Research
Pan-cancer analysis of the prognosis and immune infiltration of NSUN7 and its potential function in renal clear cell carcinoma
Jinwei Cui1,2 . Shiye Ruan2 . Zhongyan Zhang2 . Hailiang Wang3 . Qian Yan2 . Yubin Chen2 . Jiayu Yang2 . Jike Fang2 . Qianlong Wu4,5 . Sheng Chen1,2 . Shanzhou Huang1,2 . Chuanzhao Zhang1,2 . Baohua Hou1,2
Received: 18 October 2024 / Accepted: 5 March 2025
Published online: 18 March 2025
@ The Author(s) 2025 OPEN
Abstract
Background NSUN7, an enzyme responsible for the RNA m5c modification, has been recognized as a valuable indicator for predicting and diagnosing an array of cancer. Nevertheless, there is still a scarcity of thorough analyses exploring its diagnostic, predictive, and immune system-related importance in various types of cancer.
Methods We integrated multiple publicly available databases, including TCGA, TISIDB, TISCH2, and UALCAN, to compre- hensively investigate the role of NSUN7 in pan-cancer across various omics data types. The research included examining survival rates, genetic mutations, immune cell presence in tumors, analyzing differences in gene expression, and studying individual cells, among other things.
Results NSUN7 expression showed an increase across 12 cancer types and a decrease in another 12 types. NSUN7 was discovered to be linked with enhanced survival rates in bladder urothelial carcinoma (BLCA), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), lung adenocarcinoma (LUAD), pheochromocytoma and paraganglioma (PCPG), skin cutaneous melanoma (SKCM), and uveal melanoma (UVM).On the other hand, NSUN7 seemed to have a detrimental impact on the prognosis of glioblastoma multiforme/brain lower grade glioma (GBMLGG), adrenocortical carcinoma (ACC),acute myeloid leukemia (LAML), stomach adenocarcinoma (STAD), and brain lower grade glioma (LGG). Furthermore, our experimental validation confirmed the inhibitory effect of NSUN7 on proliferation of renal clear cell carcinoma while elucidating its specific part in blocking cell cycle progression.
Conclusions The findings underscore the potential utility of NSUN7 as a valuable prognostic indicator for patients and offer insights into the mechanisms underlying cancer initiation and progression.
Keywords NSUN7 . TCGA . Diagnosis . Bioinformatics
Jinwei Cui, Shiye Ruan and Zhongyan Zhang have contributed equally to this work.
Shanzhou Huang, Chuanzhao Zhang and Baohua Hou have contributed equally as co-corresponding authors.
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s12672-025-02061- W.
☒ Shanzhou Huang, hshanzh@163.com; ☒ Chuanzhao Zhang, zhangchuanzhao@gpdh.org.cn; ☒ Baohua Hou, hbh1000@126.com 1South China University of Technology School of Medicine, Guangzhou 51000, China. 2Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China. 3Department of Hepatobiliary Surgery, Weihai Central Hospital, Qingdao University, Weihai 264400, China. 4Department of General Surgery, Heyuan People’s Hospital, Heyuan 517000, China. 5Heyuan Key Laboratory of Molecular Diagnosis and Disease Prevention and Treatment, Heyuan People’s Hospital, Heyuan 517000, China.
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| https://doi.org/10.1007/s12672-025-02061-w
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Abbreviations
| NSUN2 | NOP2/Sun RNA methyltransferase 2 |
| DNMT2 | DNA methyltransferase 2 |
| m5c | 5-Methylcytosine |
| cGAS | Cyclic GMP-AMP synthase |
| STING | Stimulator of Interferon Response CGAMP Interactor 1 |
| TREX2 | Three Prime Repair Exonuclease 2 |
| SLC7A11 | Solute Carrier Family 7 Member 11 |
| QSOX1 | Quiescin sulfhydryl oxidase 1 |
| CDK13 | Cyclin dependent kinase 13 |
| NSUN5 | NOP2/Sun RNA methyltransferase 5 |
| ACC1 | Acetyl-CoA carboxylase 1 |
| ALYREF | Aly/REF Export Factor |
| NSUN7 | NOP2/Sun RNA methyltransferase 7 |
| PGC-1a | PPAR-y coactivator1a |
| eRNAs | Enhancer RNAs |
| TIMER2.0 | Tumor Immune Estimation Resource 2.0 |
| TCGA | The Cancer Genome Atlas |
| UALCAN | The University of ALabama at Birmingham CANcer data analysis Portal |
| TME | Tumor microenvironment |
| TMB | Tumor mutational burden |
| MSI | Microsatellite instability |
| TPM | Transcripts per million |
| GTEx | Genotype-tissue expression |
| HPA | The Human Protein Atlas |
| ROC | Receiver operating characteristic |
| cBioPortal | The cBio cancer genomics portal |
| TISCH2 | Tumor immune single-cell hub 2 |
| HR | Hazard ratio |
| CI | Confidence interval |
| GSEA | Gene set enrichment analysis |
| SİRNA | Small interfering RNA |
| GAPDH | Glyceraldehyde-3-phosphate dehydrogenase |
| CCK-8 | Cell counting kit-8 |
| ANOVA | Analysis of variance |
| OS | Overall survival |
| PFI | Progress free interval |
| DSS | Disease specific survival |
| CDK2 | Cyclin dependent kinase 2 |
| CCNE1 | CyclinE1 |
| MiRNA | MicroRNA |
| TERT | Telomerase reverse transcriptase |
| SMARCAL1 | SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily A-like protein 1 |
| PD-L1 | Programmed cell death 1 ligand 1 |
| ACE2 | Angiotensin converting enzyme 2 |
| ACC | Adrenocortical carcinoma |
| BLCA | Bladder urothelial carcinoma |
| BRCA | Breast invasive carcinoma |
| CESC | Cervical squamous cell carcinoma and endocervical adenocarcinoma |
| CHOL | Cholangiocarcinoma |
| COAD | Colon adenocarcinoma |
| COADREAD | Adenocarcinoma of the colon and rectum |
| DLBC | Diffuse large B-cell lymphoma, a type of lymphoid neoplasm |
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| ESCA | Esophageal carcinoma |
| GBM | Glioblastoma multiforme |
| GBMLGG | Glioma |
| HNSC | Squamous cell carcinoma of the head and neck |
| KICH | Kidney chromophobe |
| KIRC | Renal clear cell carcinoma of the kidney |
| KIRP | Renal papillary cell carcinoma of the kidney |
| LAML | Acute myeloid leukemia |
| LGG | Brain lower grade glioma |
| LIHC | Liver hepatocellular carcinoma |
| LUAD | Lung adenocarcinoma |
| LUSC | Lung squamous cell carcinoma |
| MESO | Mesothelioma |
| NSCLC | Non-small cell lung carcinoma |
| OV | Ovarian serous cystadenocarcinoma |
| PAAD | Pancreatic adenocarcinoma |
| PCPG | Pheochromocytoma and paraganglioma |
| PRAD | Prostate adenocarcinoma |
| READ | Rectum adenocarcinoma |
| SARC | Sarcoma |
| STAD | Stomach adenocarcinoma |
| SKCM | Skin cutaneous melanoma |
| TGCT | Testicular germ cell tumors |
| THCA | Thyroid carcinoma |
| THYM | Thymoma |
| UCEC | Uterine corpus endometrial carcinoma |
| UCS | Uterine carcinosarcoma |
| UVM | Uveal melanoma |
1 Introduction
Recently, the global incidence and mortality rates of cancer have markedly escalated, posing a critical public health and remaining a primary cause of death [1]. Despite the progress made in cancer treatment over the last few decades [2], such as the development of superior prevention strategies and medical innovations, many patients continue to face treatment failures and ultimately lose the battle against illness. Renal clear cell carcinoma is characterized by its complex nature, and notable immunological and metabolic heterogeneity [3]. Deciphering the common molecular mechanisms underlying cancer development patterns is crucial, along with identifying reliable biomarkers for early detection, diag- nosis, and therapeutic interventions [4, 5]. Multi-omics analysis is commonly used in pan-cancer research has become common, allowing for the discovery of more molecular markers for tumors [6].
RNA modifications serve as a key factors in determining the ultimate outcome of RNAs, exerting a substantial influence on various biological processes and cellular phenotypes, thereby holding immense potential for metabolic therapy and immunotherapy [7]. The 5-methylcytosine (m5c) is widely distributed among different types of RNAs, with the highest abundance observed in transfer RNAs and ribosomal RNAs. However, they have also been detected in messenger RNAs and other non-coding RNAs [8]. Members of the NOL1/NOP2/sun (NSUN) family and DNA methyltransferase 2 (DNMT2) enzymes are the main catalysts for the methylation of m5c in human RNA [9]. There is growing evidence indicating that m5c alteration promotes cell growth, development, cell death, and other crucial biological processes by controlling RNA stability, translation effectiveness, and transcription [9]. NOP2/Sun RNA methyltransferase2 (NSUN2), increases the durability of solute carrier family 7 member 11 (SLC7A11) mRNA through m5c modification, leading to resistance to iron- induced cell death in endometrial cancer cells and slowing down cancer advancement [10]. m5c modification has been implicated in cellular metabolism, and NSUN2 acts as a direct glucose sensor that leads to cyclic GMP-AMP synthase/ stimulator of interferon response CGAMP interactor 1 (cGAS/STING) inactivation through the maintenance of three prime repair exonuclease 2 (TREX2) expression, subsequently driving tumorigenesis and immunotherapy insensitivity [11]. In
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Fig. 1 Differential expression and subcellular localization of NSUN7. a Analysis of NSUN7 expression in TIMER2.0. b Differentiation analysis of NSUN7 expression in TCGA-GTEx database. c, d In the HPA database of NSUN7 protein in OS-U2OS, A-431 cell lines. e-m. Exploiting the HPA database to analyze the protein of NSUN7 in various cancers. (* p<0.05, ** p<0.01, *** p <0.001, ns no significance.)
non-small cell lung carcinoma, NSUN2 facilitates quiescin sulfhydryl oxidase 1 (QSOX1) m5c methylation modification by targeting the thioredoxin 1 encoding region [12]. Consequently, increased NSUN2 expression contributes to gefitinib resistance and tumor recurrence [12]. Cyclin dependent kinase 13 (CDK13) promotes the phosphorylation of NOP2/Sun RNA methyltransferase 5 (NSUN5). The phosphorylated version helps with the m5c alteration of acetyl-CoA carboxylase 1 (ACC1) mRNA. Afterwards, the m5c-altered ACC1 messenger RNA binds with Aly/REF export factor (ALYREF) to enhance its durability and aid in nuclear export, ultimately advancing prostate development [13]. NOP2/Sun RNA methyltransferase7 (NSUN7) is epigenetically silenced in hepatocellular carcinoma, leading to decreased mRNA methylation. Additionally, silencing of NSUN7 through DNA methylation has been linked to clinical results and possible susceptibility to treat- ment [14]. Moreover, NSUN7 interacts with PPAR-y coactivator 1 a (PGC-1a), aiding in the transcription of genes related to fasting and causing m5c modification of Enhancer RNAs [15]. Moreover, NSUN7 has been classified as a prognostic diagnostic symbol of sepsis, early lung adenocarcinoma, and Alzheimer’s disease [16-18]. However, an extensive pan- cancer bioinformatic analysis investigating the significance of NSUN7 remains unavailable. Therefore, an extensive and thorough analysis of the association between NSUN7 and cancer will reveal innovative biomarkers and new approaches to cancer therapy.
Our study thoroughly examined the expression patterns, prognostic significance, and molecular mechanisms of NSUN7 in various types of cancer using multiple databases for comprehensive understanding. For instance, we used TCGA [19], TIMER2.0 [20], TISDIB [21], and UALCAN [22-24] for a universal evaluation of NSUN7 in pan-cancer prognosis and immune responses. NSUN7 showed increased expression in 12 types of tumors and decreased expression in 12 types of tumors and has been associated with survival rates across various cancer types. Our results indicate that NSUN7 exhibits differ- ent levels of expression across various molecular and immune subtypes in multiple forms of cancer. This demonstrated a connection between NSUN7 expression and microsatellite instability (MSI), tumor mutational burden (TMB), and the tumor microenvironment (TME). Furthermore, we provide experimental proof of the connection between NSUN7 and renal clear cell carcinoma. The findings of our study demonstrate that NSUN7 exerts inhibitory effects on the growth of renal clear cell carcinoma by slowing cell cycle progression and consequently limiting cell growth.
2 Methods and materials
2.1 RNA expression
TIMER2.0 and mRNA sequence data in TPM format for TCGA and GTEx were uniformly processed using the Toil process by UCSC XENA [25]. The pan-cancer information was acquired from the TCGA database, while the normal tissue data were obtained from GTEx.
2.2 Subcellular localization and immunohistochemical
Immunohistochemical andimmunofluorescent staining images of NSUN7 were collected from Human Protein Atlas (HPA) Data storage.
2.3 Diagnostic and prognostic importance of NSUN7
Relationship between NSUN7 levels and survival rates, and ROC curves for NSUN7 diagnosis in different types of cancer. NUSN7 interacting proteins in TCGA-KIRC are plotted as heatmaps.
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k
h
e
The expression of NSUN7 Log2 (TPM+1)
0
1
2
00
A
ch
0
Normal
Normal
Normal
ACC
1:
BLCA
BRCA
CESC
CHOL
1:
COAD
1:
DLBC
J:
ESCA
J:
GBM
J
TGCT
BLCA
HNSC
J
OV
KICH
J:
KIRC
Ji
KIRP
LAML
J:
f
LGG
I
i
J:
LUAD
Normal
Normal
Normal
LUSC
J:
MESO
B
ov
J:
PAAD
1:
HPA Database
PCPG
1
PRAD
J:
READ
]:
SARC
SKCM
J:
STAD
THCA
PRAD
COADREAD
TGCT
JA
THCA
1:
THYM
J:
UCEC
UCS
]+
UVM
m
j
g
=
Normal
Normal
Normal
Tumor Normal
UCEC
STAD
LUAD
A-431
a
TCGA-GTEx Database
0
A
0%
ACC.Tumor
BLCA. Tumor
BLCA.Normal-
BRCA. Tumor
BRCA.Normal-
BRCA-Basal. Tumor
BRCA-Her2.Tumor
BRCA-Luminal. Tumor
CESC.Tumor
CHOL.Tumor-
=
CHOL Normal 1
COAD.Tumor
¥
COAD.Normal-
DLBC. Tumor
ESCA.Tumor
ESCA.Normal-
GBM.Tumor
HNSC.Tumor
HINSC.Normal -
HNSC-HPVpos. Tumor
HNSC-HPVneg.Tumor-
KICH.Tumor
KICH.Normal
KIRC.Tumor
KIRC.Normal
KIRP.Tumor
M
KIRP.Normal -
LAML. Tumor
LGG.Tumor
LIHC.Tumor-
LIHC.Normal
LUAD. Tumor
LUAD.Normal-
LUSC.Tumor
…
LUSC.Normal -
MESO.Tumor
OV.Tumor
PAAD.Tumor
PCPG.Tumor-
PRAD.Normal
READ.Tumor
READ.Normal -
SARC.Tumor
SKCM. Tumor
SKCM.Metastasis
STAD.Tumor
STAD.Normal-
TGCT.Tumor
THCA.Tumor
THCA.Normal
THYM.Tumor
UCEC.Tumor
UCEC.Normal-
UCS.Tumor
UVM.Tumor
TCGA Database
b
d
C
U2OS
PRAD.Tumor
NSUN7 Expression Level (log2 TPM)
a
ACC
BLCA
GBMLGG
KIRC
1.0
NSUN7
1.00
NSUN7
1.00
NSUN7
1.00
NSUN7
Survival probability
Low
Survival probability
Low
Survival probability
Low
0.75
High
Survival probability
Low
0.8
High
0.75
High
0.75
High
0.6
0.50
0.50
0.50
0.4
Overall Survival HR = 2.72 (1.20 - 6.17)
Overall Survival
0.25
0.25
HR = 0.73 (0.55 - 0.99)
Overall Survival HR = 8:00 (6.16 - 10.39)
0.25
Overall Survival HR = 0.62 (0.46 - 0.85)
0.2
P = 0.017
P = 0.040
0.00
P < 0.001
P= 0.003
0
50
100
150
0
40
80
120
160
0
50
100
150
200
0
50
100
150
Time (months)
Time (months)
Time (months)
Time (months)
Low
39
16
4
1
Low
217
39
11
5
3
Low
443
83
20
6
0
Low
140
55
6
High
40
12
3
1
High
194
38
11
1
0
High
255
9
4
1
1
High
401
146
33
KIRP
LAML
LGG
LUAD
1.0
NSUN7
1.00
NSUN7
1.00
NSUN7
1.00
NSUN7
Survival probability
Low
Low
Low
0.8
High
Survival probability
0.75
High
Survival probability
0.75
High
Survival probability
Low
0.75
High
0.6
0.50
0.50
0.50
0.4
Overall Survival
0.25
Overall Survival
0.25
HR = 0.30 (0.17 - 0.55)
HR = 1.91 (1.11 - 3.29)
Overall Survival- HR = 4.16 (2.96 - 5.84)
0.25
Overall Survival
0.2
HR = 0.70 (0.52,, 0,94)
P < 0.001
P = 0.020
0.00
P < 0.001
P = 0.017
0
50
100
150
0
25
50
75
0
50
100
150
200
0
50
100
150
200
Time (months)
Time (months)
Time (months)
Time (months)
Low
88
18
3
0
Low
34
14
3
0
Low
389
76
20
6
0
Low
168
20
5
1
0
High
202
53
6
1
High
105
24
13
2
High
141
13
4
1
1
High
362
51
11
5
3
PCPG
SKCM
STAD
UVM
1.0
NSUN7
1.00
NSUN7
1.0
NSUN7
1.0
14
NSUN7
Survival probability
0.9
Low
Survival probability
Low
High
0.75
High
Survival probability
Low
Low
0.8
High
Survival probability
0.8
High
0.8
0.50
0.6
0.6
0.7
0.6
Overall Survival HR = 0.20 (0.05 - 0.92)
0.25
Overall Survival HR = 0.69 (0.52 - 0.92)
0.4
Overall Survival HR = 1.52 (1.00 -
0.4
2.29)
Overall Survival HR = 0.37 (0.14 - 0.94)
0.5
P = 0.038
0.00
P = 0.011
0.2
P = 0.049
0.2
P = 0.036
0
100
200
300
0
100
200
300
0
30
60
90
120
0
20
40
60
80
Time (months)
Time (months)
Time (months)
Time (months)
Low
45
0
0
0
Low
299
46
7
1
Low
91
23
3
0
0
Low
48
25
8
2
1
High
139
14
1
1
High
158
43
15
4
High
279
47
11
4
1
High
32
26
10
1
1
b
CHOL
1.0
DLBC
GBM
1.0
1.0
1.0
KICH
1.0
TGCT
0.8
0.8
0.8
Sensitivity (TPR)
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.4
0.2
NSUN7
0.2
NSUN7
0.2
NSUN7
0.2
AUC: 0.784
AUC: 0.903
AUC: 0.885
0.2
NSUN7
NSUN7
AUC: 0.802
AUC: 0.958
0.0
CI: 0.653-0.915
0.0
CI: 0.860-0.946
0.0
CI: 0.853-0.918
CI: 0.721-0.882
0.0
CI: 0.932-0.985
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1-Specificity (FPR)
1-Specificity (FPR)
1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1.0
KIRC
1.0
LAML
1.0
LIHC
LUSC
1.0
THCA
1.0
0.8
0.8
0.8
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.4
0.2
NSUN7
0.2
NSUN7
0.2
NSUN7
0.2
NSUN7
0.2
NSUN7
AUC: 0.833
AUC: 0.852
AUC: 0.710
AUC: 0.765
AUC: 0.769
0.0
CI: 0.788-0.878
0.0
CI: 0.807-0.898
0.0
CI: 0.668-0.753
0.0
CI: 0.733-0.797
0.0
CI: 0.737-0.801
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
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)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
OSCc
PCPG
1.0
1.0
1.0
SARC
UCEC
1.0
SKCM
1.0
0.8
0.8
0.8
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.4
0.2
NSUN7
0.2
NSUN7
0.2
NSUN7
0.2
NSUN7
0.2
NSUN7
AUC: 0.744
AUC: 0.795
AUC: 0.795
AUC: 0.734
AUC: 0.736
0.0
Cl: 0.661-0.826
0.0
CI: 0.706-0.885
0.0
CI: 0.568-1.000
0.0
CI: 0.707-0.762
0.0
CI: 0.676-0.795
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
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)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
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2.4 Immune cell infiltration evaluation
TCGA tumors were analyzed using the XCELL and TIMER algorithms to examine the correlation between NSUN7 expres- sion and immune infiltration levels [26, 27]. The strength of the correlation is indicated by the intensity of the color. Data were visualized as heat maps.
2.5 Connections between NSUN7 levels and TMB, MSI, tumor purity, immunomodulatory genes, and RNA editing genes
All data were analyzed using the Sangerbox tool and their Pearson correlations were calculated for each tumor.
2.6 Analysis of NSUN7 expression in relation to immunological and molecular subtypes
The TISIDB portal identified associations between NSUN7 expression and the immune and molecular subtypes of cancer.
2.7 Investigate the genomic alterations of NSUN7 in multiple tumor categories
Comprehensive analysis of NSUN7 gene variants was performed using the cBioPortal website following the online instruc- tions [28]. This study examined the characteristics of NSUN7 variants across various tumors in TCGA database.
2.8 Correlation analysis between tumor stemness and NSUN7 expression
Pan-cancer data were analyzed using UCSC data. We obtained the expression profiles of NSUN7 for each sample. DNAss tumor stemness scores were calculated based on the methylation profile of each tumor [29].
2.9 Single cell analysis and drug sensitivity
Correlation analyses were conducted between NSUN7 and 14 cancer states based on the cancerSEA database [30]. The corresponding single-cell data were downloaded from. h5 format and annotation results from TISCH2 using the R software MAESTRO and Seurat to process and analyze single-cell data [31]. The cells were re-clustered using the t-SNE method. The correlation between NSUN7 expression and numerous drugs in the Cancer Therapeutics Response Portal database was investigated. Supplementary Material 2 contains the documentation of single-cell datasets ..
2.10 The differential expression of NSUN7 promote methylation
Differential NSUN7 promoter methylation was assessed using UALCAN and differences in NSUN7 promoter methylation were further analyzed in the KIRC cohort based on individual cancer stage, tumor grade, ethnicity, body weight, and other clinicopathological characteristics.
2.11 Differential analysis of NSUN7 in different clinical subgroups of KIRC and correlation with overall survival (OS) within dissimilar clinical segments of KIRC
RNA-seq data from the TCGA-KIRC project were extracted in TPM format from TCGA database. Clinical information was obtained from TCGA-KIRC database. The Survminer and ggplot2 packages were used to display the results.
2.12 Protein interaction analysis
GeneMANIA [32], a prediction site for intergenic interactions, was used to identify proteins that may interact with NSUN7.
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Fig. 3 Correlation of NSUN7 mRNA expression and immune cell infiltration levels and its role in TME. a Exploring the relationship between ▸ NSUN7 and immune cells using the TIMER algorithm. b Relationship between immune cell infiltration and NSUN7 using XCELL algorithm. c Correlation analysis between high and low expression of NSUN7 and genes related to immune regulation. d Correlation between TMB and NSUN7 expression. e MSI and NSUN7 expression correlation. f NSUN7 expression correlates with tumor purity. g, h The expression of NSUN7 in different cancer Molecular Subtype and Immune Subtype
2.13 Prognosis model assessment
Data from RNA sequencing of KIRC samples and clinical information about the patients corresponding to these samples were acquired from TCGA database. A survival curve was constructed using Kaplan-Meier analysis. Based on the provided formula, the risk score was calculated as follows
n 1 i=1 Di
Using the regression coefficients and standard errors obtained from the Cox regression analysis, the hazard ratios and their 95% confidence intervals were calculated.
2.14 GSEA enrichment analysis
To investigate how NSUN7 expression affects KIRC prognosis, gene enrichment was predefined between groups with high and low expression levels. Significantly enriched gene sets were screened with a typical value of p <0.05.
2.15 Cell lines and culture
Renal cell carcinoma lines (786-o, item no. TCH-C107; and A498 item mo.TCH-C147) and human embryonic kidney cells (293T, item no.TCH-C101) were acquired from Suzhou Starfish Biotechnology Co. Ltd. (. We cultured A498 and 786-o cells in RPMI- 1640 medium (Gibco, Carlsbad, California, USA) and 293T cells in Dulbecco’s modified Eagle’s medium. The cells were cultured at 37 ℃ in a 5% CO2 incubator supplemented with 10% fetal bovine serum (Gibco). Short tandem repeat (STR) analysis and mycoplasma contamination were performed by a cell supplier to verify the authenticity of these cell lines.
2.16 Plasmid and small interfering RNA transfection
The procedure for transfecting siRNA and transforming plasmids into six-well plates is described below. Lipofectamine 2000 (Invitrogen, USA) was used for transient transfection following the manufacturer’s instructions, using siRNA and a plasmid sourced from Ruibo (Guangzhou, China). When the cells reached approximately 80% confluence, a mixture of the diluted siRNA or plasmid and Lipofectamine 2000 was added and incubated for 15 min. After rinsing the cells with PBS, 1750 ul of medium without serum was added to each well, then the siRNA or plasmid mixture was added. The medium was changed to complete medium for additional incubation after being incubated at 37 ℃ with 5% CO2 for 6-8 h. RNA and proteins were extracted from the cells 48 h after transfection to assess transfection success. The sequence of NSUN7 siRNA was as follows: si-NSUN7-1 (forward: 5’-CACAGAAAGUCUUAAUCAATT-3’, reverse: 5’-UUGAUUAAGACUUUCUGU GTT-3’); si-NSUN7-2 (forward: 5’-GAGUACAAUCACAAGCUAATT-3’, reverse: 5’-UUAGCUUGUGAUUGUACUCTT-3’); and si- NSUN7-3 (forward: 5’-GAGUUGGGUAAAUCAUCAATT-3’, reverse: 5’-UUGAUGAUUUACCCAACUCTT-3’).
2.17 Real-time fluorescence quantitative PCR
The FastPure Cell Total RNA Isolation Kit V2 (RC112-01; Vazyme, Nanjing, China) was used to isolate total RNA from specific cells. RNA and complementary DNA were extracted using the HiScript II First Strand cDNA Synthesis Kit (R333-00-AC, Vazyme). The SYBR Green Master Mix (Q411-02; Vazyme) was used for quantitative real-time PCR (qRT-PCR). The qTOW- ER3G System from Analytics Jena (Germany) was used to run the PCR program and to gather data. GAPDH base-pair
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Subtype READ
Subtype STAD
Subtype
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LUSC
UCEC
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Expression (log2CPM)
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HM-indel
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CN_LOW
MSI
POLE
Subtype
Subtype
Subtype
Subtype
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BLCA
BRCA
KIRP
LGG
LIHC
Expression (log2CPM)
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C6
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Discover
CERE
+
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Fig. 4 Cancer cell expression of NSUN7 analyzed at the single-cell level. The t-SNE plot of single-cell clustering, where different colors rep- resent different types of cells. The t-SNE plot of the expression distribution of selected genes in different cells, where different colors repre- sent expression abundance. The darker the color, the lower the expression of the gene in the cell, and the brighter the color, the higher the expression of the gene in the cell. The bar chart of the expression abundance of selected genes in different cells. (a BLCA; b GBM; c KIRC; d LAML; e LIHC; f LUAD; g PAAD; h PRAD; i SKCM; j STAD; k OV; l THCA)
normalization was applied to the RT-qPCR results by subtracting the GAPDH value from the GAPDH value. The experi- ments were performed in triplicates. The forward primer sequence for NSUN7 is 5’-GGACTCCGTTTATGTCATGGC-3’ and reverse primer, 5’-CTCAGACTCGGACAAGGACC-3’.
2.18 Western blotting procedure
A498 and 786-o cell lines were lysed on ice for 20 min with RIPA lysis buffer (P0013B; Beyotime, Shanghai, China) to obtain proteins. A solution containing proteins was obtained after spinning at 13,000 x g for 20 min at 4 ℃. To denature the proteins, 5 x loading buffers (CW0027, CWBIO, China) were boiled for 15 min. The denatured proteins were subjected to electrophoresis, membrane transfer, blocking, and incubation with primary and secondary antibodies. Following these steps, proteins were visualized using a luminescent solution. The specific antibody stock numbers and dilution ratios are provided in Supplementary Material 1.
2.19 CCK-8 assay for cell proliferation assay
For cell proliferation experiments, 1500 cells were seeded into each well of a 96-well plate and fixed for transfection. The absorbance at 450 nm nanometers was determined following a 2 h incubation in the cell incubator with a full-wavelength enzyme marker, followed by the addition of CCK-8 reagent to the plates at 0, 24, 48, 72, and 96 h our post-inoculation.
2.20 Flow cytometry
Approximately 500,000 cells were transfected with NSUN7 siRNA and control for 48 h, then underwent the Cell Cycle Assay Kit (E-CK-A351, Elabscience, Wuhan, China) followed by analysis using flow cytometry on a CytoFLEX machine (Beck- man, USA). Cells transfected with NSUN7-oe plasmid and control plasmid were also experimented as described above.
2.21 Statistical analysis
Each experiment was repeated thrice. GraphPad Prism 9.0 or R software, version 4.1.2, was used for statistical analyses. Two groups were compared using Student’s t-test, and multiple groups were compared using one-way and repeated measures ANOVA. The following symbols were used to establish statistical significance: p <0.05 is significant.
3 Results
3.1 Diverse expression of NSUN7 and subcellular localization
NSUN7 mRNA expression was markedly higher in Cholangiocarcinoma (CHOL), Colon adenocarcinoma (COAD), Kidney Chromophobe (KICH)), Lung adenocarcinoma (LUAD), Pancreatic adenocarcinoma (PAAD), and Uterine Corpus Endome- trial Carcinoma (UCEC) than in adjacent non-tumor tissue samples using the TIMER2.0 database. Conversely, NSUN7 levels were significantly reduced in Squamous cell carcinoma of the head and neck (HNSC), Renal clear cell carcinoma of the kidney (KIRC), Renal papillary cell carcinoma of the kidney (KIRP), Liver hepatocellular carcinoma (LIHC), Lung squamous cell carcinoma (LUSC), Skin Cutaneous Melanoma (SKCM), and Thyroid carcinoma (THCA) (Fig. 1a). NSUN7 expression was markedly increased in 12 types of cancer, such as CHOL, COAD, Esophageal carcinoma (ESCA), Glioblastoma multiforme (GBM), KICH, Acute Myeloid Leukemia (LAML), LUAD, Ovarian serous cystadenocarcinoma (OV), Prostate adenocarci- noma (PRAD), Rectum adenocarcinoma (READ), UCEC, and Uterine Carcinosarcoma (UCS) in TCGA tumor-GTEx normal tissues (Fig. 1b). We further analyzed the immunohistochemical data using the HPA database. In PRAD, UCEC, OV, Bladder
Discover
a
BLCA
b
GBM
Celltype major lineage
NSUN7
Mean Expression
Celltype major lineage
NSUNT
Mean Expression
404
20
20
30
ISNE_1
A
0
CDATortw
2.00
CD4TONN
2.00
ISNE 2
ISNE_1
P-
CHẤT
ISNE 2
0
Mono/Macse
0
Mono( Macro-
NK
2
1.00
Treg
1.00
-20
-20
-20
-40
-20
ISNE_1
20
-20
ISNE_1
20
-20
30
40
-
-20
0
40
NSUNT
ESNE_I
ISNE_1
NHƯỢNG
-
C
KIRC
d
LAML
Celltype major lineage
NSUNT
Mean Expression
..
Celltype major lineage
NSUNT
Mean Expression
50-1
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25
25
25
#
2:00
1.75
ISNE_2
…
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2.00
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0
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0
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ISNE 2
0
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N
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1.25
1.00
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1.00
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-25
-25
5
-90
-10
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25
50
-25
ISNE_1
25
-15
ISNE_1
-50
-35
25
NSUN?
ISNE_1
-
NSUNT
-
e
LIHC
f
LUAD
Celltype major lineage
NSUN7
Mean Expression
Celltype major lineage
NSUN7
Mean Expression
DC
40
at-
4.
20
CENTcom
20
1
20
2.00
CD4TONN
ISNE_2
0
ILC
ISNE 2
ISNE_2
2.00
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M Mono/Macte
0
Nije
0
0
9
NK
1.00
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-20
Tivg
-20
1
-20
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2
-40
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20
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20
-40
20
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20
-40
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40
0
NSUNT
ISNE_1
ISNE_1
20
40
NILINT
-
g
PAAD
h
PRAD
Celltype major lineage
NSUNT
Mean Expression
Celltype major lineage
NSUN7
Mean Expression
504
50-
₩
-
2
25
25
M
25
25
B CD&T
COST
Ľadothelial
2.00
Epithelial
ISNE_1
ISNE 2
0
0
1.75
ISNE_1
Fibroblasts
0
ISNE 2
2.00
0
1KS
Morav Macro
Mono Mammo
Myfibroblasts
1.00
Pengeniter
1.00
Tivg
-25
-25
-25
-25
NA
&
-50
-30
ISNE_1
25
25
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25
25
V
25
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ã
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ISNE_1
a
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SKCM
STAD
Celltype major lineage
NSUNT
Mean Expression
Celltype major lineage
NSUNT
Mean Expression
40
40-
.
20
20
DC
ISNE 2
…
2.00
2.00
COST
ISNE 2
1.75
SNE_2
1.75
0
1.50
0-
Gland moon Malotant
ISNE
0
1.50
Mono Macre
0
1.25
Trog
1.00
1.00
-30
Plasma
-20
-20
-25
ISNE_1
30
-40
16
10
ISNE_1
30
NSUNT
7
8
-30
ISNE_
50
-40
ISNE_1
30
NSUNT
k
OV
THCA
Celltype major lineage
NSUNT
Mean Expression
Celltype major lineage
NSUNT
Mean Expression
20
20
Malignant
25
.
r
· I
25
CDITeo
ER:
200
CONT
ISNE_2
2.00
-
0
CLINT
ISNE 2
Malignant
0
1.75
ISNE_2
1.35
0
Endothehal
ISNE 2
0
IT’S
1.00
Malignant
1.00
coTam
-10
-10
-as
Tpoobe
-25
-
-20
-20
%
-
-30
-10
-20
-10
6
-30
I
ISNE_1
ISNE_1
10
50
ISNE_1
25
-25
0
25
NSUNT
ISNE_1
NSUNT
0
Discover
Fig. 5 Analysis of NSUN7 mutations and function. a Frequency of NSUN7 alternation in pan-cancer. b Types of mutant NSUN7 in pan-can- cers. c A number, type, and location of mutations in the NSUN7 gene. d An analysis of the relationship between NSUN7 expression and tumor stemness in pan-cancer. e Analysis of NSUN7 expression in relation to apoptosis, cell cycle, DNA damage, DNA replication, invasion, metastasis, angiogenesis, differentiation, and inflammation. f Analysis of NSUN7 expression and genes related to RNA modification. g Analy- sis of the relationship between NSUN7 expression and IC50 of different drugs using CTRP
Urothelial Carcinoma (BLCA), LUAD, and Adenocarcinoma of the colon and rectum (COADREAD), tumor tissues contained higher levels of NSUN7 than that in normal tissues. Conversely, NSUN7 expression was higher in abnormal tissues than in normal tissues in the Stomach adenocarcinoma (STAD), Testicular Germ Cell Tumors (TGCT), and THCA (Fig. 1e-m). To elucidate the intracellular distribution of NSUN7, we used indirect immunofluorescence to detect NSUN7 distribution in OS-U2OS and A-431 cells using data acquired from the HPA database. These findings indicate the predominant localiza- tion of NSUN7 within vesicular structures in both cell types (Fig. 1c, d).
3.2 NSUN7 plays dual prognostic roles in human cancers
Elevated NSUN7 expression was associated with better OS. NSUN7 was identified as a negative prognostic indicator for ACC, GBMLGG, LAML, LGG, and STAD (Fig. 2a). We examined the diagnostic utility of NSUN7 for distinguishing malignant tissues from normal tissues using ROC curves (Fig. 2b) and confirmed its potential diagnostic value.
3.3 NSUN7 exhibits a robust correlation with immune cell infiltration
TIMER plus XCELL algorithms were used to estimate the relationship between NSUN7 expression and immune cell infiltration to study the influence of NSUN7 on immune cell infiltration. The TIMER algorithm revealed a strong correla- tion between NSUN7 expression and the presence of immune cells infiltrating. Fourteen markers were associated with CD8 +T cells, 17 with CD4+T cells and 18 with B cells (Fig. 3a). Subsequently, the XCELL algorithm was used to analyze the relationship between NSUN7 expression and a broader spectrum of immune cell subtypes. In some cancers, immune cell subtypes are inversely correlated with NSUN7 expression. Low-grade glioma, NSUN7, and most other subtypes positively correlated with diffuse large B-cell lymphoma (Fig. 3b). Cancer progression is significantly influenced by the TME [33]. Within the pan-cancer dataset, it was crucial to assess the relationship between NSUN7 expression and TME in detail. Furthermore, we performed a comparative analysis of NSUN7 in relation to the genes involved in immune regulation (Fig. 3c). NSUN7 expression was exhibited a positive relationship with most immune checkpoint inhibitory molecules. NSUN7 expression showed an almost inverse correlation with five gene families in various cancerous tumors. We studied the immune relevance of NSUN7 in the TME by analyzing the relationship between NSUN7 levels, TMB, and MSI, which significantly affected immunotherapy results. In ACC, GBM, HNSC, LGG, LUSC, and THYM, NSUN7 expression was positively associated with TMB, whereas it was negatively correlated with BRCA, COAD, KIRC, SARC, STAD, THCA, and UCEC (Fig. 3d). BLCA, LUAD, and TGCT showed a positive correlation with MSI and NSUN7 expression, while SARC, STAD, UCEC, and UCS were negatively correlated (Fig. 3e). Moreover, we examined the correlation between NSUN7 expression and tumor purity by calculating the Pearson correlation within each tumor. We identified a considerable correlation among the 36 tumors, with a negative correlation in eight cancers. In contrast, a strong association was observed in 28 different cancer types. NSUN7 levels and tumor purity were negatively correlated in these cases (Fig. 3f). These results implied that NSUN7 may have a substantial impact on antitumor immunity by influencing the composition of the TME.
3.4 Correlation of NSUN7 with molecular and immune subtypes
We examined the effects of NSUN7 on immunological and molecular subtypes of human tumors using the TISDIB data- base portal. Different molecular subtypes showed different NSUN7 expression levels in BRCA, COAD, ESCA, HNSC, KIRP, LIHC, LUSC, READ, STAD, and UCEC (Fig. 3g). Furthermore, NSUN7 expression in BLCA, BRCA, KIRP, LGG, LIHC, PCPG, PRAD, TGCT, THCA, and UVM was found to be associated with immune subtypes (Fig. 3h).
Discover
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b
6%-
Mutation
NSUN7: mRNA Expression, RSEM (Batch normalized from Illumina HiSeq_RNASeqV2)
5k-
5%
Structural Variant
.
Amplification
4k
Alteration Frequency
4%
Deep Deletion
3k
o
3%
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2k
2%
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1k
1%
088
O
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o
8
0
Structural variant data
Mutation data
+
Deep Deletion
Shallow Deletion
Diploid
Gain
Amplification
CNA data
+
SKCM
UCEC
BLCA
ACC
CESC
LUSC
STAD
OV
ESCA
PAAD
LUAD
KIRP
HNSC
BRCA
GBM
COADREAD
PRAD
LIHC
SARC
THCA
KIRC
LGG
LAML
KICH
THYM
MESO
UCS
PCPG
CHOL
TGCT
UVM
DLBC
NSUN7: Putative copy-number alterations from GISTIC
NSUN7
Splice (VUS)
· Truncating (VUS)
Inframe (VUS)
Missense (VUS)
Not mutated
Not profiled for mutations
Amplification
o Gain
Diploid
Shallow Deletion
Deep Deletion
Structural Variant [8]
C
R176Q/
NSUNT_Human
Somatic Mutation Frequency O 0.8%
5
.
Driver
Vus
VUS
# NSUN7 Mutation
Missense
L
C
truncating
F
Ő
3
Fusion
O
O
.. .
..
0
0
100
200
300
400
500
600
718aa
d
f
PCPG(N-176)
SampleSize
Modification
TGCTIN-147)
·
100
LIHCIN-3663
.
200
Z
SARCIN-253)
UVMIN-T
309
TRMTGIA
correlation coefficient
-500 -600
A
TRMT61B
-10-05 00 0.5
BRCA(N-774
-700
pValue
KIRCIN-309 PALADIN-156
TRMTIOC
pValue
0.00 0.02
0.04
ESCAN IT
0.00
TRMT6
Modification:
KIRPIN-168
KIPANIN 642)
0.01
YTHDCI
STAD(N-369)
0.02
YTHOF2
16A
STESIN-548)
LOADIN-271
0.03
YTHDFI
writer
O reader eraser
COADREADIN 15
0.04
PRADIN 491
MESOON-87
0.05
YTHDFS
UCECIN-173
ALKBHI
ALKBIS
LAML(N=170
NSUNT
GBMIN-SI
THYMIN-119)
DNMT3A
GBMLOGIN-558)
NSUNG
-0.6
-0.4
-0.2
0.0
0.2
0.4
Comrelation coefficient(pearson)
0.6
NSUN3
TROMTI
e
NSUN2
NSUN4
Angiogenesis
NSUNS
Apoptosis CellCycle
DNMT3B
NOP2
Differentiation
DNMTI
DNAdamage
TE12
ALYREF
DNArepair
Cor
1.0
KIAA1429
EMT
0.5
METTL14
Hypoxia
0.0
DC3H13
Inflammation
-0.5
-1.0
METTL3
Invasion
RBMIS8
Metastasis
WTAP
Proliferation
RBMIS
CBLLI
Quiescence
ALKBHS
Stemness
TO
CRC
BRCA
GBM
Glioma
HNSCC
LUAD
RB
UM
YTHDCI
FMRI
g
HNRNPC
Correlation between CTRP drug sensitivity and mRNA expression
YTHDF2
ELAVLI
FOR
YTHDE1
· == 0.05
HNRNPAZBI
FOR
0 0.001
YTHDC2
SUNT
o
O
0
YTHIDES
Comelation -83
GF2BP1
RPPRC
2
660-NASTY
KIRANIN-884)
STINKIN
ESCAP
COADREADY(NBRE
STESINOSOS
THCA(N=560)
CHOLIN-
PRAIXNATO
KIRGIN.CZ
SKCM(N=10
CESCIN-360
GLI-NXTVY
86-NOS
B
-
LAM
P
ME
yturbine
Drugs
| - | |
Discover
▸
Fig. 6 NSUN7 promoter methylation analysis. a, b Differential methylation of the NSUN7 promoter is observed in both normal and neoplas- tic individuals. Beta values represent levels of DNA methylation ranging from 0 (unmethylated) to 1 (fully methylated). Various cut-off values of ß values are used to indicate hypermethylation. c In KIRC patients, the difference in NSUN7 promoter methylation in different groups: age, sex, race, cancer stage, lymph node metastasis, tumor grade. (* p<0.05, ** p<0.01, *** p <0.001, ns, no significance.)
3.5 Analysis of NSUN7 expression in single cell level
Numerous cells within the TME play a decisive role in facilitating cancer progression [34]. The relationship between NSUN7 relationship between NSUN7 expression and a variety of cell types was determined using publicly available single-cell datasets, to understand the various functions of NSUN7 in diverse types of cancer and to improve our understanding of its role. Single-cell studies have shown that NSUN7 is predominantly expressed in B cells, DC, and monomacro-cells in BLCA, KIRC, LUAD, and SKCM cancers (Fig. 4a, c, f, i). These cell types have been implicated in enhancing immune responses and inhibiting tumor growth, consistent with the observed positive correlation between elevated NSUN7 expression levels in BLCA, KIRC, SKCM, and LUAD cancers and overall survival. Conversely, in LAML, OV, PRAD, and THCA cancers, NSUN7 is primarily expressed in malignant cells and epithelial cells (Fig. 4d, k, h, l), which are associated with tumor progression. In these cancers, high NSUN7 expression levels are inversely correlated with overall survival. Specifically, NSUN7 was predominantly expressed in oligodendrocytes, dendritic cells, malignant cells, and gland mucous cells of GBM, LIHC, PAAD, and STAD (Fig. 4b, e, g, j). These results demonstrate the importance of NSUN7 in the TME.
3.6 Gene alterations, functions, and drug sensitivity of NSUN7
Gene variants regarding NSUN7 in different tumors were analyzed using cBioPortal. We found that SKCM, UCEC, and BLCA tumor samples exhibited the highest frequency of NSUN7 genetic changes, which were the most predominant types of gene alterations in all TCGA tumor samples (Fig. 5a). Diploidy, gain functions, and shallow deletions are typical alterations in NSUN7 (Fig. 5b, c). Recent studies have identified more than 100 RNA modifications that are closely associated with tumor progression. This assertion is supported by the significant correlation between NSUN7 expression and the genes involved in RNA modification. A strong positive relationship was observed between NSUN7 and most genes related to RNA modifica- tions, such as METTL3, METTL14, and YTHDF1, as depicted in (Fig. 5f). Additionally, we compared the relationship between tumor stemness and NSUN7 expression in different tumors. Thus, 15 tumors showed significant associations, including seven tumors with a marked positive correlation (Fig. 5d). Furthermore, our findings revealed that NSUN7 expression was negatively associated with pathways linked to apoptosis, DNA damage, cell cycle, DNA replication, invasion, metastasis, and invasion, while displaying a positive correlation with differentiation, angiogenesis, and inflammation, based on data from the cancer SEA single-cell database (Fig. 5e). To analyze effect of NSUN7 undergoing chemotherapy or targeted therapy, we obtained information from the Cancer Therapy Response Portal database. Pearson’s correlation analysis revealed a significant nega- tive association between NSUN7 expression and the IC50 of drugs, such as afatinib and austocystin D. Conversely, NSUN7 expression was positively correlated with the IC50 of drugs such as AT7867, BMS-345541, and teniposide (Fig. 5g).
3.7 NSUN7 promoter methylation analysis
We used the UALCAN database to examine methylation of the NSUN7 promoter. We found that NSUN7 showed methylation in the promoters of various cancer types, such as COAD, HNSC, and KIRC, but showed different patterns in GBM, LUAD, TGCT, and UCEC (Fig. 6a). However, the methylation status of the NSUN7 promoter was two-sided, although the p-value was not statistically significant (Fig. 6b). We also examined whether NSUN7 promoter methylation was related to the clinical charac- teristics of KIRC. A strong association was found between increased NSUN7 promoter methylation and different demographic and clinical characteristics of individuals diagnosed with KIRC, such as age, sex, ethnicity, N stage, cancer stage, and tumor grade (Fig. 6c). Interventions targeting NSUN7 promoter hypermethylation may offer promising therapeutic strategies for the treatment of KIRC.
3.8 Expression of NSUN7 in clinical subgroups of KIRC and its interaction network
A variety of clinical subgroups were examined for the differential expression of NSUN7. NSUN7 expression demonstrated an inverse relationship with age, TNM stage, tumor stage, tissue grading, and primary therapy outcomes. In addition, low NSUN7
Discover
a
Promoter methylation level of NSUN7 in COAD
Promoter methylation level of NSUN7 in GBM
Promoter methylation level of NSUN7 in HNSC
Promoter methylation level of NSUN7 in KIRC
0.325
0.6-
0:45-
0.35-
0.3-
0.4
0.325
0.5
0.35
Beta value
0.275
Beta value
Beta value
Beta value
03-
0.3
0275
0.25
025
0.3
0.2
0.25-
0.225
0.15
0.225
0.2
0.2
0.1
0.2
Normal (n=37)
Primary tumor
Normal
Primary tumor (rm5-40)
Nommal (n=50)
Primary tumor
Normal
Primary tumor
TCGA samples
TCGA samples
TCGA samples
TCGA samples
Promoter methylation level of NSUN7 in LIHC
Promoter methylation level of NSUN7 in LUAD
Promoter methylation level of NSUN7 in LUSC
Promoter methylation level of NSUN7 in PRAD
1 -
0.375-
0.35
0.3-
0.35
0.325
0.20
0.325
0.3
Beta value
0.6
Beta value
Beta value
Beta value
0.275
0.26
0.3
0.25
0.275
0.225
0.2
0.25
02
0.22
0
0.225
02
Primary tuner (-377)
Nonnal
0.175
Primary tumor
TCGA samples
TCGA samples
(rm473)
Normal
Primary tumor
Normal (m=50)
Primary tuamor
TCGA samples
TCGA samples
Promoter methylation level of NSUN7 in SARC
Promoter methylation level of NSUN7 in TGCT
Promoter methylation level of NSUN7 in UCEC
0.4
0.325
0.35
0.3
0.6
Beta value
0.5
Beta value
0.3
Beta value
0.275
9.4
025
025
03
02
0.225
0.1
0.15-
0.2
0
0.1
0.175
Primary tumor
Seminoma
Non-seminoma
Nommal
Primary tumor
TCGA samples
TCGA samples
TCGA samples
b
Promoter methylation level of NSUN7 in BLCA
Promoter methylation level of NSUN7 in BRCA
Promoter methylation level of NSUN7 in CESC
Promoter methylation level of NSUN7 in CHOL
0.35-
0.35-
0.35-
0.45-
0.325
ns
0.325
0.325
0.4-
Beta value
0.3
Beta value
03-
Beta value
0.3
Beta value
0.35
0.275
0275
275
0.3
025
0.25
0.25-
0.225
0.225
0.225
0.25
0.2
02
0.2
02
Nommal
Primary lamor
Primary Tumor
Normal
Primary luamor
Normal
Primary Buamor
TCGA samples
TCGA samples
TCGA samples
TCGA samples
Promoter methylation level of NSUN7 in ESCA
Promoter methylation level of NSUN7 in KIRP
Promoter methylation level of NSUN7 in PAAD
Promoter methylation level of NSUN7 in PCPG
0.35
0.29-
0.32-
0.325
0.5
Beta value
0.3
0.27
Beta value
Beta value
0.28
Beta value
4
0.275
0 25
0.20
025-
0.24
0.225
0.23
0.24
02
0.2
Noenal
0 22
0.22
0.1
Primary tuamor (mt05)
Primary tumser
Primary tumor
Normal
Primary tumor
TCGA samples
TCGA samples
0-275%
TCGA samples
TCGA samples
Promoter methylation level of NSUN7 in STAD
Promoter methylation level of NSUN7 in THCA
Promoter methylation level of NSUN7 in THYM
06-
0.32
03-
0.5
0.25
Beta value
Beta value
Beta value
0.20
4
0.26
0.24
0.3
0.24
0.22
02
0.22
02
Normal
Primary tumor (n=305)
Normal
Primary tumor
(- 500)
Normal (n=2)
Primary tumor (=124)
TCGA samples
TCGA samples
TCGA samples
C
Promoter methylation level of NSUN7 in KIRC
Promoter methylation level of NSUN7 in KIRC
Promoter methylation level of NSUN7 in KIRC
Promoter methylation level of NSUN7 in KIRC
0.35
0.35
0.35
0.375
0.325
*
0.325
0.325
0.35
0.3
0.3
0.325
Beta value
Beta value
Beta value
0.3
=
Beta value
0.3
0.275
0275-
2.275
0.275-
0.25
-
0.25-
0.25-
0.25
0.225
0.225
0.225-
0.225
0.2
0.2
0.2
0.2
Normal
21 - 40 Yrs
41 - 60 Yes
09#141)
61 - 80 Yrs
(=154)
81 - 100 Yrs
(n=17)
()= 160)
Mais
Female ()=154)
Nommal (n=160)
Caucasian
African-american
Asian
Normal
N1
TCGA samples
TCGA samples
(n=135)
TCGA samples
TCGA samples
Promoter methylation level of NSUN7 in KIRC
Promoter methylation level of NSUN7 in KIRC
0.375
0.35
-
0.35
1
0.325
-
0.325
-
Beta value
Beta value
0.3
0.3
0 275
0.275
-
0.25
0.25-
0.225
1
0.225
0.2
0.2
Stage1
Stage3
Snage4
Nomal
Grade1
Grade2
Grade3 (n=123)
Grade4 (n=50)
(n= 150)
(m=73)
TCGA samples
TCGA samples
Discover
▸
Fig. 7 A comprehensive analysis of NSUN7 in KIRC clinical subgroups and a molecular study of its interaction network. a NSUN7 expression differs among KIRC clinical subgroups. b Different clinical subgroups of KIRC were analyzed for K-M survival of NSUN7. c NSUN7 differential analysis in TCGA-KIRC using paired samples. d The molecular network of NSUN7 interactions using Genemania database. e, f A heatmap of co-expression and correlation analysis between NSUN7 and its interacting molecules in the KIRC cohort
expression was positively correlated with OS, Disease Specific Survival (DSS), and Progression Free Interval (PFI) (Fig. 7a). Furthermore, high NSUN7 expression was associated with better OS in most clinical subgroups, including age, sex, gender, T-stage (T3), and pathologic staging subgroups (III and IV). Histological grade subgroups: G1, G2, G3, and G4. However, the low NSUN7 group demonstrated improved overall survival in stages I and II (Fig. 7b). In the TCGA-KIRC paired cohort, NSUN7 expression was reduced in most tumor samples (Fig. 7c). Using the GeneMania database to analyze molecules interacting with NSUN7, we found that PTPN6, NUSN3, NSUN4, NSUN5, and 20 other molecules interacted with NSUN7 (Fig. 7d). We then investigated the co-expression of NSUN7 and its interacting proteins in the TCGA-KIRC cohort (Fig. 7f). We found that NSUN7, MYRIP, ZNF165, ASAP2, HOOK1, CCDC186, and RSPH3 were significantly and positively correlated (Fig. 7e). These findings indicate that NSUN7 is a significant factor in KIRC.
3.9 The GSEA enrichment and in vitro analysis
After a detailed and extensive assessment, we explored the involvement of NSUN7 in KIRC. We obtained RNA sequencing data and survival information for patients with KIRC from the TCGA. In this study, high expression of NSUN7 identified as a protective factor (Fig. 8a, b). The ROC curve areas were 0.626, 0.582, and 0.550 for the 1-, 3-, and 5-year OS rates, respectively (Fig. 8c). In the TCGA-KIRC dataset, we performed differential analysis of a single gene, comparing NSUN7 High with NSUN7 Low. GSEA showed that NSUN7 mainly negatively regulated cycle-related processes or pathways, including cell cycle progres- sion, DNA synthesis, and DNA Replication (Fig. 8d-f). Therefore, we speculate that NSUN7 may inhibit proliferation of KIRC via hindering cell cycle processes. To elucidate the impact of NSUN7 in KIRC tumorigenesis, we transfected siRNA targeting NSUN7 and overexpressed NSUN7 plasmid. We confirmed its efficacy in the 786-O and A498 cell lines to elucidate the biologi- cal functions of NSUN7 in KIRC. The expression of NSUN7 was decreased in renal clear-cell carcinoma (Fig. 8g). RT-qPCR and western blotting were used to confirm the knockdown and overexpression effectiveness (Fig. 8h, i). After transfection, the proliferation of 786-O cells was assessed using a CCK-8 assay. In A498 cells, increased expression of NSUN7 led to a notable suppression of cell growth, whereas reduced expression of NSUN7 led to enhanced cell proliferation in 786-o cells. (Fig. 8j, k). Western blotting showed that increased NSUN7 levels resulted in reduced CDK2 and CCNE1 expression, whereas decreased NSUN7 levels led to the increased expression of both (Fig. 8l, m). Flow cytometry results indicated that NSUN7 knockdown boosted cell proliferation. A decrease in S-phase cells was accompanied by an increase in the number of cells in the G2/M phase. The opposite effect was observed when NSUN7 was overexpressed (Fig. 8n). Overall, reducing NSUN7 expression in renal clear cell carcinoma enhances cell growth, whereas increasing NSUN7 levels hinders cell proliferation.
4 Discussion
The rising rates of different types of cancer have made it crucial to identify predictive biomarkers linked to tumors for diagnosis, prognosis, and treatment [35]. Owing to advancements in bioinformatic tools and databases, numerous studies have been conducted to discover molecular biomarkers that can be applied to a wide range of cancer types, revealing their potential medical and functional significance [36]. Researchers have recently utilized a signature of cell- free immune-related miRNAs for early characterization of various cancers detecting cancer early with a noninvasive diagnostic biomarker [37]. Research has indicated that pan-cancer genome-wide analyses show promise for detecting advanced tumor characteristics and may offer important targets for investigating the biological foundation of cancer [38]. Thus, there is a need to continuous research for more sensitive cancer diagnostic biomarkers and therapeutic targets for the inhibition of cell cycle progression NSUN7 is involved in the biological process of m5c, and is associated with multiple forms of cancer. However, the role of NSUN7 in pan-cancer has not been thoroughly investigated. Based on these reports, our study aimed to thoroughly examine the expression, prognostic significance, and role of NSUN7. In addition, we performed experimental validation using KIRC cells to demonstrate that NSUN7 acts as an anti-oncogene by hindering cell cycle progression. Initially, our research involved a comprehensive analysis of the predictive significance of NSUN7 across various cancer categories, including its expression levels, staging implications, immune cell penetration, biological roles, and promoter methylation patterns in a wide range of cancers. TCGA and GTEx databases were used to
Discover
a
GT
..
-
m
-
3
The expression of NSUN7 Log2 (TPM+1)
The expression of NSUN7 Log2 (TPM+1)
The expression of NSUN7 Log2 (TPM+1)
The expression of NSUN7 Log2 (TPM+1)
The expression of NSUN7 Log2 (TPM+1)
0
6
6
4
6
3
4 .
A
A
&
2
2
2
2
1
0
0
0
:
0
0
Stage I
Stage II Stage III Stage IV Pathologic stage
T1
T2
T3
T4
N1
M1
G2
G3
Pathologic T stage
Pathologic N stage
NO
Pathologic M stage
MO
G1
G4
Histologic grade
AS
6
6-
6.
6-
The expression of NSUN7 Log2 (TPM+1)
6
The expression of NSUN7 Log2 (TPM+1)
The expression of NSUN7 Log2 (TPM+1)
The expression of NSUN7 Log2 (TPM+1)
The expression of NSUN7 Log2 (TPM+1)
A
4
4 .
4
4.
2
I.
2
2
2.
2
.
0
0
0
0
0
PD
SD
PR
CR
⇐ 60
>60
Alive
No
No
Primary therapy outcome
Age
OS event
Dead
DSS event
Yes
PFI event
Yes
b
Age: ⇐ 60
Gender: Male
Gender: Female
Pathologic stage: Stage I&Stage II
1.0
NSUN7
1.0
NSUN7
1.0
NSUN7
1.0
NSUN7
Low
Low
High
0.9
Low
High
Low
0.9
High
High
Survival probability
Survival probability
Survival probability
0.9
0.8
0.8
Survival probability
0.8
0.7
0.8
0.7
0.6
0.6
0.7
0.6
Overall Survival HR = 0.54 (0.33
0.5
Overall Survival HR = 0.65 (0.45 … 0.95)
Overall Survival HR = 0.41 (0.25
0.6
Overall Survival HR = 1.52 (0.83
0.5
P = 0.016
.89)
0.4
0.4
P= 0.026
P < 0.001
0.69)
P= 0.174
2.78)
0
1000
2000
3000
4000
0
1000
2000
3000
4000
Time (days)
0
1000
2000
3000
4000
0
1000
2000
3000
4000
Time (days)
Time (days)
Time (days)
Pathologic stage: Stage III&Stage IV
Pathologic T stage: T3
Histologic grade: G1&G2
Histologic grade: G3&G4
1.0
NSUN7
1.0
NSUN7
1.0
NSUN7
1.00
NSUN7
Low
High
Low
High
Low
High
Low
High
Survival probability
0.8
Survival probability
0.8
Survival probability
0.9
Survival probability
0.75
0.6
0.6
0.8
0.50
0.7
0.4
0.4
0.25
Overall Survival HR = 0.66 (0.45-
10.96)
Overall Survival HR = 0.62 (0.40)
0.96)
0.6
Overall Survival HR = 0.64 (0.31
Overall Survival
P = 0.030
0.2
P = 0.031
P = 0.209
1.29).
HR = 0.59 (0.40 … 0.85)
0.2
0.00
P = 0.005
0
1000
2000
3000
4000
0
1000
2000
3000
4000
0
1000
2000
3000
4000
0
1000
2000
3000
Time (days)
4000
Time (days)
Time (days)
Time (days)
C
e
f
TCGA-KIRC
.
TCGA-KIRC
-
·
6
5
6
The expression of NSUN7 Log2 (TPM+1)
The expression of CCDC186 Log2 (TPM+1)
The expression of ASAP2 Log2 (TPM+1)
4
4
.
NSUN7
Log2 (TPM+1)
3
·
4
Low
2
Normal
3
Tumor
2
High
2
2
1.
Pearson
R = 0,414
Pearson ·R = 0.277 P < 0.001
0
-
1
.
P < 0.001
0
PTPN6
0
Normal
Tumor
0
2
4
6
0
2
4
6
The expression of NSUN7 Log2 (TPM+1)
The expression of NSUN7 Log2 (TPM+1)
NSUN4
d
NSUN5
MYRP
MING
NSUN2
8
.
The expression of HOOK1 Log2 (TPM+1)
The expression of MYRIP
6
NOP2
RSPHS
NSLIN2
6
NSUN6
.
Log2 (TPM+1)
4
CÔỐC TẠI
NSUN?
.
4
SEMG1
NSUING
SPAG1
Z-score
2
2
2.5
TLX1
Pearson
R = 0.336
Pearson
ZNF165
CARMIL
0
0
P < 0.001
R= 0.494
0
P < 0.001
HOOK!
0.0
AŠLAPS
2
ASAP2
0
2
4
6
0
4
6
The expression of NSUN7 Log2 (TPM+1)
The expression of NSUN7 Log2 (TPM+1)
HOOK1
-2.5
CARMIL1
6
.
5.5
TLX1
The expression of ZNF165 Log2 (TPM+1)
The expression of RSPH3 Log2 (TPM+1)
4.5
DHX32
·
4
CCDC186
3.5
RSPH3
2
Pearson R= 0.478
2.5
Pearson
OR2H1
P < 0.001
R = 0.360
1.5
P < 0.001
MYRIP
0
2
4
6
0
2
4
6
The expression of NSUN7 Log2 (TPM+1)
The expression of NSUN7 Log2 (TPM+1)
Discover
▸
Fig. 8 Prognostic model construction and GSEA enrichment analysis and vitro experiment. a Relationship between NSUN7 expression and survival time and survival status in TCGA data. b KM survival curves of NSUN7 in TCGA-KIRC data, in which different groups were tested by log rank. c ROC curves and AUC VALUE values of NSUN7 at different times. d-f Results of GSEA enrichment analysis. g The expression of NSUN7 exhibits differential patterns in clear cell renal cell carcinoma. h, i The knockdown and overexpression efficiency of NSUN7 were assessed using RT-qPCR and Western blot techniques. j, k The cell proliferation was assessed using the CCK-8 assay. l, m The Western blot was employed to detect proteins associated with the cell cycle. n Cell cycle was detected by flow cytometry. (The experiments were con- ducted in triplicate and quantified. The values are presented as the mean+SD of three independent experiments. P value was shown as * P<0.05, ** P<0.01, *** P <0.001, **** P < 0.0001, independent Student’s t test)
analyze NSUN7 expression across 33 different types of tumors. NSUN7 was differentially expressed in 24 tumor types. High NSUN7 expression was associated with better OS in patients with BLCA, KIRC, KIRP, LUAD, PCPG, SKCM, and UVM. Conversely, elevated levels of NSUN7 been linked to poor survival rates in patients with ACC, GBMLGG, LAML, LGG, and STAD. This suggests that the role of NSUN7 in cancer is twofold. This phenomenon is prevalent in tumors. Cancer cells precisely regulate telomerase reverse transcriptase (TERT) expression via allele-specific DNA methylation of the TERT pro- moter [39]. Recent studies have revealed that SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily a-like protein 1 (SMARCAL1) inhibits the cGAS-STING pathway by maintaining genomic stability. In contrast, SMARCAL1 serves as a dual regulator that influences both the expression of programmed cell death 1 ligand 1 (PD-L1) and congenital immune signaling, thus promoting immune evasion in tumors [40]. Studies have shown that tumor pro- gression is significantly correlated with the TME, a complex ecosystem within the body that surrounds the tumor [41]. Understanding the TME is crucial for recognizing the immune-related factors that play a role in cancer advancement and developing cancer immunotherapy [42]. The TMB indicates the likelihood of mutations arising due to a malfunction in the DNA mismatch repair system, which in turn indicates the capacity and level of neoantigen generation in tumors [43]. The therapeutic outcomes of immunotherapy are expected to be more effective in patients with high MSI or TMB. The analysis revealed an inverse correlation between MSI, TMB, and NSUN7 expression in specific cancer types, such as BRCA and COAD. This indicates that NSUN7 may be involved in regulating the tumor immune microenvironment. Thus, we investigated the role of NSUN7 in the immune response to tumors and in the TME. Variations in NSUN7 expression levels have been noted within the TME in various cancers, showing a close correspondence with overall prognosis in comprehensive cancer research. As a systemic ailment, tumors exhibit repetitive occurrence and development accom- panied by intricate microscopic molecular mechanisms [44]. Examining the impact of genes on different types of cancer from various perspectives is crucial for gaining a thorough understanding of the underlying factors that lead to cancer- related deaths. Two factors must be considered when analyzing NSUN7 promoter methylation. Our research revealed that NSUN7 showed increased methylation in the promoters of COAD, HNSC, KIRC, LIHC, LUSC, PRAD, and SARC, but displayed the opposite pattern in GBM, LUAD, TGCT, and UCEC. Scientists have discovered reduced DNA methylation of angiotensin-converting enzyme 2 (ACE2) in most tumors exhibiting high ACE2 levels, prompting further exploration into the genetic and epigenetic changes of ACE2 [45]. This partly explains the differential expression of NSUN7 in pan-cancers.
Our findings indicate that NSUN7 may serve as a potential molecular indicator. To verify the function of NSUN7 in vitro, we confirmed its precise role of NSUN7 in the renal clear-cell carcinoma cell lines (786-o and A498). The CCK-8 proliferation assay demonstrated that knockdown of NSUN7 promoted the proliferation of renal clear-cell carcinoma cells, and conversely, overexpression of NSUN7 in A498 cells inhibited cancer cell proliferation. Analysis of the TCGA- KIRC GSEA results revealed a negative correlation between NSUN7 expression and the cell cycle. Tumor progression is significantly influenced by the cell cycle [46]. Therefore, we hypothesized that NSUN7 inhibits cell proliferation by inhibiting cell cycle progression. Western blotting experiments showed that knockdown of NSUN7 resulted in elevated expression of CDK2 and CCNE1. In summary, NSUN7 may inhibit tumor progression by blocking the cell cycle.
In addition, we must recognize the limitations of our study. First, most pan-cancer study data were obtained from publicly accessible online databases. The lack of comprehensive clinical cohort data could introduce systematic biases requiring validation [47]. Second, our experimental studies were primarily conducted in vitro without valida- tion through in vivo experiments, and therefore offer limited clinical applicability. Third, we identified a significant association between NSUN7 and the immune microenvironment; however, direct evidence elucidating the impact of NSUN7 on immunotherapy is lacking. Further research is required to substantiate this correlation and elucidate the mechanisms underlying the interaction between NSUN7 and immunotherapeutic processes. Nonetheless, it serves as a novel avenue for future research on clinical conversion therapies.c
Discover
a
b
d
Risk Type
1.00
Log-rank P = 0.0201
Groups
Cell Cycle
HR(High groups)-0.698
groups-High groups
0.0
NES == 1.703
High groups
Pady < 0.001
95%CI(0.515, 0.945)
groups=Low groups
FDR < 0.001
Low groups
Log2(TPM+1)
Overall suvival probability
Enrichment Score
-0.1
0.75
4
-0.2
-0.3
0.50
Ranked list metric
6
2
4
0,25
2
-2
-4
Median time:7.6 and 6.3
0
5000
10000
1500
0.00
Rank in Ordered Dataset
0
Groups
groups”High groups 266
167
67
23
3
0
e
Status
Synthesis of DNA
12
groups=Low groups
266
157
85
32
10
0
0.0
NES_1.998
Alive
Padi < 0.001
Dead
0
25
S
7.5
10
12.5
Enrichment Score
-0.1
FOR < 0.001
Time (years)
-0.2
-0.3
8
C
-0.4
Time
1.00
Ranked list metric
6
4
4
2
0
2
0.75
-4
0
5000
10000
1500
f
Rank in Ordered Dataset
0
True positive fraction
DNA Replication
0,50
0.0
NES == 2.131
Enrichment Score
Padi < 0.001
-0.1
FOR - 10/001
-0.2
-0.3
NSUN7
0.25
-0.4
Type
1-Years,AUC=0.626,95%C1(0.558-0.694)
3-Years,AUC=0.582,95%C1(0.529-0.635)
Ranked list metric
6
0,00
5-Years,AUC=0.55,95%CI(0.494-0.606)
4
2
0.00
0.25
0.50
False positive fraction
0.75
1.00
O
1
-4
0
5000
10000
1500
2-score of expression
-2 -101 2
Rank in Ordered Dataset
g
293-T
786-0
A498
h
786-0
i
A498
kDa
A498
Vector OE
kDa
Relative NSUN7 mRNA levels
Scramble si1 si2 si3 kDa
786-0
1,5-
.
-
15-
**
NSUN7
-81
Relative NSUNT miRNA levels
Relative NSUN7 mRNA levels
1.5
-81
NSUN7
1.0-
NSUN7
10-
-81
0.5-
0. 5
5-
0,0-
2937
786-0
A498
GAPDH
-36
a
H
Scramble
GAPDH
-36
GAPDH
-36
¢
, 9
0.
Vector
DE
786-0
A498
1
k
I
Scramble
si1
si2
kDa
m
Vector
OE
kDa
786-0
A498
2.5-
Scramble
OD Value 450(nm)
CDK2
-33
CDK2
2.5
-33
2.0-
si1
Vector
OD Value 450(nm)
2.0
OE
1.5-
.
52
1.5
1.0-
1.0
9.5-
0.5-
CCNE1
-47
CCNE1
i
-
-47
0.0-
0
1
2
3
4
0.0
6
1
2
3
4
days
days
-36
GAPDH
n
GAPDH
-36
Scramble
si1
si2
786-0
80-
nc
si1
300
60-
si2
*
200
200
40-
Count
786-0
Percentage of cells (%)
-
**
-
20-
T
**
1 1
200K
0
7
1
200K
a
400K
0
G1
s
G2
Vector
OE
A498
Percentage of cells (%)
80-
**
Vector
-
A498
60
OE
400
-
40-
**
200
20-
300K
400Kč
..
200K
-
0
G1
s
G2
PE
Discover
5 Conclusion
The discovery of the significance of NSUN7 in cancer diagnosis and prognosis has established a solid foundation for understanding its pivotal role in tumor progression.
Acknowledgements None.
Author contributions Jinwei cui: Writing original draft, Investigation, Data curation, review and editing, Visualization, Validation. Shiye Ruan contributed to writing, reviewing, editing, formal analysis, visualization, validation, and software development. Zhongyan Zhang: Writ- ing-review and editing, Visualization, Experiment validation. Hailiang Wang: Writing-original draft. Qian Yan: Investigation, Visualization. Yubin Chen: Validation, Methodology. Jiayu Yang: Writing-original draft. Jike Fang: Writing-original draft. Qianlong Wu: Visualization. Sheng Chen: Visualization. Shanzhou Huang: Validation, methodology, investigation, and funding acquisition. Chuanzhao Zhang: Writing-review editing, validation, supervision, and funding acquisition; conceptualization. Baohua Hou: Writing-review and editing, writing-first draft-review, validation, supervision of data management and funding acquisition.
Funding This work was supported by grants from the Science and Technology Program of Maoming (2024kjcxLX046), High-level Hospital Construction Research Project of Heyuan People’s Hospital (YNKT202202), Guangdong Province’s Special Fund for Science and Technol- ogy Innovation Strategy (“Major Project + Task List”) Project of Heyuan (23051017147335/2022001), the Science and Technology Program of Guangzhou (2024A04J10016 and 202201011642).
Data availability The original data of this study are available from the corresponding authors.
Declarations
Ethics approval and consent to participate Not applicable.
Competing interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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://creativeco mmons.org/licenses/by-nc-nd/4.0/.
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