Original Article N7-methylguanosine regulatory genes well represented by METTL1 define vastly different prognostic, immune and therapy landscapes in adrenocortical carcinoma
Fangshi Xu1,2, Danrui Cai3, Shanshan Liu4, Kaini He5, Jing Chen4, Li Qu4, Tie Chong1, Xueyi Li4, Bincheng Ren4
1Department of Urology, Second Affiliated Hospital of Xi’an Jiaotong University, No. 157, West Five Road, Xi’an 710004, Shaanxi, China; 2Department of Urology, Shaanxi Provincial People’s Hospital, No. 256, Friendship West Road, Xi’an 710068, Shaanxi, China; 3Department of Ophthalmology, Second Affiliated Hospital of Xi’an Jiaotong University, No. 157, West Five Road, Xi’an 710004, Shaanxi, China; 4Department of Rheumatology and Immunology, Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China; 5Department of Gastroenterology, Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
Received May 16, 2022; Accepted January 30, 2023; Epub February 15, 2023; Published February 28, 2023
Abstract: Although N7-methylguanosine (m7G) is one of the most frequent RNA modifications, it has received little attention. Adrenocortical carcinoma (ACC) is a highly malignant and easily metastatic tumor, eagerly needing for novel therapeutic strategy. Herein, a novel m7G risk signature (METTL1, NCBP1, NUDT1 and NUDT5) was con- structed using the Lasso regression analysis. It possessed highly prognostic value and could improve the predictive accuracy and clinical making-decision benefit of traditional prognostic model. Its prognostic value was also success- fully validated in GSE19750 cohort. Through CIBERSORT, ESTIMATE, ssGSEA and GSEA analyzes, high-m7G risk score was found to be closely associated with increased enrichment of glycolysis and suppression of anti-cancer immune response. Therapeutic correlation of m7G risk signature was also investigated using tumor mutation bur- den, the expressions of immune checkpoints, TIDE score, IMvigor 210 cohort and TCGA cohort. m7G risk score was a potential biomarker for predicting the efficacy of ICBs and mitotane. Furthermore, we explored the biofunctions of METTL1 in ACC cells through a series of experimentations. Overexpression of METTL1 stimulated the proliferation, migration and invasion of H295R and SW13 cells. Immunofluorescence assays revealed that the infiltrating levels of CD8+ T cells was lower and that of macrophages was higher in clinical ACC samples with high METTL1 expres- sion compared to that in low expression ones. Silencing METTL1 could significantly inhibited tumor growth in mouse xenograft model. Western blot assays showed that METTL1 positively regulated the expression of glycolysis rate- limiting enzyme HK1. Finally, miR-885-5p and CEBPB were predicted as the upstream regulators of METTL1 through data mining of the public databases. In conclusions, m7G regulatory genes well represented by METTL1 profoundly affected the prognosis, tumor immune, therapeutic outcomes, and malignant progression of ACC.
Keywords: N7-methylguanosine, adrenocortical carcinoma, risk signature, prognosis, METTL1
Introduction
Adrenocortical carcinoma (ACC) is a highly malignant and aggressive urologic cancer with a rare incidence of 2.0 per million [1]. Due to its atypical incipient symptoms, patients common- ly present advanced or metastatic disease at the time of initial diagnosis, with a five-year overall survival rate (OSR) < 15% [2]. Radical adrenalectomy is the only option to achieve ACC cure. However, cases suitable for surgery account for only approximately 30% of all ACC patients [3]. Apart from radical resection, other
current therapeutic approaches have not achieved satisfactory effects. As the mainstay adjuvant therapy of ACC, mitotane combined with EDP (etoposide, doxorubicin, and cisplat- in), can produce an objective response rate (ORR) of less than 30%, and progression-free survival (PFS) of patients who receive this treat- ment is only 5.6 months [4]. It is thus necessary to develop novel therapeutic strategies so as to improve prognostic evaluation system for ACC.
RNA epigenetic modulation is a current topic in oncology. One prominent example is N6-methy-
| Names | Gene counts | Description |
|---|---|---|
| GOMF m7G 5-PPPN Diphosphatase Activity | 12 | Catalysis of the reaction: 7-methylguanosine 5'-triphospho-5'-polynucleotide + H20 = 7-methylguanosine 5'-phosphate + polynucleotide |
| GOMF RNA 7-Methylguanosine Cap Binding | 13 | Binding to a 7-methylguanosine group added cotranscriptionally to the 5' end of RNA molecules tran- scribed by polymerase II |
| GOMF RNA Cap Binding | 20 | Binding to a 7-methylguanosine (m7G) group or derivative located at the 5' end of an RNA molecule |
ladenosine (m6A) modification, which is closely involved in the prognosis, immune response, and development of ACC [5-7]. N7-methy- lguanosine (m7G) is a further prevalent pattern of RNA modification, however, its roles in can- cer are so far unclear. m7G refers to the gua- nine methylation at the 5’-cap of RNA, com- monly occurring at position 46 (G46) in the vari- able region of the tRNA loop [8]. The functional complex consisting of methyltransferase 1 (METTL1) and the WD repeat domain 4 (WDR4) is responsible for this guanosine methylation process [9]. RNA exhibits higher stability after m7G modification, which has attracted research interest in oncology. METTL1/WDR4-mediated m7G tRNA modification promotes the progres- sion of lung cancer [10]. m7G modification has been a focus of cancer research, however, the association of m7G regulator genes and cancer prognosis, cancer treatment, and the tumor immune microenvironment (TIM) are so far unclear.
In view of above context, we constructed a novel m7G risk signature for ACC clinical assessment through Lasso regression analy- sis. Its prognostic potential, immune effects, metabolic impacts, mutation features, and therapeutic correlations were comprehensively investigated. More importantly, we confirmed the oncogenic abilities of METTL1, the most critical regulator in m7G modification, during ACC progression for the first time through in vitro experiments. Our findings provide new insights regarding ACC treatment and assess- ment options.
Materials and methods
Data source
Clinical and transcriptomic data were retrieved from the TCGA (https://portal.gdc.cancer.gov/)
and GEO (https://www.ncbi.nlm.nih.gov/geo/) public databases. No normal samples were available in the TCGA-ACC project, thus we used 128 normal adrenal tissue samples from the GTEx database (https://xenabrowser.net/ datapages/) to screen differentially express- ed genes (DEGs). All transcriptome data was standardized through log2 (FPKM+1) transfor- mation. The clinical characteristics of the TCGA and GSE19750 cohorts are shown in Supplementary Table 1.
m7G-related gene set
We reviewed studies on m7G modification and three pivotal gene sets in the Molecular Signatures Database (MSigDB) to establish an m7G-related gene set, which comprised 34 m7G regulators (Supplementary Table 2). Three MSigDB gene sets included ‘GOMF m7G 5-PPPN Diphosphatase Activity’, ‘GOMF RNA 7-Methylguanosine Cap Binding’ and ‘GOMF RNA Cap Binding’. Respective detailed descrip- tions are shown in Table 1. To further confirm the reliability of our m7G gene set, we con- structed its protein-protein interaction (PPI) network and conducted the corresponding bio- logical function analyses using the Metascape online tool (http://metascape.org/) [11].
Establishing the m7G-related risk signature
m7G-related DEGs were identified using the ‘Limma’ package in R software (version 4.1.2). The following screening criteria thresholds were used: adjusted p-value < 0.05 and the ab- solute value of log2 FC >1 (2-fold difference in gene expression). Next, we identified prognos- tic m7G genes through Cox univariate regres- sion analysis. The intersection between DEGs and prognostic genes was obtained through a Venn diagram. Finally, we established a novel
m7G risk signature of ACC using Lasso regres- sion analysis.
Evaluation of the prognostic value
The optimal cutoff value of the m7G risk score was calculated using the Cutoff Finder online tool (http://molpath.charite.de/cutoff) [12]. According to this cutoff value, 79 ACC sampl- es were assigned to high- and low-m7G risk groups. Then, survival differences between the risk groups were determined through Kaplan- Meier analyses. Cox univariate and multivariate analyses were used to identify the independ- ent prognostic factors. The predictive accuracy of the m7G risk signature was evaluated through a receiver operating characteristic curve (ROC). Decision curve analysis (DCA) was applied to assess whether the m7G risk score could improve the traditional prognostic model based on clinical stage. The clinical subgroup analyses were conducted to ensure the appli- cable scope of the m7G model in prognostic analyses. Due to insufficient samples in N1 stages (n = 10), we did not perform survival dif- ference analyses in this subgroup. We utilized a nomogram comprising TNM-staging and m7G risk scores to predict the overall survival rate of individuals at 2, 3, and 5 years. Its prognostic accuracy was assessed through calibration curves. Further, the prognostic value of the m7G risk signature was validated in the GSE19750 dataset.
Immune analyses
The infiltration levels of 22 immune cell sub- types in each ACC sample were calculated using the CIBERSORT algorithm [13]. The activi- ties of 10 immune-related pathways were quan- tified using single-sample gene set enrichment analysis (ssGSEA) [14]. The R package ‘Limma’ was applied to determine differences in infiltra- tion levels of immune cells and the activities of immune-related pathways between different m7G risk groups. The ESTIMATE algorithm was employed to compare differences in stromal, immune, and ESTIMATE scores between high- and low-risk groups [15]. The corresponding tumor purity of each ACC sample was quanti- fied through the same algorithm. The TIMER database offers a comprehensive resource for systematical analysis of immune infiltrates across diverse cancer types (https://cistrome. shinyapps.io/timer/) [16]. The correlations
between the somatic copy-number alterations (SCNAs) of m7G signature genes and the abundance of six core immune cells were ana- lyzed using the ‘SCNA’ module in the TIMER database.
Gene set enrichment analysis (GSEA)
GSEA was utilized to assess the impacts of m7G risk scores on multiple metabolic pro- cesses, including glycolysis, nucleotide metab- olism, amino acid (AA) metabolism, and fatty acid (FA) metabolism. Analytical gene sets were obtained from the MSigDB database, and their basic information is presented in Supplementary Table 3. Phenotype labels were set as high-m7G risk samples versus low-m7G risk ones. The number of permutations was 1,000, and gene symbols were not collapsed.
Calculation of the tumor mutation burden (TMB) and mutational analyses
The TMB of each ACC sample was calculated as the total mutation frequency divided by 38. The corresponding somatic mutation data were obtained from the TCGA database. The ‘Data Type’ and ‘Workflow Type’ were ‘Masked somatic mutation’ and ‘VarScan’, respectively. Mutational data were visualized using the R package ‘maftools’. The cBioPortal database (http://cbioportal.org) was used to acquire information on somatic mutation frequency and patterns of m7G signature genes across two ACC projects (n = 184 samples).
Therapeutic correlation analyses
The TCGA-ACC cohort was used to compare dif- ferences in m7G risk score between responding and non-responding patients treated with radi- ation and mitotane therapy. Then, we explored potential linkages between the efficacy of ICBs and the m7G risk score from four perspectives, including TMB, tumor immune dysfunction and exclusion (TIDE) scoring, immune checkpoints (ICs) expression, and the IMvigor 210 cohort. TMB is considered a promising biomarker for predicting the efficacy of immune checkpoint blockades (ICBs) [17, 18], thus TMB differences between high- and low-risk groups were deter- mined. The TIDE scoring system is a crucial method to predict patient responses to anti- PD-1/L1 and anti-CTLA4 treatments based on the estimation of T cell dysfunction and tumor
immune evasion [19]. Using an online tool (http://tide.dfci.harvard.edu/login/), the TIDE score of each ACC patient was calculated, and differences in TIDE scores between the high- and low-m7G risk groups were determined. Expression levels of ICs can reflect the poten- tial to benefit from ICBs [20], thus correlations of expression of six ICs and m7G risk scores were tested. We then used the IMvigor210 cohort that reported the therapeutic outcomes of PD-1 inhibitor atezolizumab and the corre- sponding transcriptomic data [21] to confirm differences in m7G risk score between patients responding and not responding to therapy.
Analysis in upstream regulatory mechanism of METTL1
Three miRNA databases were employed to predict potential miRNA responsible for nega- tively regulating METTL1, including miRDB (http://www.mirdb.org/) [22], TargetScanHum (Ver 8.0, http://www.targetscan.org/vert_80/) [23] and ENCORI (https://starbase.sysu.edu. cn/) [24]. The minimum free energy of predict- ed miRNAs was calculated using RNAhybrid (https://bibiserv.cebitec.uni-bielefeld.de/rna- hybrid/) online tool [25]. The binding site between miRNA and METTL1 was predicted using TargetScanHum database. Using Gene- Cards (https://www.genecards.org/) [26], ALG- GEN (https://alggen.lsi.upc.es/cgi-bin/promo_ v3/) [27] and hTFtarget (http://bioinfo.life.hust. edu.cn/hTFtarget/) [28] databases, we also investigated the potential regulatory transcrip- tion factors (TFs) of METTL1. The promoter sequences of METTL1 was obtained from UCSC genome database (http://genome.ucsc. edu/) [29]. The motif sequence of candidate TF and the prediction of binding site were derived from JASPAR database (https://jaspar.genereg. net/) [30].
Cell culture and transfection
Two adrenocortical cancer cell lines (H295R and SW13) were used for in vitro experiments. All cells were purchased from Procell Life Science & Technology Company (Wuhan, China). H295R cells were cultured in Dulbe- cco’s Modified Eagle Medium (DMEM) with 10% fetal bovine serum (FBS), termed DMEM/F12, ITS-G (an insulin, transferrin, and selenium solution) and 1% penicillin-streptomycin (P/S). SW13 cells were cultured in DMEM medium
containing 10% FBS and 1% P/S. sh-METTL1 and amplification plasmids (OE-METTL1) were purchased from HanHeng Biotechnology (Shanghai, China). Their respective sequences are shown in Supplementary Table 4. The cells were transfected using Lentiviruses (Hanheng Biotechnology, Shanghai, China).
Clinical samples and RT-qPCR
To confirm ectopic expression of METTL1 in ACC, we collected 10 pairs of ACC and adjacent normal tissues from the Department of Urology, Second Affiliated Hospital of Xi’an Jiaotong University to conduct RT-qPCRs. All patients provided written informed consent. The study protocol was approved by the Ethics Commit- tees of the Second Affiliated Hospital of Xi’an Jiaotong University.
Total RNA was extracted using TRIzol Reagent (TakaRa Bio, Shiga, Japan). RNA purity was measured based on the A260/A280 ratio us- ing a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Reverse transcription was performed using a PrimeScript RT Reagent Kit (TaKaRa Bio). Amplification was traced using SYBR-Green PCR Reagent (TaKaRa Bio) and an ABI Prism 7900 sequence detection system (Applied Biosystems, Foster City, CA, USA). GAPDH was used as an internal reference. The relative gene expression was calculated according to the 2-AACT method. Primer sequences are shown in Supplementary Table 5.
Western blot
The associations of METTL1 with four gly- colysis rate-limiting enzymes (PKM, PFKFB3, HK1 and HK2) were analyzed using Western blot. The experimental procedures were per- formed similar to previous study [31]. Briefly, transfected cells were lysed by RIPA buffer (Beyotime, China). After centrifugation, the supernatant was collected. Protein concentra- tion was measured by BCA kit (Phygene Life Sciences Company, Fuzhou, China). Sample proteins were separated by 10% SDS-PAGE. After electrophoresis, protein samples were transferred to PVDF membranes (BestBio, Shanghai, China). The PVDF membranes were blocked by 5% skim milk at 37℃ for 2 h. After washing by TBST buffer (BIOSIC, Nanjing, China) for three times, the membranes were incubat-
ed with the primary antibody (Abcam, UK) over- night at 4℃ and were incubated with the sec- ondary antibody (Abcam, UK) for 1 h at room temperature, respectively. Protein blots were exposure using ECL reagent (Abcam, UK) and detected by BioRad imaging system (BioRad, USA).
Colony formation assay
ACC cells at a density of 5 x 103 cells/well were seeded into six-well plates. When colonies were visible, they were washed using PBS, fixed with 4% paraformaldehyde, and were stained using Giemsa. Colonies were counted using a micro- scope at 20-fold magnification, with five ran- dom fields.
Transwell migration and invasion assays
For these assays, 24-well transwell chambers (Corning, NY, USA) were used. The experiment was conducted as described previously [5]. For transwell migration assays, DMEM/F12 or DMEM medium containing 0.1% FBS was added to the upper chambers, and medium containing 10% FBS was added to the lower chambers. After incubation for 24 h, migrated cells that adhered to the lower surface of the membrane were fixed by paraformaldehyde for 20 min and were stained with 0.1% crystal violet for 20 min. Cells in five random visual fields were counted at 20-fold magnification. When conducting the invasion assays, the upper chambers were precoated with Matrigel (Corning).
Immunofluorescence
We used 4-mm tissue sections of ACC clinical tissues for immunofluorescence assays as described previously [32]. Through immunoflu- orescence staining, the nucleus, CD8/CD163, and METTL1 were stained blue, red, and green respectively. The slides were analyzed using an automatic fluorescent microscope with a 40 x objective lens (Olympus BX53, Olympus, Tokyo, Japan).
Xenograft assay
We used six-weeks-old female BALB/c nude mice to conduct the tumor xenograft experi- ments. H295R cells that were stably transfect- ed with sh-METTL1 and sh-vector were injected
subcutaneously into the flanks of each mouse. The injection concentration and volume were 5 x 106 cells/mL and 100 uL, respectively. The tumor volume was calculated as 0.5 x tumor length x (tumor width)2. Tumor length and width were measured using a Vernier caliper every three days. After two weeks, all mice were killed, and xenograft tumors were collected. mRNA levels of METTL1 and P53 in xenograft tumors were evaluated by qPCR. This experi- ment was approved by the ethics committee of the Second Affiliated Hospital of Xi’an Jiaotong University.
Statistical analyses
All statistical analyses were performed using R software (version 4.1.2) and GraphPad Prism (version 8.0). Unpaired t-tests were used to test differences in continuous variables be- tween multiple experimental groups. Kolmo- gorov-Smirnov tests were used to assess the relationships between m7G risk scores and the clinicopathological characteristics of ACC. Survival analyses were based on the Kaplan- Meier method. Cell experiments were con- ducted using three independent replicates. Statistical significance is reported at P < 0.05.
Results
m7G is an important RNA modification
A flowchart of this study is shown in Figure 1. We established a reliable m7G-related gene set, by which a novel m7G risk signature was established. We then assessed its various roles in ACC clinical assessment and treat- ment. The main mechanism of m7G process is visualized in Figure 2A. m7G is most frequently located at position 46 in the tRNA variable region, termed G46 [8]. This methylation pro- cess is driven and catalyzed by the m7G func- tional complex that consists of two subunits, namely METTL1 and WDR4 [33]. The former exhibits methyltransferase activity, while the latter provides the molecular scaffolds for methylation reaction [8]. m7G can ultimately result in improving the stability of various modi- fied RNAs, including tRNA, mRNA, rRNA, and miRNA, which is strikingly different from m6A modification [9]. Further, m7G profoundly affects cancer progression, immune response, and drug resistance through modifying the
m7G and METTL1 in ACC
m7G related genes(n=34)
Validation cohort GSE19750 (n=22)
DEGs (n=16)
Prognositc genes (n=19)
Lasso Regression Analysis
m7G risk signature
Prognostic value
Immune effects
Metabolic reprogramming
Mutational information
Therapeutic correlation
PCA
Immune Cell abundance
Glycolysis
Mutation frequency
ICB
Survival difference
Amino acid metabolism
Mitotane
ESTIMATE score
Mutation pattern
Radiotherapy
ROC and DCA
Tumor purity
fatty acid metabolism
TMB
Independent factor
Immune pathway
Nucleotide metabolism
Mutational genes
SCNA
Clinical subgroups
mIHC
Expression
qPCR
Nomogram
Immune correlation
Immunohistochemistry
m7G risk signature
METTLI
Proliferation
Colony formation
Migration
Transwell assay
Invasion
Transwell assay
expressive status of pivotal regulatory genes [9, 10, 34].
Based on the regulatory mechanisms of m7G, we identified 34 core m7G-related genes from the MSigDB database. A PPI network of these m7G genes is shown in Figure 2B. Next, the hub module in m7G PPI network was identified (Figure 2C), in which METTL1 and WDR4 were included. Through biological function analyses, these genes were shown to be closely involved in tRNA methylation, RNA decapping, and the regulation of translation, which confirmed the reliability of our m7G gene set (Figure 2D).
A novel m7G risk signature for ACC assess- ment
Nearly half of m7G regulatory genes (16/34, 47.06%) were differentially expressed in ACC samples, compared to normal samples (Figure
3A). Up to 55.9% of the m7G genes were able to affect the prognosis of ACC (Figure 3B), and most of them were associated with unfavorable survival outcomes. Eight intersection genes were used for the Lasso regression analysis (Figure 3C), and a novel m7G risk signature was constructed as follows (Figure 3D-F): m7G risk score = 0.496 * (METTL1 relative expres- sion) + 0.714 * (NCBP1 relative expression) + 0.863 * (NUDT1 relative expression) + 0.576 * (NUDT5 relative expression). According to the optimal cutoff value of the m7G risk score (8.27), ACC patients in the TCGA cohort were assigned to high- and low-risk groups. PCA result indicated that the m7G risk score explained approximately 70% of the prognostic variance, confirming the capacity of our m7G model to stratify prognostic risks (Figure 3G). Further, patients with a high m7G risk were more likely to be in the late clinical, M, and T stages (Figure 3H).
A
B
tRNA
5’
3’
D-arm
T-arm
G46
CH3
m7G
METTL1
H2N
WDR4
Improve the stability
tRNA
RNA
mRNA
rRNA
miRNA
Cancer progression
Immune response
Drug resistance
+
C
D
NUDT1
CYFIP1
GO:0034655: nucleobase-containing compound catabolic process
GO:0006417: regulation of translation
NCBP2
R-HSA-8953854: Metabolism of RNA
METTL1
NCBP1
GO:0110154: RNA decapping
GO:0051030: snRNA transport
R-HSA-1169408: ISG15 antiviral mechanism
GO:0006446: regulation of translational initiation
EIF4E
WDR4
EIF4E
R-HSA-2393930: Phosphate bond hydrolysis by NUDT proteins
GO:0030488: tRNA methylation
hsa01521: EGFR tyrosine kinase inhibitor resistance
GO:1901655: cellular response to ketone
AGO2
EIF4E3
0.0
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
-log10(P)
m7G risk signature presents considerable prognostic value
The risk plots of the m7G signature are shown in Supplementary Figure 1. The proportion of death events in the high-risk group were sub- stantially higher than that in the low-risk group. Similarly, high m7G risk was associated with poor survival outcomes (HR = 12.78, P < 0.001; Figure 4A). With regard to prediction accuracy, the m7G risk score was the best indi- cator (AUC = 0.876) for prognostic assess- ment, compared to other traditional clinico- pathological characteristics of ACC (Figure 4B). Further, the m7G risk signature had the highest predictive accuracy for the three-year OSR of ACC patients (AUC = 0.953; Figure 4D). More importantly, applying m7G risk score to the prognostic model based on clinical stage great- ly increased its net benefit when making clini- cal decisions (Figure 4C). Further, combining the clinical stage and the m7G risk score also
improved previous predictive accuracy (AUC = 0.891; Figure 4E). Although clinical stage, T stage, M stage, and m7G risk score were asso- ciated with ACC prognosis (Figure 4F), only the m7G risk score was an independent progno- stic factor of ACC (HR = 4.103; Figure 4G). To determine the applicable scope of m7G risk sig- nature, we conducted clinical subgroup analy- ses. The results showed that m7G risk signa- ture could distinguish the survival differences of patients with each stage of ACC disease (Figure 4H-M). For the sake of clinical practice, we constructed a nomogram consisting of TNM-staging and m7G risk score to predict the 2, 3, 5-year survival rates of ACC patients (Figure 5A). The calibration plots indicated that the predicted probabilities were close to the actual survival rates (Figure 5B-D). Taken together, these results confirmed that the m7G risk signature is highly promising for prognostic assessment of ACC.
m7G and METTL1 in ACC
A
1
LSM1
0
5
10
15
20
C
m7G DEGs
Prognostic genes
D
E
Hazard ratio
77777
5
5 555
555
5554
4 4 4 3
33
20
332
7
5
5
4
Partial Likelihood Deviance
1.0
9.5
0.8
Coefficients
8
8
11
9.0
0.6
0.4-
8.5
0.2
0.0
8.0
-5
-4
-3
-2
-5
-4
-3
-2
Log(2)
Log(2)
| Type EIF4E | 4 | Type N | B | pvalue | Hazard ratio | |
|---|---|---|---|---|---|---|
| 2 | T | METTL1 | 0.004 | 2.606(1.361-4.991) | ||
| EIF4A1 | WDR4 | <0.001 | 6.176(2.918-13.074) | |||
| NUDT4B | 0 | AGO2 | <0.001 | 6.810(3.072-15.094) | ||
| CYFIP1 | 0.037 | 2.057(1.045-4.053) | ||||
| NUDT1 | -2 | DCPS | <0.001 | 5.898(2.613-13.313) | ||
| NUDT16 | EIF3D | 0.025 | 1.863(1.081-3.211) | |||
| IFITS | -4 | EIF4A1 | <0.001 | 7.541(2.522-22.554) | ||
| EIF4E2 | <0.001 | 4.559(2.514-8.265) | ||||
| NCBP1 | LSM1 | 0.013 | 2.493(1.208-5.144) | |||
| NUDTS | NCBP1 | <0.001 | 4.381(2.255-8.511) | |||
| METTL1 | NCBP2 | 0.039 | 2.205(1.042-4.667) | |||
| NCBP2L | 0.001 | 0.124(0.035-0.440) | ||||
| NSUN2 | NCBP3 | 0.027 | 3.648(1.161-11.462) | |||
| GEMIN5 | DCP2 | <0.001 | 3.342(1.648-6.780) | |||
| NCBP2 | NUDT1 | <0.001 | 3.532(2.128-5.864) | |||
| NUDT3 | 0.009 | 3.510(1.373-8.976) | ||||
| LARP1 | NUDT4 | 0.040 | 0.558(0.320-0.972) | |||
| NUDT16L1 | NUDT5 | <0.001 | 3.862(2.037-7.323) | |||
| EIF3D | NUDT7 | <0.001 | 0.402(0.254-0.636) |
F
H
1.0
0.863
0.8
Coefficients
0.714
m7G Risk
Status
Stage
M
N
T
0.6
0.576
0.496
00000 00000
0.4
High (n = 28)
0.2
0.0
METTL1 NCBP1 NUDT1 NUDT5
G
3
2
PC2 (22.9%)
Low (n = 49)
1
Group
0
S Low-Risk
C High-Risk
- 1
p = 3.3e-08 p = 0.00075 p = 0.00028 p =0.28 p=
-2
p = 0.00055
-3
-4
-2
0
2
Alive Dead
II
IV
MO
M1
NO
N1
T1
T2
T3
T4
PC1 (46.3%)
A
B
TCGA-ACC cohort
C
m7G risk score
0.06
1.0
1.0
Model A
Low
Survival probability
High
Improved model A
0.8
0.8
0.04
All positive
0.6
Sensitivity (TPR)
Net Benefit
All negative
0.6
0.02
0.4
0.2
0.4
Gender (AUC = 0.506)
0.00
HR = 12.78 (4.99-32.77)
0.0
P < 0.001
Clinical stage (AUC = 0.787)
T (AUC =0.791)
0
2.5
5
7.5
10
12.5
0.2
M (AUC = 0.701)
-0.02
Overall survival time (Year)
N (AUC = 0.557)
Low
49
35
20
m7G risk score (AUC = 0.876)
8
4
2
0.0
-0.04
High
30
12
2
0
0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
0.00
0.25
0.50
0.75
1.00
Threshold Probability
D
E
F
1.0
m7G risk score
Clinical stage+m7G risk score
1.0
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.4
0.4
0.2
1-Year (AUC = 0.855)
0.2
3-Year (AUC = 0.953)
model
AUC: 0.891
0.0
5-Year (AUC = 0.847)
0.0
Cl: 0.813-0.970
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
0
5
10
G
H
Clinical stage I-II
Clinical stage III-IV
1.0
Low
1.0
Low
Survival probability
0.8
High
Survival probability
0.8
High
0.6
0.6
0.4
0.4
0.2
HR = 15.11 (2.88-79.28)
0.2
HR = 8.73 (1.98-38.55)
0.0
P = 0.001
0.0
P = 0.004
0
2.5
5
7.5
10
12.5
0
2.5
5
7.5
10
Overall survival time
(Year)
Overall survival time (Year)
Low
36
25
17
7
3
2
Low
12
9
3
1
1
High
10
4
2
0
0
High
19
0
0
0
0
2
4
6
J
T 1-2
K
T 3-4
L
MO
M
NO
1.0
Low
1.0
1
Low
1.0
Low
1.0
Low
Survival probability
0.8
High
Survival probability
0.8
High
Survival probability
0.8
High
Survival probability
0.8
High
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
HR = 19.53 (3.87-98.50)
0.2
0.2
HR = 5.05 (1.14-22.29)
HR = 12.26 (3.76-40.00)
0.2
HR = 11.76 (4.19-33.01)
0.0
P < 0.001
0.0
P = 0.032
0.0
P < 0.001
0.0
P < 0.001
0
2.5
5
7.5
10
12.5
0
1
2
3
4
5
6
0
2.5
5
7.5
10
12.5
0
2.5
5
7.5
10
12.5
Overall survival time
(Year)
Overall survival time
(Year)
Overall survival time
(Year)
Overall survival time (Year)
Low
40
29
19
8
4
2
Low
8
8
5
4
3
1
0
Low
45
31
20
8
4
2
Low
44
30
18
7
3
2
High
11
5
2
0
0
High
18
16
6
3
0
0
High
17
7
2
0
0
High 24
10
2
0
0
| Characteristics | HR (95% CI) | P value |
|---|---|---|
| Gender | 1.043(0.473-2.299) | 0.917 |
| Clinical stage | 2.902(1.844-4.569) | <0.001 |
| T | 3.363(2.098-5.393) | <0.001 |
| M | 6.038(2.664-13.683) | <0.001 |
| N | 2.058(0.774-5.472) | 0.148 |
| m7G risk score | 4.410(2.726-7.134) | <0.001 |
| Characteristics | HR (95% CI) P value | |
|---|---|---|
| Clinical stage | 1.377(0.328-5.776) | 1 F 0.662 |
| T | 2.063(0.747-5.693) N 0.162 I | |
| M | 0.290(0.057-1.485) I 0.137 | |
| m7G risk score | 4.103(2.253-7.475) 1 <0.001 | |
Figure 4. Prognostic value of the m7G risk signature. A. Overall survival difference between high- and low-m7G risk groups. B. Accuracy of m7G risk score and clinical characteristics of ACC for predicting OSR. C. The DCA results. Model A (blue line) represents the prognostic model based on clinical stage. Improved model A (red line) represents model A with m7G risk score added. D. Time-dependent accuracy of m7G risk score for predicting OSR. E. Predictive accuracy of the combination of clinical stage and m7G risk score. F, G. Identification of ACC-independent prognostic factors through Cox univariate (blue) and multivariate (red) analyses. H-M. Clinical subgroup analyses. OSR, overall survival rate.
The prognostic value of m7G risk signature in a validation cohort
Taking a further step, we tested the prognostic value of m7G risk signature using GSE19750 cohort. As expected, high m7G risk scores were
associated with an unfavorable prognosis in the GSE19750 cohort (Figure 5E). The predic- tive accuracy of the m7G risk signature in the GSE19750 cohort was approximately 0.80, which was slightly lower than that in the TCGA cohort (Figure 5F). In addition, the m7G risk
m7G and METTL1 in ACC
A
Points
0
20
40
60
80
100
B
1.00
C
x
1.0
Actual 1-Year OS(proportion)
0.95
0.9
T
T2
T3
Actual 2-Year OS(proportion)
0.8
T1
T4
0.90
M1
0.7
M
0.85
MO
0.6
N
N1
0.80
0.5
NO
0.75
0
m7G Risk
High
0.88
0.90
0.92
0.94
0.96
0.98
1.00
0.6
0.7
0.8
0.9
1.0
Low
Nomogram-Predicted Probability of 1-Year OS
Nomogram-Predicted Probability of 2-Year OS
Total Points
D
9
14
0
40
80
120
160
200
240
280
0.9
Linear Predictor
Actual 3-Year OS(proportion)
0.8
13
-3
-2
-1
0
1
2
3
m7G risk score
0.7
2-year Survival Probability
12
0.8
0.6 0.4 0.2
0.6
3-year Survival Probability
0.5
11
Spearman
0.8
0.6 0.4 0.2
5
r = 0.062
P = 0.770
5-year Survival Probability
:
10
0.8
0.6 0.4 0.2
0.5
0.6
0.7
0.8
0.9
0
500
1000
1500
2000
Nomogram-Predicted Probability of 3-Year OS
Tumor weight (g)
E
GSE19750 cohort
F
GSE19750 cohort
G
GSE19750 cohort
H
GSE19750 cohort
1.0
Low
1.0
14
*
14
ns
Survival probability
0.8
High
0.8
0.6
Sensitivity (TPR)
m7G risk score
13
m7G risk score
13
0.4
0.6
12
12
0.2
0.4
-
HR = 4.51 (1.42-14.28)
0.0
P = 0.011
11
0
5
10
15
0.2
11
Overall survival time (Year)
1-Year (AUC = 0.682)
3-Year (AUC = 0.890)
Low
11
7
4
2
0
0.0
5-Year (AUC = 0.791)
10
10
High
11
2
0
0
0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
Stage I-II
Stage III-IV
Tumor size<5cm Tumor size≥5cm
J
K
Non-Secretory function
F
ns
I
4
Grade 3-4
ns
Secretory function
Grade 1-2
10
11
12
13
14
10
11
12
13
14
m7G risk score
m7G risk score
Figure 5. Validation of the prognostic value of m7G risk signature. A. Nomogram consisting of TNM-staging and m7G risk levels. B-D. Calibration plots for evaluating the predicting accuracy of m7G nomogram. E. Survival difference between high- and low-m7G risk groups in the GSE19750 cohort. F. Time-dependent predictive accuracy of m7G risk scores in the GSE19750 cohort. G. Difference in m7G risk scores between ACC patients at clinical stages I-II and III-IV. H. Difference in m7G risk scores between tumor sizes. I. Correlation of tumor size and m7G risk score. J. Relationship between ACC secretory function and m7G risk score. K. Relationship between histological grade and m7G risk score. * P < 0.05; NS, not statistically significant.
scores in patients at clinical stage III-IV were markedly higher than those of patients at clini- cal stage I-II (Figure 5G). Nevertheless, the m7G risk score was not correlated with tumor size, secretory function, and histological grade (Figure 5H-K).
High m7G risk implies the suppression of anti- tumor immune responses
The abundances of 21 immune subtypes in each ACC sample were variable (Supplement- ary Figure 2). High m7G risk was associated with decreased infiltration levels of CD8 T cells, resting CD4 memory T cells, activated NK cells, M2 macrophages, and resting mast cells. By contrast, higher infiltration levels of follicular helper T cells, M0 macrophages, and eosino- phils appeared in the m7G high-risk group com- pared with the low-risk group (Figure 6A). According to previous immunological studies, the above alterations of immune abundances are commonly detrimental to the anti-tumor immune process (Table 2). Furthermore, anti- gen presentation cell functions, check-point, cytolytic activity, and type-II IFN response were suppressed in the m7G high-risk group (Figure 6B). The immune score showed similar trends as abundances of immune cells and activities of immune pathways. Stromal score, immune score, and ESTIMATE score were markedly higher in the low-risk than in the high-risk gro- up (Figure 6C). By contrast, tumor purity was significantly higher in the high-risk than in the low-risk group (Figure 6D). Taken together, as shown in an immune heatmap (Supplementary Figure 3), different m7G risk levels were as- sociated with substantially different immune microenvironments.
The m7G risk level is associated with glycolysis and nucleotide metabolism
Metabolic reprogramming is a critical hallmark of tumor biology. Especially, glycolysis, which is a less efficient form of energy supply than oxi- dative phosphorylation, can drive tumor growth
and confer tumor cells drug resistance [35]. GSEA analyses showed that glycolysis was markedly enriched in ACC samples with high m7G risk (Figure 6E, 6F), which was conducive to ACC progression from a metabolism per- spective. Moreover, ‘Biosynthetic process’, ‘Nucleotide metabolism’, and ‘DNA replication’ were also enriched in the high-risk group (Figure 6G-I). Considering that active biosyn- thesis and nucleotide metabolism promote the occurrence and progression of cancers [36], these observations confirmed the correlations between high m7G risk scores and ACC pro- gression. Interestingly, there were no differenc- es in enrichments of FA and AA metabolisms between high- and low-risk groups (Figure 6J, 6K).
To go a step further, we analyzed the expres- sive correlations between m7G risk score and four glycolysis rate-limiting enzymes (PKM, PFKFB3, HK1 and HK2) using TCGA data. As shown in Figure 7A, HK1 expression in high m7G risk group was significantly higher than that in low m7G risk group. However, HK2 pre- sented the opposite trend, HK2 expression was lower in high m7G risk group. Besides, there were no differences in PKM and PFKFB3 expressions between two risk groups. From correlation view, HK1 and PKM expressions were positively correlated with m7G risk score (Figure 7B, 7C), whereas HK2 held negative correlation (Figure 7D). PFKFB3 expression was not correlated with m7G risk score (Figure 7E). These findings revealed that m7G risk score may herald the expressive alteration of glycolysis rate-limiting enzymes, which was the possible reason for high enrichment of glycoly- sis metabolism in high m7G risk group (Figure 6E-G).
Considering the critical roles of METTL1 in m7G modification and m7G risk score, we explored the effects of METTL1 on the expressions of above glycolysis enzymes through Western blot. The results showed that only HK1 ex- pression varied with the METTL1 expression
A
The immune abundances of cells
*
0.6
**
0.5
**
*
score
0.4
ns
ns
0.3
:
High
ns
Low
*
0.2
ns
ns
ns
**
ns
ns
*
ns
ns
ns
$
ns
0.1
ns
ns
0.0
B cells naive B cells memory
Plasma cells
T cells CD8
T cells CD4 naive
T cells CD4 memory resting T cells CD4 memory activated
T cells follicular helper T cells regulatory (Tregs)
T cells gamma delta
NK cells resting
NK cells activated
Monocytes Macrophages MO
Macrophages M1
Macrophages M2
Dendritic cells resting
Dendritic cells activated
Mast cells resting Mast cells activated
Eosinophils
Neutrophils
B
The activities of Immune-related pathways
C
1.0
4000
Immune Score
0.9
ns
ns
High
ns
Low
0.8
ns
2000
score
0.7
score
0.6
0
0.5
High
0.4
Low
-2000
APC co-inhibition
APC co-stimulation
Check-point
Cytolytic activity
Inflammation-promoting
Parainflammation
T cell co-inhibition
T cell co-stimulation
Type-I IFN Response
Type-II IFN Response
StromalScore ImmuneScore ESTIMATEScore
E
Enrichment plot: HALLMARK_GLYCOLYSIS
F
Enrichment plot: GO_GLYCOLYTIC_PROCESS
G
Enrichment plot: BIOSYNTHETIC_PROCESS
D
0.5
Enrichment score (ES)
0.4
P=0.017
Enrichment score (ES)
0.5
0.4
P=0.035
Enrichment score (ES)
P<0.001
Tumor Purity
0.4
0.3
0.3
0.3
1.1
0.2
0.2
0.2
1.0
0.1
0.1
0.1
0.0
0.0
0.0
0.9
-0.1
-0.1
0.8
Ranked list metric (Signal2Noise)
Ranked list metric (Signal2Noise)
Ranked list metric (Signal2Noise)
score
0.7
I (positively comelated)
If (positively comelated)
H (positively comelated)
2
2
2
4
0.6
Zero eress at 21855
4
Zero eress at 21855
1
Zero cross at 29855
0
C
D
0.5
1
‘L’ (negatively correlated)
‘L’ (negatively correlated)
L’ (negatively comelated)
0
10.000
20.000
30.000
40.000
50.000
0
10.000
20.000
30.000
40.000
50.000
0
10,000
20,000
30,000
40,000
50,000
T
T
Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Dataset
High-Risk
Low-Risk
Enrichment profile
Hits
Ranking metric scores
Enrichment profile
- Hits
Ranking metric scores
Enrichment profile - Hits
Ranking metric scores
H
Enrichment plot: WP_NUCLEOTIDE_METABOLISM
Enrichment plot: KEGG_DNA_REPLICATION
J
Enrichment plot: HALLMARK_FATTY_ACID_METABOLISM
K
Enrichment plot:
0.8
0.9
Enrichment score (ES)
AMINO_ACID_AND_DERIVATIVE_METABOLIC_PROCESS
0.7
P=0.004
Enrichment score (ES)
0.8
0.6
0.7
P<0.001
Enrichment score (ES)
0.3
P=0.439
Enrichment score (ES)
0.3
0.6
P=0.227
0.5
0.5
0.2
0.2
0.4
0.4
0.3
0.3
0.1
0.1
0.2
0.2
0.0
0.0
0.1
0.1
-0.1
0.0
0.0
-0.1
0.2
0.2
Ranked list metric (Signal2Noise)
Ranked list metric (Signal2Noise)
Ranked list metric (Signal2Noise)
Ranked list metric (Signal2Noise)
H (positively comelated)
H (positively comelated)
2
2
If (positively comelated)
2
H’ (positively cotrelated)
2
1
Zero oross at 20855
1
Zero oross at 20856
1
Zero eross at 20855
1
Zero oross at 20855
0
0
0
0
1
“L’ (negatively correlated)
“L’ (negatively comelated)
1
‘L’ (negatively correlated)
“L’ (negatively comrelated)
0
10.000
20.000
30.000
40.000
50.000
0
10.000
20.000
30.000
40.000
50.000
0
10,000
20,000
30,000
40,000
50,000
1
0
10,000
20,000
30.000
40,000
60.000
Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Dataset
Enrichment profile - Hits
Ranking metric scores
Enrichment profile Hits
Ranking metric scores
Enrichment profile - Hits
Ranking metric scores
Enrichment profile Hits
Ranking metric scores
m7G and METTL1 in ACC
| Immune cells | Changing trend | Basic function | Final effect on anti-tumor immune |
|---|---|---|---|
| T cells CD8 | Decreased | CD8+ T cells can eradicate tumor cells by recognizing tumor-associated antigens. | Unfavorable |
| T cells CD4 memory resting | Decreased | Memory CD4 T cells can rapidly enhance anti- tumour activity of CTLS. | Unfavorable |
| T cells follicular helper | Increased | TFH cells can secrete immune-protective factors but are exclusive with cytotoxic process. | Uncertain |
| NK cells activated | Decreased | NK cells can kill tumor cells by cytotoxicity and producing IFN-y. | Unfavorable |
| Macrophages M0 | Increased | Infiltration of macrophages in solid tumors is associated with poor prognosis and correlates with chemotherapy resistance in most cancers. | Unfavorable |
| Macrophages M2 | Decreased | Macrophages M2 promote tumor growth by inhibiting the functions of CD8+ T cells. | Beneficial |
| Mast cells resting | Decreased | Mast cells exert the pro-oncogenic roles through releasing angiogenic factors, such as VEGF. | Beneficial |
| Eosinophils | Increased | Eosinophils can secrete pro-angiogenic and unique granule proteins, the latter factors possess anti-tumor capacities. | Uncertain |
m7G, N7-methylguanosine; ACC, adrenocortical carcinoma; CTLs, cytotoxic T lymphocytes; TFH, T cells follicular helper; NK, natural killer; IFN, interferon; VEGF, vascular endothelial growth factor.
A
TCGA cohort
B
C
Low
10
10
Relative expression
6
High
9
9
m7G risk score
m7G risk score
8
8
4
7
7
2
6
6
Spearman
5
R= 0.317
Spearman
P= 0.005
5
R = - 0.276
0
P= 0.014
PKM
HK1
HK2
PFKFB3
2
3
4
5
0
HK2 relative expression
2
4
6
HK1 relative expression
D
E
F
Control
OE-METTL1
sh-METTL1
10
10
H295R
9
9
m7G risk score
m7G risk score
HK1
102kDa
8
8
HK2
102kDa
7
7
PKM
58kDa
6
6
Spearman
Spearman
5
R= 0.238
P= 0.035
5
R = 0.206
PFKFB3
60kDa
P= 0.068
6
7
8
1
2
3
4
5
PKM relative expression
PFKFB3 relative expression
GAPDH
36kDa
change, but no expressive alterations of PKM, HK2 and PFKFB3 were observed (Figure 7F).
METTL1 overexpression could increase HK1 expression, whereas its deletion decreased
HK1 expression. Altogether, high m7G risk was associated with active glycolysis metabolism (Figure 6E-G) and METTL1, the core member in m7G risk signature, could affect the expression of glycolysis rate-limiting enzyme HK1 (Figure 7F).
High m7G risk is related to adverse genetic alterations
Somatic mutations were common in ACC sam- ples. Missense mutation was the most fre- quent mutational form (Figure 8A), and single nucleotide polymorphism (SNP) was also the dominant variant type (Figure 8B). Meanwhile, C>T (n = 3,758) and C>A (n = 3,220) substitu- tions were the major types of SNPs (Figure 8C). The mean variant of each ACC sample was as high as 21.5 (Figure 8D). Moreover, the mutations of TTN, TP53, MUC4, MUC16, and CTNNB1 frequently occurred in ACC samples (Figure 8E). Different m7G risk levels displayed different mutational characteristics (Figure 8F, 8G). The total mutation frequency in the high-risk group was up to 83.33%, whereas that in the low-risk group was only 38.74%. Moreover, the frequencies of characteristic mutated genes in the high-risk group were substantially higher than those in the low-risk group, such as TP53, CTNNB1, and MUC4. These findings indicated that high m7G risk was associated with adverse genetic mutations of ACC. Nonetheless, the somatic mutations of m7G signature genes were rarely visible in ACC samples. METTL1 exhibited the highest muta- tion frequency at 7% (Figure 8H).
m7G risk scores may serve as biomarkers of the efficacy of ICBs and mitotane treatments
No difference in m7G risk scores between patients responding and not responding to radiotherapy was observed (Figure 9A). With respect to the mitotane treatment, the most commonly used adjuvant option for ACC, m7G risk score in drug-sensitive patients was signifi- cantly higher than that in drug-resistant patients (Figure 9B).
We investigated potential linkages between m7G risk scores and ICB efficacy. TMB was markedly higher in the high-risk than in the low- risk group (Figure 9C). The TMB value was also positively correlated with m7G risk score (r = 0.561, P < 0.001; Figure 9D). High TMB is com-
monly accompanied by high production of tumor neoantigens, suggesting a good res- ponse for ICBs [17]. TIDE scores were lower in the high-risk than in the low-risk group (Figure 9E). Patients with low m7G risk were more sus- ceptible to suffer from T cell dysfunction (Figure 9E). These findings also suggested that high m7G risk may indicate good responses to ICBs. The high-risk group showed higher expres- sion of CD274 (PD-L1) than the low-risk group (Figure 9F). LAG3 expression was positively correlated with m7G risk score (r = 0.272, P = 0.015; Figure 9K). However, the expression of other ICs was not associated with m7G risk score (Figure 9G-J, 9L). Further, the IMvigor 210 cohort revealed that m7G risk scores were higher in CR/PR than in SD/PD patients (Figure 9M). The ORR in the high-risk group was 31.9%, which was also significantly higher than that in the low-risk group (15.5%; Figure 9N). Thus, a high m7G risk may indicate ICB treat- ment response.
METTL1 can promote the proliferation, migra- tion, and invasion of ACC cells
Considering that METTL1 was the most critical regulatory gene in the m7G process (Table 3), we further explored its roles in ACC progres- sion. Using 10 pairs of clinical samples, we confirmed that METTL1 was significantly upreg- ulated in ACC tissues compared to adjacent normal tissues (Figure 10A). The qPCR tests revealed that sh-METTL1 and OE-METTL1 could effectively manipulate METTL1 expres- sion (Figure 10B, 10C). Colony formation assays showed that METTL1 overexpression promoted, whereas METTL1 silencing inhibit- ed the proliferation of ACC cells (Figure 10D, 10E). The colony numbers in the OE-METTL1 group were significantly higher than that in other experimental groups; by contrast, the least colonies were observed in the sh-METTL1 group (Figure 10F, 10G). Regarding migrative and invasive abilities, upregulation METTL1 enhanced, whereas METTL1 deletion sup- pressed the migration ability of ACC cells (Figure 11A). Likewise, METTL1 stimulated invasion by ACC cells (Figure 11B). The results of quantitative analyses also were in line with these results (Figure 11C-F). Collectively, METTL1 exhibited oncogenic potential in ACC progression.
A
B
C
D
Variants per sample Median: 21.5
E
Variant Classification
Variant Type
SNV Class
Top 10 mutated genes
Missense_Mutation
TOG
1776
250
Nonsense_Mutation
SNP
TTN
12%
T>A
672
TP53
18%
Frame_Shift_Del
MUC4
14%
Splice_Site
1184
T>C
989
MUC16
14%
Frame_Shift_Ins
INS
CTNNB1
15%
In_Frame_Del
C>T
3758
PKHD1
9%
Nonstop_Mutation
592-
NF1
9%
C>G
1124
CNTNAPS
9%
In_Frame_Ins
DEL
PCDH15
8%
Translation_Start_Site
C>A
3220
ASXL3
8%
0
0
1000
2000
3000
4000
5000
6000
0
1000
2000
3000
4000
5000
6000
7000
0.00
0.25
0.50
0.75
1.00
0
7
=
&
F
Altered in 25 (83.33%) of 30 samples.
G
Altered in 19 (38.78%) of 49 samples.
943
290
TMB
TMB
0
No. of samples
9
0
6
0
0
No. of samples
TP53
30%
TP53
8%
CTNNB1
27%
CTNNB1
10%
MUC16
17%
MUC16
12%
MUC4
30%
MUC4
6%
TTN
20%
TTN
6%
PKHD1
17%
PKHD1
4%
CNTNAP5
17%
CNTNAP5
4%
ASXL3
13%
ASXL3
2%
DST
13%
DST
2%
HMCN1
10%
HMCN1
8%
Risk
Risk
Missense_Mutation
Frame_Shift_Del
Risk
Nonsense_Mutation
Risk
Frame_Shift_Ins
Nonsense_Mutation
Missense_Mutation
In_Frame_Del
· Frame_Shift_Del
· Multi_Hit
high
· Multi_Hit
high
low
low
H
Study of origin
Mutation spectrum
METTL1
7%*
NCBP1
3%*
NUDT1
3%*
NUDT5
3%*
Genetic Alteration
Inframe Mutation (unknown significance)
Amplification
Deep Deletion
No alterations
Not profiled
Study of origin
Adrenocortical Carcinoma (TCGA, Firehose Legacy)
Adrenocortical Carcinoma (TCGA, PanCancer Atlas)
Mutation spectrum
C>A
C>G
C>T
T>A
T>C
T>G
No data
m7G and METTL1 in ACC
A
Radiation Therapy
B
Mitotane therapy
C
Tumor mutation burden
D
ns
10
10
25
…
25
Spearman
r = 0.561
9
9
20
20
P < 0.001
m7G risk score
m7G risk score
8
8
TMB value
15
15
TMB
7
7
10
10
6
5
6
5
0
5
5
0
-5
Non-response
Response
Non-response
Response
High-Risk
Low-Risk
6
8
10
m7G risk score
E
TIDE analysis
F
ns
15
ns
1.0
Expression
10
score
0.5
High
High
Low
Low
5
0.0
ns
ns
I
ns
0
I
I
I
±
I
-0.5
TIDE
Dysfunction
Exclusion
CD274 CTLA4 BTLA HAVCR2 TIGIT LAG3
G
H
I
J
12
10
10
10
10
9
9
9
m7G risk score
m7G risk score
m7G risk score
m7G risk score
8
8
8
8
7
7
7
6
Spearman
6
r = - 0.157
Spearman
6
Spearman
6
Spearman
P = 0.166
5
r =- 0.155
P = 0.174
5
r = 0.110
P = 0.334
5
r = 0.041
P = 0.717
4
0.0
2.5
5.0
7.5
10.0
0.0
0.5
1.0
1.5
2.0
0.0
0.1
0.2
0.3
0
5
10
CD274 expression
CTLA4 expression
BTLA expression
HAVCR2 expression
M
N
K
L
IMvigor 210 cohort
IMvigor 210 cohort
1.0
11
6.0
…
31.9%
15.5%
12
10
0.8
m7G risk score
5.5
9
Proportion
m7G risk score
10
m7G risk score
0.6
CR/PR
8
5.0
SD/PD
8
0.4
7
P=0.027
4.5
6
Spearman
0.2
r = 0.272
6
Spearman
P = 0.015
5
r =- 0.176
4.0
0.0
P = 0.121
4
CR/PR
SD/PD
High-risk
Low-risk
0
5
10
15
0.0
0.5
1.0
1.5
2.0
LAG3 expression
TIGIT expression
| m7G Signature gene | Study | Cancer type | Main function |
|---|---|---|---|
| METTL1 | PMID: 34371184 | LC, ICC, HCC | Promote cancer progression |
| PMID: 34352206 | |||
| PMID: 34898034 | |||
| NCBP1 | PMID: 31448526 | LUAD | Promote cancer progression |
| NUDT1 | PMID: 29075149 | GC, LUAD | Promote cancer progression |
| PMID: 21289483 | |||
| NUDT5 | PMID: 33096144 PMID: 35247377 | NSCLC, GC | Promote cancer progression |
m7G, N7-methylguanosine; LC, lung cancer; ICC, intrahepatic cholangiocarcinoma; HCC, hepatocellular carcinoma; LUAD, lung adenocarcinoma; GC, gastric cancer; NSCLC, non-small-cell lung cancer.
A
B
C
6
The relative expression of METTL1
5
H295R
4
SW13
4
Relative METTL1
4
3
expression
Relative METTL1 expression
3
2
2
2
1
1
**
0
0
0
Adjacent normal Tumor
Blank
OE-Vector
OE-METTL1
sh-Vector
sh-METTL1
Blank
OE-Vector
OE-METTL1
sh-Vector
sh-METTL1
D
Blank
OE-vector
OE-METTL1
sh-vector
sh-METTL1
H295R
E
SW13
F
600
H295R
G
800
SW13
Colony numbers
Colony numbers
400
600
400
200
200
0
0
Blank
OE-vector
OE-METTLI
sh-vector
sh-METTL1
Blank
OE-vector
OE-METTLI
sh-vector
sh-METTL1
A
Blank
OE-vector
OE-METTL1
sh-vector
sh-METTL1
20%
20x
20元
20x
H295R
Migration
50pm
Dum
50um
50um
50pm
20
20x
20x
SW13
50pm
50pm
B
Blank
OE-vector
OE-METTL1
sh-vector
sh-METTL1
20
20%
20
H295R
Invasion
50jim
4
50pm 2
4
Dum
50
20-
20x
20x
20x
20x
SW13
50pm
50pm
50pm
50um
50um
C
400
H295R
D
300
SW13
E
250
H295R
=
F
250
SW13
=
200
200-
Migrative cell numbers
300-
Migrative cell
numbers
200
Invasive cell
Invasive cell
T
numbers
150
numbers
150
200-
T
100-
100
100-
100-
=
50
50
0
0
0
0
Blank
OE-vector
OE-METTLI
sh-vector
sh-METTLI
Blank
OE-vector
OE-METTLI
sh-vector
sh-METTLI
Blank
OE-vector
OE-METTLI
sh-vector
sh-METTL1
Blank
OE-vector
OE-METTLI
sh-vector
sh-METTL1
METTL1 expression affects the infiltration lev- els of CD8+ T cells and macrophages in ACC tissues
ssGSEA results revealed that as the core mem- ber of the m7G risk signature, METTL1 expres- sion was negatively correlated with the infiltrat- ing levels of CD8+ T cells (Figure 12A), and it was positively correlated with that of macro- phages (Figure 12B). Further, the somatic copy number alteration (SCNA) of METTL1 was also associated with the infiltration levels of CD8+ T cells and macrophages (Figure 12C). Arm-level deletion of METTL1 was accompa- nied by the increased abundance of CD8+ T cells and decreased abundance of macro- phages (Figure 12C).
We then confirmed the effects of METTL1 on infiltration levels of immune cells through
immunofluorescence assays. ACC sample with high-expression METTL1 showed very low fluo- rescence intensity of CD8+ T cells (red), but conspicuous that of METTL1 (green). In con- trast, ACC samples with low-expression METTL1 presented high fluorescence intensity of CD8+ T cells (red; Figure 12D). Regarding macrophages, the clinical samples exhibited the opposite trend to the fluorescence inten- sity of CD8+ T cells. The fluorescence intensi- ties of macrophages (CD163, red) and METTL1 (green) were both strong in ACC samples with high METTL1 expression, whereas in ACC samples with low METTL1 expression, they were weak (Figure 12E). Hence, METTL1 did not only stimulate the malignant behaviors of ACC cells but also affected the infiltration lev- els of CD8+ T cells and macrophages in ACC tissues.
m7G and METTL1 in ACC
A
B
C
ACC
0.62
0.62
0.5
**
*
Enrichment of CD8+ T cells
Enrichment of Macrophages
0.60
0.60
Infiltration Level
0.4
Copy Number
0.58
0.58
Arm-level Deletion
0.3
Diploid/Normal
Arm-level Gain
0.56
0.56
0.2
High Amplication
0.54
Spearman
0.54
Spearman
r =- 0.264
r = 0.286
0.1
P = 0.019
P = 0.011
0.52
0.52
4.0
4.5
5.0
5.5
6.0
6.5
2
4
6
B Cell
CD8+ T Cell
CD4+ T Cell
Macrophage
Neutrophil
Dendritic Cell
The relative expression of METTL1
The relative expression of METTL1
D
DAPI
CD8
METTL1
Merge
40x
40x
40x
40x
High
50pm
50um
50um
50um
40x
40x
40x
40x
Low
50um
50um
50um
50um
E
DAPI
CD163
METTL1
Merge
40x
40x
40x
40x
High
50um
50um
50μm
50um
40x
40x
40x
40x
Low
50um
50um
50μm
50um
A
D
800
P=1.5e-07
sh-vector
Tumor weight (mg)
600
400
200
0
sh-vector
sh-METTL1
E
800
1
2
3
4
5
6
Tumor volume(mm3)
600
sh-vector
sh-METTLI
B
400
**
sh-METTL1
200
**
0
0
5
10
15
20
Days
F
Relative METTL1 expression
1.5
1.0
1
2
3
4
5
6
C
0.5
sh-vector
0.0
sh-vector sh-METTL1
G
4
sh-METTL1
Relative P53 expression
3
1
2
3
4
5
6
2
5
7
8
9
10
11
12
13
14
15
1
0
T
sh-vector
sh-METTL1
Silencing METTL1 suppresses tumor growth in a xenograft model
Visually, the tumor burden of nude mice in the sh-METTL1 group was lower than that in the negative control group (Figure 13A, 13B). After the mice were sacrificed, we confirmed that silencing METTL1 indeed suppressed xeno-
graft tumor growth (Figure 13C). Tumor weight in the sh-METTL1 group was significantly lower than that in the sh-vector group (Figure 13D), and tumor volume exhibited the same trend (Figure 13E). qPCR revealed that METTL1 expressions of xenograft tumors in the sh-MET- TL1 group were significantly lower than that in the sh-vector group (Figure 13F). However, the
mRNA levels of P53, a classical tumor suppres- sor gene, were substantially higher in the sh- METTL1 than in the sh-vector group (Figure 13G). These results highlighted that METTL1 deletion decelerated ACC growth and increased P53 expression.
Potential regulatory mechanisms of METTL1 in ACC progression
Using TargetScanHuman, miRDB and ENCORI databases, we predicted potential upstream miRNAs of METTL1. The intersection part of three databases was obtained through a Venn diagram, miR-1277-3p and miR-885-5p were screened as the candidate regulators (Figure 14A). Next, the minimum free energy (MFE) of these miRNAs was quantified via RNAhybrid database. MEF of miR-1277-3p and miR-885- 5p was -15.5 and -27.4 kcal/mol, indicating the latter was more accessible to bind to METTL1 (Figure 14B, 14C). The binding site between miR-885-5p and METTL1 was also predicted with the aid of TargetScanHuman database. As shown in Figure 14D, miR-885- 5p may target the 3’-UTR region of METTL1 namely 5’-GUAAUGGA-3’.
Furthermore, the potential transcription factor (TF) of METTL1 was also investigated. CEBPB was speculated as the upstream TF of METTL1 based the intersection of hTFtarget, Gene- cards and ALGGEN databases (Figure 14E). The motif sequence of CEBPB exhibited the specificity and conservation of its binding site (Figure 14F). Theoretically, the promoter sequence with the highest binding probability with CEBPB was 5’-TATTGCACAAT-3’ (Figure 14F). Using JASPAR database, we predicted the binding site between CEBPB and METTL1 (Figure 14G). the most probable site was locat- ed between the 479th and 489th bases upstream of the METT1 transcription starting site (TSS), and the sequence was 5’-CGTTT- CACCAT-3’ (Figure 14H). Collectively, miR-885- 5p and CEBPB may participate ACC progres- sion through regulating METTL1.
Discussion
ACC is a rare urological carcinoma with an inci- dence of 0.7-2.0/million [37]. Due to the high degree of malignancy and early metastases, the five-year OSR of ACC patients is commonly less than 20%. To maximize the patients’ sur-
vival outcomes, multiple therapies such as molecular target treatment (MTT) and ICBs were explored for use in ACC treatment. Nonetheless, MTT produces only negligible results [38], and selecting suitable cases for ICBs is a persistent problem. By contrast to m6A, m7G has not received sufficient attention although it may exert important functions dur- ing cancer regulation and treatment [39-41].
Prognostic evaluation is vital for deciding on therapeutic strategies, however, traditional clinicopathological indicators of ACC do not allow for accurate prognosis. TNM-staging, clin- ical stage, Ki-67, and histological grade can be used for stratifying patient survival outcomes, however, up to 25% of patients experience a different outcome than predicted [42]. Hence, better indicators are required to compensate for the deficiency of current prognostic meth- ods. In the present study, we established a novel m7G risk signature, and the m7G risk score showed outstanding predictive accuracy for OSR and was identified as the only indepen- dent ACC-prognostic factor. More importantly, m7G risk score could improve the predictive accuracy and making-decision benefit of tradi- tional AJCC-Stage prognostic system (Figure 4C, 4E). These findings assured us that m7G risk score possessed highly prognostic value.
RNA methylation profoundly affects the anti- cancer immune response and the efficacy of immunotherapy [9, 43]. For instance, methyl- transferases METTL3/14 can enhance the response to anti-PD-1 treatment in colorectal cancer (CRC) and melanoma [44]. The activa- tion of retinoic acid-inducible gene-I (RIG-I) which is an innate immune receptor and is responsible for triggering type-I IFN response, relies on m7G recognition [45]. Moreover, METTL1/WDR4-mediated tRNA m7G can af- fect the immune landscape of head and neck squamous cell carcinoma (HNSCC) by altering the proportion of CD8+ T cells, NK cells, and CD4+ T cells [33]. In the current study, we also confirmed that the m7G risk score was strongly correlated to the immune microenvironment of ACC. High m7G risk significantly suppressed the immune enrichment of CD8+ T cells and NK cells, but it stimulated that of macrophages and TFH. Regarding the most potent anti-tumor immune cells, the functions of CD8+ T cells and NK cells in eradicating tumor cells did not need
A
B
TargetScanHuman
RNAhybrid
Target: METTL1(NM005371.6)
127
Length: 1378
5’
MiRNA: miR-1277-3p
37
6
2
Length: 22
MFE: - 15.5kcal/mol
6
1
23
C
5’
miRDB
ENCORI
Target: METTL1(NM005371.6)
Length: 1378
MiRNA: miR-885-5p
miR-1277-3p miR-885-5p
Length: 22
MFE: - 27.4kcal/mol
D
E
hTFtarget
F
2.0
ATT&CASAAz
44
1.5
20
0
1
0.5
239
2
20
0.0
1
2
3
4
5
6
7
8
9
10
11
H
GeneCards
ALGGEN
5’-CGTTTCACCAT-3’
Predictive Binding Site
CEBPB
-2000
-489 ~ - 479
+1
a JASPAR
METTL1 TSS
| 0 TargetScanHuman Prediction of microRNA forgets | Predicted consequential pairing of target region (top) and miRNA (bottom) | Site type | Context++ score | Context++ score percentile | Weighted context++ score | Conserved branch length | PCT | Predicted relative Kp |
|---|---|---|---|---|---|---|---|---|
| Position 84-91 of METTL1 3' UTR hsa-miR-885-5p | 5' GGAAGAAAGUUCUACGUAAUGGA. 3' UCUCCGUCCCAUCACAUUACCU | 8mer | -0.40 | 99 | -0.40 | 0.072 | N/A | -3.286 |
G
| Name | Score | Relative score Y | Sequence ID | Start | End | Strand A | Predicted sequence 4 |
|---|---|---|---|---|---|---|---|
| MA0466.1.CEBPB | 10.499343 | 0.9299949439183495 | hg38_knownGene_ENST00000324871.12 | 479 | 489 | - | CGTTTCACCAT |
| MA0466.2.CEBPB | 5.9176693 | 0.8649728020946857 | hg38_knownGene_ENST00000324871.12 | 479 | 488 | + | ATGGTGAAAC |
| MA0466.2.CEBPB | 5.651492 | 0.861368825631875 | hg38_knownGene_ENST00000324871.12 | 44 | 53 | + | TTTGTGAAAC |
| MA0466.2.CEBPB | 5.4439955 | 0.8585593698214778 | hg38_knownGene_ENST00000324871.12 | 525 | 534 | - | CTTGCGCTAC |
| MA0466.2.CEBPB | 5.0090384 | 0.8526701538325228 | hg38_knownGene_ENST00000324871.12 | 479 | 488 | - | GTTTCACCAT |
Figure 14. Potential regulatory mechanism of METTL1 in ACC. A. The predicting intersection of three miRNA da- tabases. B, C. The minimum free energy of miR-1277-3p and miR-885-5p based on RNAhybrid database. D. The predicted binding site between miR-885-5p and 3’-UTR region of METTL1 based on TargetScanHuman database. E. The predicting intersection of three TF databases. F. The motif sequence of CEBPB. G. Five predicted binding sites between CEBPB and promoter region of METTL1 based on JASPAR database. H. CEBPB binding site with highest predictive score. MFE, minimum free energy; TSS, transcription starting site.
to further elaborate [46]. Macrophages medi- ate immunotolerance and immune evasion through releasing CCL2, CCL5, and VEGF cytokines [47, 48]. However, the roles of TFH cells in immune regulation is more complex. As TFH cells can produce CXCL13 which exerts immune-protective functions, they are strongly associated with long survival time of patients with breast cancer [49]. Nevertheless, TFH cells and cytotoxic transcriptional programs are functionally exclusive, thus TFH cells may be detrimental to anti-tumor immune and ICB ther- apy [50]. In view of these facts, the effects of m7G risk on the immune microenvironment of ACC are complicated and multifaceted.
ICBs represent a revolutionary change in can- cer treatment, however, identifying suitable cases is challenging. Currently, several bio- markers and methods have been tested to predict the efficacy of ICIs, such as TMB [51], IC expression [52], and TIDE scoring [19]. Surprisingly, the m7G risk score was associated with all the above predictive markers, which demonstrated its potential for predicting ICIs therapeutic response. Although PD-L1 expres- sion and TMB may each inform on the use of ICIs in most cancers [52], considerable con- troversy on these biomarkers remains. For instance, low TMB does not preclude respons- es to ICIs, especially in patients with Kaposi sarcoma [53] and Merkel cell carcinoma [54]. Moreover, experimental determination of TMB requires whole exome sequencing, which is technically complicated and highly expensive, thus limiting its clinical applicability [51]. Therefore, the m7G risk score sheds new light on ICIs prediction.
The catalytic function of METTL1 is a prerequi- site of the m7G process [8]. As expected, METTL1 was a member of the m7G risk signa- ture, which was consistent with the core identi- ty of METTL1 in m7G. Recent studies confirmed its pivotal roles in cancer onset and develop- ment. For example, METTL1/WDR4-mediated m7G can promote the progression of lung can- cer [36], and METTL1 drives oncogenic trans- formation through accelerating the m7G mo-
dification of Arg-TCT tRNA [55]. The METTL1- m7G-EGFR axis facilitates the progression of bladder cancer [56]. In the present study, we confirmed the oncogenic potential of METTL1 in ACC for the first time. METTL1 overexpres- sion substantially enhanced the proliferation, migration, and invasion abilities of ACC cells. Moreover, METTL1 deletion significantly sup- pressed xenograft tumor growth. These find- ings thus confirmed its potential as a tumor therapeutic target.
Glycolysis termed ‘Warburg effect’ can satisfy the metabolic requirements of cell proliferation and regulate cancer metastasis, thus acting as a pivotal hallmark of solid cancers [57]. In the present study, we found that glycolysis enrichment was concomitant with high-m7G risk scores, indicating m7G modification may drive glycolysis metabolism. However, only HK1 was affected by METTL1 among four glycolysis rate-limiting enzymes. Tissue-specific expres- sions of these glycolytic enzymes were the pos- sible reasons. Duan K et al. have confirmed that although HK1 and PKM were both upregu- lated in ACC compared to normal adrenal corti- cal tissue and adrenal cortical adenoma (ACA), PKM expression was overall low in ACC [58]. Moreover, HK2 mainly expressed in insulin-sen- sitive tissues, such as colon and fat, but not adrenal [59]. PFKFB3 mainly expressed the cancers of brain, skeletal muscle and liver [60]. In light of these facts, PKM, HK2 and PFKFB3 may rarely express in ACC compared to HK1, leading their expressions not to be affected by METTL1.
There are some limitations to this study. First, the m7G risk signature remains to be tested in a real clinical cohort. Second, since the detec- tion of m7G modifications have a certain degree of difficulty, it is intractable to determine the specific relationships between m7G risk score and m7G modification level. Third, due to the fact that genetic mutation analysis was reliant on the whole-exome next-generation sequenc- ing, we were unable to validate mutational fea- tures caused by m7G risk score in our current experimental condition. Fourth, the specific
m7G and METTL1 in ACC
mechanisms of METTL1 in ACC progression remain experimental validation. Hence, further research is necessary.
Conclusions
Although m7G is one of the most frequent RNA modifications, its roles in ACC remain obscure. Herein, we developed a novel m7G risk signa- ture for ACC clinical assessments. m7G risk score acted a biomarker for assessing progno- sis, anti-tumor immune response, glycolysis metabolism and predicting the efficacy of ICBs and mitotane treatments. It greatly con- tributes to acquire the disease state of ACC patients, in turn advancing individualized thera- py. Moreover, we confirmed the pro-oncogenic roles of METTL1 in ACC progression, which highlighted its great potentials as a novel anti- cancer target. In conclusion, m7G, an unre- solved epigenetic aspect, is expected to advance the paradigm of ACC treatment and clinical assessment.
Acknowledgements
All authors would like to thank Second Affiliated Hospital of Xi’an Jiaotong University for its sup- port. Dr. Fangshi Xu would like to thank his wife Dr. Danrui Cai for her encourage. All authors also thank Bullet Edits Limited for the linguistic editing of the manuscript. The animal study was reviewed and approved by the Ethics Committee of the Second Affiliated Hospital of Xi’an Jiaotong University.
For METTL1 testing on tumor samples, all patients provided written informed consent.
Disclosure of conflict of interest
None.
Address correspondence to: Xueyi Li and Bin- cheng Ren, Department of Rheumatology and Immunology, Second Affiliated Hospital of Xi’an Jiaotong University, No. 157, West Five Road, Xi’an 710004, Shaanxi, China. Tel: +86-29-876793- 23; E-mail: 13992891987@139.com (XYL); ren- bincheng7@163.com (BCR)
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m7G and METTL1 in ACC
| Items | TCGA-ACC | GSE19750 |
|---|---|---|
| Sample size | 79 | 22 |
| Survival status | ||
| Dead | 27 | 18 |
| Alive | 52 | 4 |
| Age | NA | |
| <60 | / | 16 |
| ≥60 | / | 6 |
| Clinical stage | ||
| Stage I | 9 | 1 |
| Stage II | 37 | 7 |
| Stage III | 16 | 1 |
| Stage IV | 15 | 4 |
| Unknown | 2 | 9 |
| T | NA | |
| T1 | 9 | / |
| T2 | 42 | / |
| T3 | 8 | / |
| T4 | 18 | / |
| Unknown | 2 | / |
| M | NA | |
| M0 | 62 | / |
| M1 | 15 | / |
| Unknown | 2 | / |
| N | NA | |
| N0 | 68 | / |
| N1 | 9 | / |
| Unknown | 2 | / |
NA, not available.
Supplementary Table 2. The m7G-related genes
Gene symbol
n=34
METTL1
WDR4
NSUN2
AGO2
CYFIP1
CYFIP2
DCPS
EIF3D
EIF4A1
EIF4E
EIF4E1B
EIF4E2
EIF4E3
EIF4G3
GEMIN5
IFIT5
LARP1
LSM1
NCBP1
NCBP2
NCBP2L
NCBP3
SNUPN
DCP2
NUDT1
NUDT10
NUDT11
NUDT16
NUDT16L1
NUDT3
NUDT4
NUDT4B
NUDT5
NUDT7
| Gene | Sequence (5'-+3') |
|---|---|
| sh-METTL1 OE-METLL1 | CCGGGATGACCCAAAGGATAAGAAACTCGAGTTTCTTATCCTTTGGGTCATCTTTTTG |
| METTL1-XbaI-F: GCTCTAGAATGGCAGCCGAGACTCGGAACGTGGCCGG | |
| METTL1-BamHI-R: CGGGATCCTCAGTGACCAGGCAGGCTGGTTTGGG |
OE, over expression.
| Names | Gene counts | Description |
|---|---|---|
| GO glycolytic process | 106 | Fermentation that includes the anaerobic conversion of glucose to pyruvate via the glycolytic pathway. |
| Hallmark Glycolysis | 200 | Genes encoding proteins involved in glycolysis and gluconeogenesis. |
| Biosynthetic process | 470 | The energy-requiring part of metabolism in which simpler substances are transformed into more complex ones, as in growth and other biosynthetic processes. |
| WP Nucleotide Metabolism | 19 | Nucleotide metabolism. |
| KEGG DNA Replication | 36 | DNA replication. |
| Amino acid and derivative Metabolic Process | 101 | The chemical reactions and pathways involving amino acids, organic acids containing one or more amino sub- stituents, and compounds derived from amino acids. |
| Hallmark Fatty acid Metabolism | 158 | Genes encoding proteins involved in metabolism of fatty acids. |
| Gene | Primer | Sequence (5'-+3') |
|---|---|---|
| METLL1 | Forward | 5'-AGCTATACCCAGAGTTCTTCGCTCCAC-3' |
| Reverse | 5'-ACAGCCTATGTCTGCAAACTCCACT-3 | |
| TP53 | Forward | 5'-TAACAGTTCCTGCATGGGCGGC-3' |
| Reverse | 5'-AGGACAGGCACAAACACGCACC-3' | |
| GAPDH | Forward | 5'-GTCGCCAGCCGAGCCACATC-3 |
| Reverse | 5'-CCAGGCGCCCAATACGACCA-3' |
10
Risk score
8
6
12.5
Survival time
10.0
Status
7.5
· Alive
5.0
· Dead
2.5
Group
4
METTL1
2
NCBP1
0
NUDT1
-2
NUDT5
100%
Relative Percent
80%
60%
40%
20%
0%
Supplementary Figure 2. The infiltrating levels of 21 immune cells in each TCGA-ACC sample.
TCGA-OR-ASJA-01A-11R-A298-0
TCGA-OR-ASL4-01A-11R-A298-02
TCGA-OR-ASJS-01A-11R-A298-07
TCGA-OR-ASKW-01A-11R-A298-07
TCGA-OR-ASJE-01A-11R-A298-07
TCGA-OR-ASKX-01A-11R-A298-07
TOGA-OR-ASIT-01A-11R-A298-07
TCGA-P6-ASOF-01A-11R-A298-07
TCGA-OR-ASL9-01A-11R-A298-07
TCGA-OR-ASJL-01A-11R-A298-07
TCGA-OR-ASK9-01A-11R-A295-07
m7G and METTL1 in ACC
TCGA-OR-ASJV-01A-11R-A29S-07
TCGA-OR-ASKV-01A-11R-A298-07
TCGA-OR-ASJW-01A-11R-A29S-07
TCGA-OR-ASLD-01A-11R-A298-07
TCGA-OR-ASKT-01A-11R-A298-07
TCGA-PK-ASHA-01A-11R-A298-07
TCGA-OR-ASKO-01A-11R-A298-07
TCGA-OR-A5J7-01A-11R-A298-07
TCGA-PK-ASH8-01A-11R-A298-07
TCGA-OR-ASJP-01A-11R-A298-07
TCGA-OR-ASJC-01A-11R-A298-07
TCGA-OR-ASLJ-01A-11R-A298-07
TCGA-OR-ASLP-01A-11R-A298-07
TCGA-P6-ASOG-01A-22R-A298-07
TCGA-OR-ASLA-01A-11R-A298-07
TCGA-OR-ASKO-01A-11R-A298-07
TCGA-OR-ASLN-01A-11R-A298-07
TCGA-OR-ASLM-01A-11R-A298-07
TCGA-OR-ASK5-01A-11R-A298-07
TCGA-OR-ASK6-01A-11R-A298-07
TOGA-OR-ASK3-01A-11R-A298-07
TCGA-OR-ABLE-01A-11R-A298-07
TCGA-OR-A5LL-01A-11R-A298-07
TCGA-OR-A512-01A-11R-A298-07
TOGA-OR-ASJJ-01A-11R-A298-07
TCGA-OR-ASK8-01A-11R-A298-07
TCGA-PA-ASYG-01A-11R-A298-07
TOGA-OR-ASKU-01A-11R-A298-07
TCGA-OR-ASJO-01A-11R-A29S-07
TCGA-OR-ASLB-01A-11R-A298-07
TCGA-OR-ASK2-01A-11R-A298-07
TCGA-OR-A5J3-01A-11R-A298-07
TCGA-OR-ASL6-01A-11R-A298-07
TCGA-OR-ASJS-01A-11R-A298-07
TCGA-OR-ASJR-01A-11R-A298-07
TOGA-OR-ASJZ-01A-11R-A298-07
TCGA-OR-ASJO-01A-11R-A298-07
TCGA-OR-ASJO-01A-11R-A298-07
TCGA-OR-ASKY-01A-11R-A29S-07
TCGA-OR-ASKZ-01A-11R-A298-07
TCGA-OR-A5JK-01A-11R-A298-07
TCGA-OR-ASJY-01A-31R-A298-07
TCGA-OU-ASPI-01A-12R-A298-07
TCGA-OR-ASL8-01A-11R-A298-07
TOGA-OR-AS/1-01A-11R-A298-07
TCGA-OR-ASL3-01A-11R-A298-07
TOGA-OR-ASJM-01A-11R-A298-07
TCGA-OR-A5JX-01A-11R-A298-07
TOGA-OR-ASLS-01A-11R-A298-07
TOGA-PK-ASH9-01A-11R-A298-07
TCGA-OR-ASJI-01A-11R-A298-07
TCGA-PK-ASHB-01A-11R-A298-07
TCGA-OR-ASLG-01A-11R-A298-07
TCGA-OR-ASLT-01A-11R-A298-07
TOGA-OR-ASJG-01A-11R-A298-07
TCGA-OR-ASK4-01A-11R-A298-07
TCGA-OR-ASK1-01A-11R-A29S-07
Mast cells activated
Mast cells resting
Dendritic cells resting Dendritic cells activated
Macrophages M2
Macrophages M1
Macrophages MO
Monocytes
NK cells activated
NK cells resting
= T cells gamma delta
= T cells follicular helper T cells regulatory (Tregs)
= T cells CD4 memory resting T cells CD4 memory activated
T cells CD4 naive
T cells CD8
Plasma cells
B cells naive B cells memory
TCGA-OR-ASLO-01A-11R-A298-07
TCGA-OR-ASJB-01A-11R-A298-07
TCGA-OR-ASLK-01A-11R-A298-07
TCGA-OR-ASJ9-01A-11R-A298-07
TOGA-OR-ASLR-01A-11R-A298-07
TCGA-OR-ASJ6-01A-31R-A298-07
TCGA-OR-ASJF-01A-11R-A298-07
TCGA-OR-A5J8-01A-11R-A298-07
TCGA-OR-ASLC-01A-11R-A298-07
Neutrophils
Eosinophils
TCGA-OR-ASL5-01A-11R-A298-07
TCGA-OR-ASLH-01A-11R-A298-07
m7G and METTL1 in ACC
TumorPurity
TumorPurity
ESTIMATEScore
3
1
ImmuneScore
StromalScore
2
Subtype
0.5
aDCs
1
Type-II IFN Response
ESTIMATEScore
iDCs
0
3000
NK cells
Mast-cells
-1
Tfh
CD8+ T cells
-2
-2000
Inflammation-promoting
Cytolytic activity TIL
-3
ImmuneScore
2000
Check-point
T cell co-inhibition
T cell co-stimulation Th1 cells
-1000
APC co-inhibition
StromalScore
Treg
1500
CCR
Parainflammation
B cells
-1500
Neutrophils
pDCs
Subtype
T helper cells
Low
APC co-stimulation
High
HLA
Macrophages
Th2 cells
Type-I IFN Response
DCs
MHC-class I