758835919861905.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lei Q, Wang D, Sun K, Wang L, Zhang Y. Resistance mechanisms of anti-PD1/PDL1 therapy in solid tumors. Front Cell Dev Biol. 2020;8:672. https://doi.org/10.3389/fcell.2020.00672.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Zhang C, Zhang Z, Sun N, Zhang Z, Zhang G, Wang F, et al. Identification of a costimulatory molecule-based signature for predicting prognosis risk and immunotherapy response in patients with lung adenocarcinoma. Oncoimmunology. 2020;9(1):1824641. https://doi.org/10.1080/2162402X.2020.1824641.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lu S, Stein JE, Rimm DL, Wang DW, Bell JM, Johnson DB, et al. Comparison of biomarker modalities for predicting response to PD-1/PD-L1 checkpoint blockade: a systematic review and meta-analysis. JAMA Oncol. 2019;5(8):1195–204. https://doi.org/10.1001/jamaoncol.2019.1549.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Thommen DS, Koelzer VH, Herzig P, Roller A, Trefny M, Dimeloe S, et al. A transcriptionally and functionally distinct PD-1(+) CD8(+) T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade. Nat Med. 2018;24(7):994–1004. https://doi.org/10.1038/s41591-018-0057-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Khan P, Siddiqui JA, Maurya SK, Lakshmanan I, Jain M, Ganti AK, et al. Epigenetic landscape of small cell lung cancer: small image of a giant recalcitrant disease. Semin Cancer Biol. 2020. https://doi.org/10.1016/j.semcancer.2020.11.006.

  12. Roundtree IA, Evans ME, Pan T, He C. Dynamic RNA modifications in gene expression regulation. Cell. 2017;169(7):1187–200. https://doi.org/10.1016/j.cell.2017.05.045.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wang T, Kong S, Tao M, Ju S. The potential role of RNA N6-methyladenosine in Cancer progression. Mol Cancer. 2020;19(1):88. https://doi.org/10.1186/s12943-020-01204-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Yang Y, Hsu PJ, Chen YS, Yang YG. Dynamic transcriptomic m6A decoration: writers, erasers, readers and functions in RNA metabolism. Cell Research. 2018;28(6):616–24. https://doi.org/10.1038/s41422-018-0040-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Pinello N, Sun S, Wong JJ. Aberrant expression of enzymes regulating m6A mRNA methylation: implication in cancer. Cancer Biol Med. 2018;15(4):323–34. https://doi.org/10.20892/j.issn.2095-3941.2018.0365.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Tong J, Cao G, Zhang T, Sefik E, Amezcua Vesely MC, Broughton JP, et al. m6A mRNA methylation sustains Treg suppressive functions. Cell Res. 2018;28(2):253–6. https://doi.org/10.1038/cr.2018.7.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Jin D, Guo J, Wu Y, Du J, Yang L, Wang X, et al. m6A mRNA methylation initiated by METTL3 directly promotes YAP translation and increases YAP activity by regulating the MALAT1-miR-1914-3p-YAP axis to induce NSCLC drug resistance and metastasis. J Hematol Oncol. 2019;12(1):135. https://doi.org/10.1186/s13045-019-0830-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhou S, Bai ZL, Xia D, Zhao ZJ, Zhao R, Wang YY, et al. FTO regulates the chemo-radiotherapy resistance of cervical squamous cell carcinoma (CSCC) by targeting β-catenin through mRNA demethylation. Mol Carcinog. 2018;57(5):590–7. https://doi.org/10.1002/mc.22782.

    Article  CAS  PubMed  Google Scholar 

  19. Li HB, Tong J, Zhu S, Batista PJ, Duffy EE, Zhao J, et al. m6A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways. Nature. 2017;548(7667):338–42. https://doi.org/10.1038/nature23450.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Han D, Liu J, Chen C, Dong L, Liu Y, Chang R, et al. Anti-tumour immunity controlled through mRNA m6A methylation and YTHDF1 in dendritic cells. Nature. 2019;566(7743):270–4. https://doi.org/10.1038/s41586-019-0916-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zhao X, Cui L. Development and validation of a m6A RNA methylation regulators-based signature for predicting the prognosis of head and neck squamous cell carcinoma. Am J Cancer Res. 2019;9(10):2156–69.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Zhou J, Wang J, Hong B, Ma K, Xie H, Li L, et al. Gene signatures and prognostic values of m6A regulators in clear cell renal cell carcinoma - a retrospective study using TCGA database. Aging. 2019;11(6):1633–47. https://doi.org/10.18632/aging.101856.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Li Y, Xiao J, Bai J, Tian Y, Qu Y, Chen X, et al. Molecular characterization and clinical relevance of m6A regulators across 33 cancer types. Mol Cancer. 2019;18(1):137. https://doi.org/10.1186/s12943-019-1066-3.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Liu J, Harada BT, He C. Regulation of gene expression by N6-methyladenosine in cancer. Trends Cell Biol. 2019;29(6):487–99. https://doi.org/10.1016/j.tcb.2019.02.008.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Huang H, Weng H, Chen J. m6A modification in coding and non-coding RNAs: roles and therapeutic implications in cancer. Cancer Cell. 2020;37(3):270–88. https://doi.org/10.1016/j.ccell.2020.02.004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Nombela P, Miguel-López B, Blanco S. The role of m6A, m5C and Ψ RNA modifications in cancer: novel therapeutic opportunities. Mol Cancer. 2021;20(1):18. https://doi.org/10.1186/s12943-020-01263-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wang L, Hui H, Agrawal K, Kang Y, Li N, Tang R, et al. m6A RNA methyltransferases METTL3/14 regulate immune responses to anti-PD-1 therapy. EMBO J. 2020;39(20):e104514. https://doi.org/10.15252/embj.2020104514.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wang H, Hu X, Huang M, Liu J, Gu Y, Ma L, et al. Mettl3-mediated mRNA m6A methylation promotes dendritic cell activation. Nat Commun. 2019;10(1):1898. https://doi.org/10.1038/s41467-019-09903-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zhang S, Zhao BS, Zhou A, Lin K, Zheng S, Lu Z, et al. m6A demethylase ALKBH5 maintains tumorigenicity of glioblastoma stem-like cells by sustaining FOXM1 expression and cell proliferation program. Cancer Cell. 2017;31(4):591–606.e596.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Tang B, Yang Y, Kang M, Wang Y, Wang Y, Bi Y, et al. m6A demethylase ALKBH5 inhibits pancreatic cancer tumorigenesis by decreasing WIF-1 RNA methylation and mediating Wnt signaling. Mol Cancer. 2020;19(1):3. https://doi.org/10.1186/s12943-019-1128-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Jin D, Guo J, Wu Y, Yang L, Wang X, Du J, et al. m6A demethylase ALKBH5 inhibits tumor growth and metastasis by reducing YTHDFs-mediated YAP expression and inhibiting miR-107/LATS2-mediated YAP activity in NSCLC. Mol Cancer. 2020;19(1):40. https://doi.org/10.1186/s12943-020-01161-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Su Y, Huang J, Hu J. m6A RNA methylation regulators contribute to malignant progression and have clinical prognostic impact in gastric cancer. Front Oncol. 2019;9:1038. https://doi.org/10.3389/fonc.2019.01038.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Liu X, Liu L, Dong Z, Li J, Yu Y, Chen X, et al. Expression patterns and prognostic value of m6A-related genes in colorectal cancer. Am J Transl Res. 2019;11(7):3972–91.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Zhuang Z, Chen L, Mao Y, Zheng Q, Li H, Huang Y, et al. Diagnostic, progressive and prognostic performance of m6A methylation RNA regulators in lung adenocarcinoma. Int J Biol Sci. 2020;16(11):1785–97. https://doi.org/10.7150/ijbs.39046.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lederer M, Bley N, Schleifer C, Hüttelmaier S. The role of the oncofetal IGF2 mRNA-binding protein 3 (IGF2BP3) in cancer. Semin Cancer Biol. 2014;29:3–12. https://doi.org/10.1016/j.semcancer.2014.07.006.

    Article  CAS  PubMed  Google Scholar 

  36. Yang Z, Wang T, Wu D, Min Z, Tan J, Yu B. RNA N6-methyladenosine reader IGF2BP3 regulates cell cycle and angiogenesis in colon cancer. J Exp Clin Cancer Res. 2020;39(1):203.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Huang W, Li Y, Zhang C, Zha H, Zhou X, Fu B, et al. IGF2BP3 facilitates cell proliferation and tumorigenesis via modulation of JAK/STAT signalling pathway in human bladder cancer. J Cell Mol Med. 2020;24(23):13949–60. https://doi.org/10.1111/jcmm.16003.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ji G, Huang C, He S, Gong Y, Song G, Li X, et al. Comprehensive analysis of m6A regulators prognostic value in prostate cancer. Aging. 2020;12(14):14863–84. https://doi.org/10.18632/aging.103549.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Li W, Li N, Gao L, You C. Integrated analysis of the roles and prognostic value of RNA binding proteins in lung adenocarcinoma. PeerJ. 2020;8:e8509. https://doi.org/10.7717/peerj.8509.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Li K, Luo H, Luo H, Zhu X. Clinical and prognostic pan-cancer analysis of m6A RNA methylation regulators in four types of endocrine system tumors. Aging. 2020;12(23):23931–44. https://doi.org/10.18632/aging.104064.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Fang J, Hu M, Sun Y, Zhou S, Li H. Expression profile analysis of m6A RNA methylation regulators indicates they are immune signature associated and can predict survival in kidney renal cell carcinoma. DNA Cell Biol. 2020;39(12):2194–211. https://doi.org/10.1089/dna.2020.5767.

    Article  CAS  Google Scholar 

  42. Wang Y, Fu D, Chen Y, Su J, Wang Y, Li X, et al. G3BP1 promotes tumor progression and metastasis through IL-6/G3BP1/STAT3 signaling axis in renal cell carcinomas. Cell Death Dis. 2018;9(5):501. https://doi.org/10.1038/s41419-018-0504-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zheng H, Zhan Y, Zhang Y, Liu S, Lu J, Yang Y, et al. Elevated expression of G3BP1 associates with YB1 and p-AKT and predicts poor prognosis in nonsmall cell lung cancer patients after surgical resection. Cancer Med. 2019;8(16):6894–903. https://doi.org/10.1002/cam4.2579.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. van Tran N, Ernst FGM, Hawley BR, Zorbas C, Ulryck N, Hackert P, et al. The human 18S rRNA m6A methyltransferase METTL5 is stabilized by TRMT112. Nucleic Acids Res. 2019;47(15):7719–33. https://doi.org/10.1093/nar/gkz619.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sun S, Fei K, Zhang G, Wang J, Yang Y, Guo W, et al. Construction and comprehensive analyses of a METTL5-associated prognostic signature with immune implication in lung adenocarcinomas. Front Genet. 2020;11:617174. https://doi.org/10.3389/fgene.2020.617174.

    Article  CAS  PubMed  Google Scholar 

  46. Shriwas O, Priyadarshini M, Samal SK, Rath R, Panda S, Das Majumdar SK, et al. DDX3 modulates cisplatin resistance in OSCC through ALKBH5-mediated m6A-demethylation of FOXM1 and NANOG. Apoptosis. 2020;25(3-4):233–46.

    Article  CAS  PubMed  Google Scholar 

  47. Chen CL, Tsukamoto H, Liu JC, Kashiwabara C, Feldman D, Sher L, et al. Reciprocal regulation by TLR4 and TGF-β in tumor-initiating stem-like cells. J Clin Invest. 2013;123(7):2832–49. https://doi.org/10.1172/JCI65859.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Zhao J, Fu X, Chen H, Min L, Sun J, Yin J, et al. G3BP1 interacts with YWHAZ to regulate chemoresistance and predict adjuvant chemotherapy benefit in gastric cancer. British J Cancer. 2021;124(2):425–36. https://doi.org/10.1038/s41416-020-01067-1.

    Article  CAS  Google Scholar 

  49. Iams WT, Porter J, Horn L. Immunotherapeutic approaches for small-cell lung cancer. Nat Rev Clin Oncol. 2020;17(5):300–12. https://doi.org/10.1038/s41571-019-0316-z.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Li N, Kang Y, Wang L, Huff S, Tang R, Hui H, et al. ALKBH5 regulates anti-PD-1 therapy response by modulating lactate and suppressive immune cell accumulation in tumor microenvironment. Proc Natl Acad Sci U S A. 2020;117(33):20159–70. https://doi.org/10.1073/pnas.1918986117.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

All authors would like to thank the specimen donors used in this study and the research groups who provided data for this collection. All authors would also like to thank CapitalBio Technology for their kind help.

Funding

This work was supported by the CAMS Innovation Fund for Medical Sciences (2017-I2M-1-005, 2016-I2M-1-001), the National Key R&D Program of China (2016YFC1303201), the National Natural Science Foundation of China (81802299, 81502514), the Fundamental Research Funds for the Central Universities (3332018070), the National Key Basic Research Development Plan (2018YFC1312105), the Beijing Natural Science Foundation (7204291), the Beijing Hope Run Special Fund of Cancer Foundation of China (LC2019B18), and the Beijing Natural Science Foundation (J20010).

Author information

Author notes

  1. Zhihui Zhang, Chaoqi Zhang and Yuejun Luo contributed equally to this work.

Authors and Affiliations

  1. Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China

    Zhihui Zhang, Chaoqi Zhang, Yuejun Luo, Peng Wu, Guochao Zhang, Qingpeng Zeng, Lide Wang, Fengwei Tan, Qi Xue, Shugeng Gao, Nan Sun & Jie He

  2. Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China

    Zhaoyang Yang, Liyan Xue, Bo Zheng & Hua Zeng

Authors
  1. Zhihui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chaoqi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuejun Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guochao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qingpeng Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lide Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zhaoyang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Liyan Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Bo Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hua Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Fengwei Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Qi Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Shugeng Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Nan Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Jie He
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

NS and JH supervised the project, designed, edited, and led the experiments. ZHZ, PW, and YJL conducted the experiments and data analysis. CQZ and ZHZ prepared all the figures and tables. ZHZ, CQZ, and YJL drafted the manuscript. CQZ, GCZ, QPZ, LDW, ZYY, LYX, BZ, HZ, FWT, QX, and SGG collected the clinical samples and provided material support. All the authors read and approved the final manuscript.

Authors’ information

Zhihui Zhang, Chaoqi Zhang, and Yuejun Luo contributed equally to this work.

Corresponding authors

Correspondence to Nan Sun or Jie He.

Ethics declarations

Ethics approval and consent to participate

The protocol of this study was approved by the Ethics Committee and Institutional Review Boards of Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Primer sequences of the samples from the NCC cohort for qPCR. Table S2. The descriptions of the 30 m6A regulators collected in this study. Table S3. Univariable and multivariate Cox regression of m6A score and clinicopathological characteristics and relapse free survival in SCLC.

Additional file 2: Fig. S1.

Co-occurrence of genetic alterations of the m6A regulators in SCLC. Fig. S2. Correlation between the expression of m6A regulators in SCLC. Fig. S3. Validation of the OS predictive performance of the m6A score across clinical subgroups. Fig. S4. Validation of the RFS predictive performance of the m6A score across clinical subgroups.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, Z., Zhang, C., Luo, Y. et al. m6A regulator expression profile predicts the prognosis, benefit of adjuvant chemotherapy, and response to anti-PD-1 immunotherapy in patients with small-cell lung cancer. BMC Med 19, 284 (2021). https://doi.org/10.1186/s12916-021-02148-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12916-021-02148-5

Keywords

  • m6A regulators
  • Small cell lung cancer
  • Chemotherapy
  • Immunotherapy
  • Individualized medicine

BMC Medicine

ISSN: 1741-7015

Contact us