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The role of PCMT1 in prognosis tumor immune microenvironment and therapeutic responses across cancers
Bo Wang1+, Sijia Huang21, Ruizhen Ren3+, Ruiqian Yao1, Erwen Kou1, Haixia Zhao1, Hao Zhu2, Mengyu Zhang4*, Liangzhe Wang1* and Yuanjie Zhu1*
Bo Wang, Sijia Huang and Ruizhen Ren contributed equally to this work.
*Correspondence: Mengyu Zhang mengyu222@126.com Liangzhe Wang
lzwang@hotmail.com Yuanjie Zhu zhuyj@smmu.edu.cn 1Department of Dermatology, Naval Medical Center, Naval Medical University, Shanghai 200052, China
2School of Medicine, Shanghai University, Shanghai 200444, China 3The Third Hospital of Handan, Handan 056001, China 4Naval Medical Center, Shanghai 200052, China
Abstract
Background Emerging evidence highlights the overexpression of Protein-L- isoaspartate (D-aspartate) O-methyltransferase (PCMT1) in multiple malignancies. However, its pan-cancer prognostic significance, tumor immune microenvironment (TIME) interactions, and therapeutic implications remain underexplored.
Methods Multi-omics data were integrated from UCSC Xena, GTEx, UALCAN, and published cohorts. PCMT1 expression patterns were systematically analyzed across 33 cancer types. Associations between PCMT1 and clinical outcomes, immune infiltration, immune checkpoint genes (ICGs), tumor mutation burden (TMB), microsatellite instability (MSI), and drug sensitivity were evaluated using bioinformatics pipelines.
Results Our pan-cancer analysis revealed differential expression patterns of PCMT1 across various malignancies, with significant upregulation in 20 cancer types and downregulation in 3 cancer types. Notably, PCMT1 overexpression was predominantly observed in epithelial-origin tumors, such as ACC (adrenocortical carcinoma), BRCA (breast invasive carcinoma), COAD (colon adenocarcinoma), and LUAD (lung adenocarcinoma). Survival analysis demonstrated that elevated PCMT1 expression was significantly correlated with unfavorable prognosis in multiple epithelial tumors, particularly in BRCA, esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), and mesothelioma (MESO). Furthermore, comprehensive analysis identified significant associations between PCMT1 expression and various tumor microenvironment features, including immune scores, six distinct immune cell types, four immunosuppressive cell populations, cancer-associated fibroblasts (CAFs)-related markers, and immunosuppressive factors. PCMT1 expression also showed significant correlations with tumor mutation burden (TMB), microsatellite instability (MSI), DNA stemness score (DNAss), and RNA stemness score (RNAss). Particularly noteworthy was the strong positive correlation between PCMT1 expression and CAFs infiltration, along with their associated factors. These findings were further validated in independent immunotherapy cohorts, where PCMT1 consistently demonstrated immunosuppressive characteristics.
Discover
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Conclusion Multi-omics analysis suggests that PCMT1 may serve as a potential prognostic biomarker and a novel immunotherapy target for pan-cancer. Keywords PCMT1, Tumor immune microenvironment, Pan-cancer, Cancer-associated fibroblasts
1 Introduction
Cancer remains a leading global health challenge and a top cause of death worldwide [1, 2]. Although research has improved prevention and treatment, the global cure rate for cancer is still low [3, 4]. Several traditional anti-cancer strategies like surgery, radiation therapy and chemotherapy are used to treat cancer. In recent years, beside targeted drug therapy, immunotherapy has become a crucial component of cancer treatment, offering new hope for patients [5]. Immunotherapy drugs that target immune checkpoint pro- teins, such as PD-1 and CTLA-4, can reinvigorate the immune system’s ability to recog- nize and attack cancer cells, markedly enhancing patient outcomes [6, 7]. Nevertheless, it encounters several challenges, such as variable efficacy, limited response rates, and some patients may develop resistance or experience adverse effects [8, 9]. Consequently, there is an urgent need to identify more specific and sensitive biomarkers to clarify the interplay between cancer and the immune system and to comprehend the molecular mechanisms underlying cancer progression for early detection and treatment.
Protein L-isoaspartate (D-aspartate) O-methyltransferase (PCMT1), or PIMT, is an essential enzyme that repairs and maintains protein structure and function across vari- ous tissues [10, 11]. It converts iso-Asp residues back to their normal form, restoring damaged proteins [12]. PCMT1 operates as a monomeric enzyme with two isoforms from alternative splicing and is involved in RNA processing, including mRNA nuclear export for better protein translation [13]. Spontaneous protein deamidation and isom- erization, linked to aging and stress, result in abnormal L-isoaspartyl residues [14-16].
Recent studies have highlighted its overexpression in malignancies such as bladder [17], breast [18], lung, and ovarian cancers [19], where it contributes to tumor progres- sion, metastasis, and therapy resistance. PCMT1 is involved in the regulation of tumor progression and may influence several signaling pathways, such as PI3K/Akt/mTOR, PI3K/Akt/STMN1, and EMT-related pathways, which constitute potential therapeutic targets for cancer treatment. The mechanisms through which PCMT1 facilitates tumor invasion and progression vary among different tumors. In bladder cancer, it serves as a negative prognostic marker associated with stage, metastasis, and infiltration, influ- encing cell migration and invasion by modulating EMT-related genes, including E-cad- herin, vimentin, Snail, and Slug [17]. In ovarian cancer, PCMT1 enhances metastasis and apoptosis resistance through its interaction with LAMB3, thereby activating the integrin-FAK-Src pathway [19]. In breast cancer, elevated PCMT1 expression correlates with increased immune infiltration and tumor purity, but is inversely related to CD4 + T cell levels [20]. Silencing PCMT1 enhances breast cancer cell sensitivity to paclitaxel by inhibiting the PI3K/Akt/STMN1 pathway [21].
Although emerging studies have linked PCMT1 over-expression to tumor cell prolif- eration and invasion [22], its systematic role in shaping the tumor immune microenvi- ronment (TIME) across multiple cancers remains unexplored. TIME is now recognized as a pivotal determinant of cancer progression and therapeutic response. Immune cell infiltration, immune checkpoint expression, and cancer-associated fibroblast (CAF)
abundance are key components shaping the TIME [23]. Recent investigations have revealed that protein repair enzymes like PCMT1 may indirectly modulate immune evasion by stabilizing antigen-presentation machinery or immunosuppressive cyto- kines [24]. For instance, a recent multi-omics study demonstrated that FAT4 muta- tions enhance immunotherapy response by promoting CD4+ memory T-cell infiltration [25]. Similarly, PCMT1 may participate in a “metabolism-immunity” axis, influencing immune checkpoint blockade (ICB) efficacy, although this hypothesis remains unex- plored [26].
These studies demonstrated that PCMT1 plays a critical role in cancer invasion and progression. However, research on PCMT1 within the context of pan-cancer analy- sis remains limited. The prognostic significance of PCMT1 expression, along with its relationship to tumor immune microenvironments and therapeutic responses across multiple cancer types, has not been comprehensively elucidated. In this study, we con- ducted an extensive pan-cancer analysis to assess the expression levels and prognostic implications of PCMT1, utilizing data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) projects. Additionally, we examined the association between PCMT1 and key components of the tumor microenvironment (TME), as well as its relationship with immunotherapy, to elucidate its regulatory mechanisms across 33 tumor types. Our study aims to uncover the prognostic value of PCMT1 in vari- ous cancers and provide novel insights into its role in tumor immunity and therapeutic responses.
2 Materials and methods
2.1 The datasets
FPKM values for gene expressions, somatic mutation data, and clinicopathological details for 33 human cancers were obtained from UCSC Xena (https://xenabrowser.net /datapages/). Full names and abbreviations for the 33 cancers are listed in Table 1. Nor- mal tissue data for PCMT1 expressions were sourced from GTEx. To evaluate the link between PCMT1 expressions and immune checkpoint inhibitor therapy effectiveness, we looked for study cohorts with published clinical and gene expression data related to this therapy.
2.2 Expression analysis of PCMT1 in tumor tissues
We extracted gene expression data from the TCGA and GTEx databases using the com- mand-line tool wget, subsequently merging and normalizing the data using the normali- zeBetweenArrays algorithm from the limma R package (version 3.50.3). The Wilcoxon rank sum test was employed to evaluate differences in PCMT1 expression between tumor and normal groups, as well as across various tumor stages. To investigate the rela- tionship of PCMT1 expression with pancancer stages, we utilized the GEPIA2 (http:/ /gepia2.cancer-pku.cn) and TISIDB (http://cis.hku.hk/TISIDB) databases. Additionally, we examined differences in PCMT1 protein expression between normal and tumor tis- sues using the CPTAC database via UALCAN (http://ualcan.path.uab.edu/index.html). Immunohistochemical images for nine tumor types and their normal counterparts were obtained to further analyze PCMT1 protein expression.
Furthermore, genetic alterations of PCMT1 within the TCGA pancancer atlas cohort were visualized using cBioPortal (http://www.cbioportal.org/). The “View 3D Structure”
| Full names | Abbreviations |
|---|---|
| Adrenocortical carcinoma | ACC |
| Bladder urothelial carcinoma | BLCA |
| Breast invasive carcinoma | BRCA |
| Cervical squamous cell carcinoma and endocervical adenocarcinoma | CESC |
| Cholangiocarcinoma | CHOL |
| Colon adenocarcinoma | COAD |
| Esophageal carcinoma | ESCA |
| Head and neck squamous cell carcinoma | HNSC |
| Kidney chromophobe cell carcinoma | KICH |
| Kidney renal clear cell carcinoma | KIRC |
| Kidney renal papillary cell carcinoma | KIRP |
| Liver hepatocellular carcinoma | LIHC |
| Lung adenocarcinoma | LUAD |
| Lung squamous cell carcinoma | LUSC |
| Mesothelioma | MESO |
| Ovarian serous cystadenocarcinoma | OV |
| Pancreatic adenocarcinoma | PAAD |
| Prostate adenocarcinoma | PRAD |
| Rectum adenocarcinoma | READ |
| Skin cutaneous melanoma | SKCM |
| Stomach adenocarcinoma | STAD |
| Thyroid carcinoma | THCA |
| Thymoma | THYM |
| Uterine corpus endometrialcarcinoma | UCEC |
| Uterine carcinosarcoma | UCS |
| Sarcoma | SARC |
| Glioblastoma multiforme | GBM |
| Brain lower grade glioma | LGG |
| Pheochromocytoma and paraganglioma | PCPG |
| Uveal Melanoma | UVM |
| Testicular germ cell tumors | TGCT |
| Lymphoid neoplasm diffuse large B-cell lymphoma | DLBC |
| Acute myeloid leukemia | LAML |
feature in the “Mutations” module was utilized to display the most frequent mutation sites of PCMT1 in a 3D schematic representation of its protein structure.
2.3 Survival analysis of PCMT1 expression levels
The dataset from UCSC Xena on cancer patient survival times included Disease-Free Survival (DFS), Disease-Specific Survival (DSS), Overall Survival (OS), and Progres- sion-Free Survival (PFS). For each cancer type, patients were divided into high and low PCMT1 expression groups using the median value, which provides balanced group sizes and is less sensitive to distributional heterogeneity. Using “survival” and “survminer” R packages, univariate Cox regression and Kaplan-Meier (KM) analyses were performed to evaluate the link between PCMT1 expression and the four survival metrics across 33 cancer types.
2.4 Correlations between PCMT1 expressions and TME in pan-cancers
The TIMER2 platform (http://timer.cistrome.org/) was used to assess the correlation between PCMT1 expression and six immune cell types (B cells, CD4+ T cells, CD8 + T
cells, dendritic cells, macrophages, and neutrophils) as well as four immunosuppressive cell categories (cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), and regulatory T cells (Tregs). For visualization, results from the TIMER algorithm were displayed using reshape2 and RColorBrewer in R.
To explore the link between PCMT1 and tumor immunosuppression, a correlation analysis was performed between PCMT1 and factors related to cancer-associated fibro- blasts (CAFs) and immunosuppression [27-29]. High tumor mutation burden (TMB) and microsatellite instability (MSI) suggest positive responses to immune checkpoint inhibitors. TMB was derived from UCSC Xena somatic mutation files for 33 cancer types and calculated using a unified non-synonymous mutation counting workflow implemented with maftools R package (version 2.22.0). MSI data was obtained from pre- vious research [30]. The “fmsb” R package was used to create radar charts illustrating the relationship between PCMT1 and TMB/MSI.
Additionally, RNA and DNA stemness scores for all TCGA tumor types were sourced from earlier studies (https://doi.org/10.1016/j.cell.2018.03.034/attachment/37f8d8d0-a f00-404b-9bce-2d7d1d6a1a0d/mmc1.xlsx) and combined with gene expression data, excluding samples with zero expression [31]. Pearson correlation analysis was then used to examine the connection between PCMT1 expression and stemness scores. Lastly, gene set enrichment analysis (GSEA) was performed to divide the samples into two groups based on PCMT1 expression levels and retrieve statistically different pathways between the two groups from the subset based on c2.cp.kegg.v7.4.symbols.gmt.
2.5 Analysis of PCMT1 expressions in predicting chemotherapeutic and immunotherapeutic efficacies
Associations between drug responses and PCMT1 expression were examined using the Genomics of Drug Sensitivity in Cancer (GDSC) database via the gene set cancer analysis (GSCA) platform (http://bioinfo.life.hust.edu.cn/GSCA). The GDSC resource is based on high-throughput screening of>1,000 authenticated human cancer cell lines, each with comprehensive genomic profiles. Only cell lines that passed strict quality- control procedures-including confirmation of cell line identity, elimination of cross- contaminated lines, verification of tissue origin, and completeness of genomic and drug-response data-were included in the analysis [32]. Drug sensitivity metrics (IC50 and AUC) were computed from fluorescence-based viability assays across nine drug concentrations, and assays failing internal quality metrics were removed prior to data release. The GSCA platform further integrates GDSC drug-response data with matched transcriptomic profiles [33]. Only cell lines with both reliable PCMT1 expression data and high-quality drug-response measurements were retained. Spearman correlation analysis and elastic-net-based modeling built into GSCA were applied to identify drugs whose sensitivity was significantly associated with PCMT1 expression.
To validate the clinical relevance of PCMT1 expression in chemotherapy response, ROC Plotter was used to evaluate multiple patient cohorts (ovarian, colorectal, glioblas- toma, and breast cancers) with predefined criteria: inclusion of samples with available treatment-response annotations, exclusion of samples lacking PCMT1 expression data, and use of standardized preprocessing pipelines within the platform.
For immunotherapy prognosis, three studies categorized patients into response (com- plete/partial response) and non-response (progressive/stable disease) groups, with the Wilcoxon test employed to evaluate differences in PCMT1 expression between these groups. Lastly, to explore the mechanisms underlying PCMT1-mediated resistance to immunotherapy, the study investigated correlations between PCMT1 expression, sur- vival risk, and cytotoxic T lymphocyte (CTL) presence across various cohorts undergo- ing immunotherapy, utilizing the “Query Gene” module on the TIDE website.
3 Results
3.1 Features of PCMT1 expression in tumor tissues
Firstly, we assessed PCMT1 expression combined using TCGA and GTEx data. Com- pared to normal tissues, PCMT1 was differentially expressed in 23 of 33 tumors, such as ACC, BRCA, CESC, COAD, ESCA, LGG, LUAD, LUSC, OV, PAAD, PCPG, PRAD, READ, SKCM, STAD, TGCT, THCA, UCEC, and UCS, among which almost all of the epithelial-origin tumors (carcinomas) showed higher level of PCMT1. While no differ- ential expression was observed in most mesenchymal-origin tumors (sarcomas), such as SARC, GBM, PCPG and so on. Surprisingly, a significantly lower level of PCMT1 was seen in two of epithelial-origin tumors, such as Cholangiocarcinoma (CHOL) and Kid- ney renal clear cell carcinoma (KIRC) (Fig. 1A). To assess the differences in PCMT1 gene at the translational level, CPTAC database was used to compare differences in PCMT1 protein expression between normal and tumor groups on the UALCAN website. Nota- bly, the PCMT1 protein levels in tumor tissues of BRCA, COAD, GBM, HNSC, KIRC, LIHC, LUAD, LUSC, PAAD, UCEC were significantly lower than normal tissues, which is not in tandem with PCMT1 RNA expressions (Fig. 1B).
In addition, using the HPA database, we found that the expression level of PCMT1 was significantly increased in tumor tissues of breast, skin, testis, and thyroid, endometrium, lung, ovarian, pancreas, and stomach, which is consistent with the PCMT1 RNA expres- sions (Fig. 2).
Subsequently, cBioPortal was utilized to investigate the genetic alterations of PCMT1 across various tumor types. Notably, uveal melanoma patients exhibited the highest fre- quency of PCMT1 gene alterations, with a rate of 7.52%, all of which were characterized by deep deletions. In contrast, the mutation frequency in uterine corpus endometrial carcinoma was the highest at approximately 1.85% (Fig. 3A). We also identified the mutation sites of PCMT1 and depicted the three-dimensional (3D) structure of the most frequently mutated site (Fig. 3B and C).
3.2 Analysis of association of PCMT1 expression with survival in pan-cancer
The prognostic significance of PCMT1 across different cancers was assessed using Cox regression analyses based on data from the TCGA database. The forest plots presented in Fig. 4 indicate that elevated PCMT1 expression serves as a predictor of for poor OS in BRCA, ESCA, HNSC, LIHC, and MESO; poor PFS in BRCA, HNSC, and LUSC; poor DFS in BRCA, LIHC, and LUSC; and poor DSS in BLCA, BRCA, HNSC, LIHC, LUSC, and MESO. In contrast, the elevated PCMT1 expression positively correlated with the prognosis in KIRC, LGG, READ, and THYM.
A
B
PCMT1 expression(log)
-
2-
6-
8-
4
0-
Protein expression of PCMT1 in Ovarian cancer
Protein expression of PCMT1 in Clear cell RCC
Expression level of PCMTI in Breast cancer
ACC
n = 127
n = 79
BLCA
n = 28
ns
n = 411
=
=
=
BRCA n = 292
CPTAC samples
OV
CPTAC samples
KIRC
BRCA
n = 1097
CESC
n = 13
n = 304
CHOL
n =9
n = 36
=
COAD
n = 345
n = 469
DLBC
n = 444
n = 40
ESCA
n = 660
n = 161
GBM
n = 1151
ns
Protein expression of PCMTI in Hepatocellular carcinoma
2
n = 155
Protein expression of PCMT1 in Pancreatic adenocarcinoma
n = 44
ns
Protein expression of PCMT1 in Colon cancer
HNSC
n = 500
n = 51
ns
=
=
=
KICH
n = 65
COAD
KIRC
n = 99
n = 534
PAAD
CPEAC samples
LIHC
CPTAC samples
KIRP n = 59
ns
CPTAC samples
n =288
LAML
n = 70
n = 151
-
LGG
n = 1146
n = 511
..
LIHC
n = 160
ns
disease
LUAD
n= 371
n = 346
n = 524
LUSC
n = 336
a
-
MESO
n = 501
Protein expression of PCMT1 in Lung adenocarcinoma
n = 86
n = 88
Protein expression of PCMT1 in UCEC
Protein expression of PCMT1 in Glioblastoma multiforme
n = 374
=
=
=
PAAD
n = 169
n = 177
n = 178
CPEAC samples
UCEC
CPEAC sangles
LUAD
CPFAC samples
GBM
PCPG
n =3
PRAD
n = 152
n - 496
READ
n = 314
n = 166
SARC
n=2
ns
SKCM
n = 259
n = 813
n = 105
STAD
n = 204
n = 375
?- vallı
TGCY
n = 165
n = 150
Protein expression of PCMTI in Lung squamous cel
Protein expression of PCMT1 in Head and neck squamous
THCA
n = 336
n = 502
THYM
n = 446
ns
=
=
n = 119
LUSC
HNSC
carcinoma
UCEC n = 113
carcinoma
CPTAC samgiles
n = 547
CPTAC samples
-
UCS
n=78
n = 56
UVM
Tumor
Normal
sample_type
count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples. Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral PCMT1 protein between normal tissue and BRCA, COAD, GBM, HNSC, KIRC, LIHC, LUAD, OV, PAAD and UCEC tissues. PCMT1 mRNA between tumor group and normal group in TCGA and GTEx databases. B Differential expression of Fig. 1 Differences in RNA and protein expression levels of PCMT1 in different tumors. A Differential expression of
(*p<0.05, ** p<0.01, *** p <0.001)
Pancreas normal
Lung normal
Breast normal
Pancreas cancer
Lung cancer
Breast cancer
Skin normal
Ovarian normal
Endometrium
Skin cancer
Ovarian cancer
Endometrial cancer
Testis normal
Stomach normal
Thyroid normal
Testis cancer
Stomach cancer
Thyroid cancer
Fig. 2 Representative immunohistochemistry images of PCMT1 in breast, endometrium, lung, ovarian, pancreas,
skin, stomach, testis, thyroid, as well as their malignant tissues based on The Human Protein Atlas
A
8%
6%
Alteration Frequency
4%
2%-
Structural variant data Mutation data CNA data
Uveal Melanoma (TCGA, PanCancer Atlas)
Diffuse Large B-Cell Lymphoma (TCGA, PanCancer Atlas)
Sarcoma (TCGA, PanCancer Atlas)
Liver Hepatocellular Carcinoma (TCGA, PanCancer Atlas)
Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)
Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas)
Stomach Adenocarcinoma (TCGA, PanCancer Atlas)
Thymoma (TCGA, PanCancer Atlas)
Skin Cutaneous Melanoma (TCGA, PanCancer Atlas)
Bladder Urothelial Carcinoma (TCGA, PanCancer Atlas)
Colorectal Adenocarcinoma (TCGA, PanCancer Atlas) Breast Invasive Carcinoma (TCGA, PanCancer Atlas)
Adrenocortical Carcinoma (TCGA, PanCancer Atlas)
Head and Neck Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Lung Adenocarcinoma (TCGA, PanCancer Atlas)
Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Testicular Germ Cell Tumors (TCGA, PanCancer Atlas)
Brain Lower Grade Glioma (TCGA, PanCancer Atlas)
Pheochromocytoma and Paraganglioma (TCGA, PanCancer Atlas)
Esophageal Adenocarcinoma (TCGA, PanCancer Atlas) Pancreatic Adenocarcinoma (TCGA, PanCancer Atlas)
Glioblastoma Multiforme (TCGA, PanCancer Atlas)
Prostate Adenocarcinoma (TCGA, PanCancer Atlas)
Kidney Renal Papillary Cell Carcinoma (TCGA, PanCancer Atlas)
Lung Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Kidney Renal Clear Cell Carcinoma (TCGA, PanCancer Atlas)
Acute Myeloid Leukemia (TCGA, PanCancer Atlas)
Cholangiocarcinoma (TCGA, PanCancer Atlas)
Kidney Chromophobe (TCGA, PanCancer Atlas)
Mesothelioma (TCGA, PanCancer Atlas)
Thyroid Carcinoma (TCGA, PanCancer Atlas) Uterine Carcinosarcoma (TCGA, PanCancer Atlas)
Mutation
Structural Variant
Amplification
Deep Deletion
Multiple Alterations
B
5
# patients
A226V/X226_splice
0
PCMT
0
100
200
286aa
A
B
C
D
=
=
3.3 Correlations between PCMT1 expression with tumor microenvironment (TME) in pan- cancer
To evaluate the role of PCMT1 in the TME, we used the TIMER2 database to explored the association of PCMT1 with immune cell infiltration in 33 tumors. Immune infiltra- tion analysis showed that the expression level of PCMT1 was associated with the level of immune cell infiltration (including B cell, CD4+ T cells, CD8 + T cells, neutrophils, mac- rophages, and dendritic cells) (Fig. 5A). Especially in PAAD, PRAD, LIHC, and THYM, there was a strongly positive correlation between the expression level of PCMT1 and the immune cell infiltration level (almost all 6 types of immune cell). We also assessed the relationship between infiltrations of 4 immunosuppressive cells and PCMT1 expres- sion. Figure 5B showed that the expression level of PCMT1 was associated with the infiltrations of 4 immunosuppressive cells (CAFs, TAMs, MDSCs, and Tregs). Although
A
ACC
0.6
B
C
… …
…
ACC
BLCA
-
-
-
BLCA
0.4
BRCA
0.4
BRCA
02
CESC
02
-
CESC
0.05
CHOL
0.2
CHOL
0
COAD
COAD
0
DLBC
-0.25
DLBC
-
ESCA
-0.2
ESCA
-0.2
GBM
GBM
-
-
HNSC
-0.4
HNSC
-0.4
*
KICH
.
…
… …
KICH
KIRC
KIRP
KIRC
LGG
-
-
…
KIRP
… … .
**
LIHC
-
…
…
…
LGG
LUAD
-
-
LIHC
LUSC
MESO
…
…
. .
LUAD
OV
LUSC
PAAD
MESO
*
PCPG
…
…
OV
-
-
PRAD READ
D
… …
PAAD
-
PCPG
SARC
-
-
SKCM
-
…
PRAD
-
STAD
READ
-
-
TGCT
SARC
THCA
-
-
…
SKCM
THYM
03
-
UCEC
-05
-
.
STAD
UCS
TGCT
Macrophage M2_CIBERSORT-ABS
*
UVM
.**
…
THCA
Cancer associated fibroblast_EPIC
Cancer associated fibroblast_MCPCOUNTER
Cancer associated fibroblast_TICE
Cancer associated fibroblast_XCELL
Macrophage MO_CIBERSORT
Macrophage MO_CIBERSORT-ABS
Macrophage M1_CIBERSORT
Macrophage M1_CIDERSORT-ABS
Macrophage M1_QUANTISEQ
Macrophage M1_XCELL
Macrophage M2_CIBERSORT
Macrophage M2_QUANTISEO
Macrophage M2_TIDE
Macrophage M2_XCELL
MDSC_TIDE
T cell regulatory (Tregs)_CIBERSORT
T cell regulatory (Tregs)_CIBERSORT-ABS
T cell regulatory (Tregs)_QUANTISEO
T cell regulatory (Tregs)_XCELL
…
-
THYM
UCEC
UCS
POPG
UVM
B cell
Macrophage
Myeloid dendritic cell
Neutrophil
T cel CD4+
T cel CD8+
-
-
E
-
-
-
ACTA2
06
-
CCL2
CCLS
0.4
-
P
**
**
*
**
CXCL12
-
GF1
02
-
.
-
-
FAP
0
w
FOF2
-
-
HOF
-0.2
-
.
..
P4HA3
PALLD
CAFs-associated factors
-0.4
*
POPN
1
+
S10048 $10049
TGFB1
- -
IGFB2
..
TGFB3 THUS1
-
.
INC
-
CTLA4
-
POCD1
CD274
PDCDILG2
Immunosuppressive factors
FAS
FASLG
ACC
BLCA
ARCA
CESC
CHOL
COAD
DLBC
GBM
HNSC
NOCH
KORC
GIRP
LAMI
LIHC
LAJAD
LUSC
MESO
PWAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TOCT
THCA
THYM
UCS
UVM
correlations between PCMT1 and CAF/M2-TAMs/Tregs were not completely consis- tent in different algorithms. We consistently observed that PAAD showed a positive correlation with CAFs, MESO, HNSC, BLCA and BRCA exhibited positive correlations with macrophage M0, BLCA showed a robust positive correlation with macrophage M1, and ACC, BLCA, BRCA, CESC, COAD, ESCA, HNSC, LGG, LIHC, LUAD, LUSC, MESO, PCPG, READ, SKCM, STAD, TGCT, THCA and UCEC consistently displayed positive correlations with MDSCs.
Subsequently, we evaluate the correlations between PCMT1 and CAFs-associated fac- tors and immunosuppressive factors. As shown in Fig. 5E, there were significant asso- ciations between PCMT1 and most CAFs-associated factors and immunosuppressive factors, suggesting the roles of PCMT1 in tumor microenvironment. There were signifi- cant positive correlations between PCMT1 and most CAFs-associated/immunosuppres- sive factors in BLCA, KIRP, LIHC, LUAD, PAAD, and UVM, verifying that the function of PCMT1 is positively related to CAFs in these tumor types. Notably, PCMT1 expres- sions in some tumors were positively correlated with common immune checkpoint genes, such as CTLA4, FAS, FASSG.
Next, correlations between PCMT1 expressions and TMB/MSI were evaluated by a radar map to predict immunotherapeutic efficacies. Expressions of PCMT1 were nega- tively correlated with MSI of PRAD, LUSC, LUAD, and KICH, but positively correlated
with MSI of UCEC, STAD, READ, HNSC (Fig. 5C). Expressions of PCMT1 were nega- tively correlated with TMB in THYM, but positively correlated with TMB in ACC, UCS, UCEC, STAD, OV, LUAD, and BRCA (Fig. 5D). There were no overlapping tumors in negative correlations among TMB, MSI and PCMT1. However, UCEC and STAD showed positive correlations between PCMT1 and TMB or MSI.
DNAss reflects epigenetic characteristics and RNAss reflects gene expression. The findings showed that PCMT1 expression, negatively correlated with DNAss in 3 tumor types, including BLCA, KICH, and THYM; positively correlated with DNAss in 9 tumor types, including BRCA, STAD, and TGCT (Fig. 6A). PCMT1 expression, negatively cor- related with RNAss only in KICH, while positively correlated with RNAss in 18 tumor types (Fig. 6B).
3.4 Patients with elevated PCMT1 levels were sensitive to chemotherapy, not immunotherapy
The relationship between PCMT1 levels and GDSC drug sensitivity was evaluated using the GSCA website. Elevated PCMT1 levels were correlated with increased sensitivity of cancer cell lines to AR-42, AT -7519, BHG712, BMS345541, BX-912, CAY10603, I-BET-762, JW-7-24-1, KIN001-260, Masitinib, Navitoclax, NG-25, Nilotinib, NPK76-II-72-1, OSI-027, PI-103, QL-XI-92, THZ-2-102-1, TL-1-85, TL-2-105, UNC0638, WZ3105. While with decreased activities of Afatinib, BMS - 536,924, BMS - 754,807, Cetuximab, Gefitinib, Midostaurin, Nutlin - 3a (-), and Trametinib in various cancer cell lines (Fig. 7A).
The impact of PCMT1 on chemotherapeutic responses in different tumor cohorts was also determined. It was found that breast cancer and glioblastoma patients with elevated PCMT1 levels had greater chemotherapeutic benefits, while OV patients with elevated PCMT1 levels were not sensitive to chemotherapy, relative to those with low expressions (Fig. 7B).
What’s more, to investigate the reasons for poor immunotherapeutic effects in patients with high PCMT1 expressions, a PCMT1 gene query was conducted on the TIDE web- site. In bladder cancer, GBM, KIRC and melanoma, relations between PCMT1 and prog- nosis and the relation between PCMT1 and CTL were consistent (Fig. 7C). The higher the CTL level, the better the patient’s prognosis.
A
B
DNA stemness
RNA stemness
PRAD
PAAD
.
UCS
-
KIRP
DLBC
.
KIRP
PAAD
.
SKCM
DLBC
MESO
UCS
SKCM
CESC
LAML
Ov
SARC
PRAD
ACC
CHOL
LIHC
P.Value
UVM
ACC
PCPG
<0.001
P.Value
UVM
READ
<0.001
UCEC
0.001-0.01
CESC
0.01-0.05
0.001-0.01
HNSC
LIHC
Cancer
THYM
READ
>0.05
Cancer
0.01-0.05
PCPG
LAML
LGG
KIRC
>0.05
LUSC
Size
GBM
LUAD
Size
KIRC
200
THCA
400
BLCA
300
GBM
SARC
600
ESCA
600
HNSC
BLCA
800
THCA
900
KICH
ESCA
LUAD
OV
MESO
LUSC
COAD
KICH
CHOL TGCT
COAD
STAD
BRCA
LGG
STAD
UCEC
THYM
TGCT
BRCA
-0.50
-0.25
0.00
0.25
0.50
Correlation
-0.50
-0.25
0.00
0.25
0.50
Correlation
A
B
Breast_cancer
Glioblastoma
Ovarian cancer
Correlation between GDSC drug sensitivity and mRNA expression
205202_4
296202_
.
8
8
FDR
8
BE
0 €=0.05
-Log10(FDR)
1
Symbol
8
8
PCMT
O
6
o
O
€
A
.
-
Responder
4
Correlation
Responder
-0.2
=
9
=
0.2
:
#
=
1
Drug
=
=
a
=
:
=
0
02
@
0
8
False positive rate
False positive rate
62
False positive rate
58
C
a
Continuous z= = 0.615 , p= 0.538
Continuous z = 3.24 x 10-1, p= 7.46 x 10-1
Continuous z = 2.21, p= 2.74 x 10-2
Continuous z = - 6.38 x 10-4, p= 5.24 x 10-1
1.0
1.0
PCMT1 Top (n=28)
1.0
PCMT1 Tep (n= 36)
PCMT1 Bottom (n=17)
0.9
PCMT1 Bottom (n=5)
Survival Fraction
0.8
.8
0,6
Survival Fraction
0.8
Survival Fraction
Survival Fraction
O
PCMT1 Top (n=4)
4
CMT1 Bottom (n=9)
5.4
5
0.4
2
A
PCMT1 Top (n=22)
0.2
0.2
0.3
9
PCMT1 Bottom (n=326)
0.2
0
5
10
15
20
25
0
100
200
300
400
500
600
40
0
250 500 750 1000 1250 1500 05 (day)
OS (month)
05 (day)
0
20
05 (month)
60
== 0.129 . p= 0.016
Fm-3.71e-01 . p=1.73e-01
r =- 9.01e-02 . p=1.23e-01
== 0.0499. p= 0.757
0.6
0
0
0.4
2
0
-
0.2
2
-
PCMT1
PCMT1
PCMT1
0.0
PCMT1
-2
0.5
a
0.2
-0.4
-6
-0.6
-8
15
·
.
-4
-2
0
2
4
-1
0
2
3
-1
0
1
2
3
-2
-1
0
1
2
CTL
CTL
CTL
CTL
Bladder Cancer_PD-1
GBM_PD-1
KIRC_PD-1
Melanoma_PD-1
4 Discussion
Although PCMT1 plays an vital role in biological functions, its function in tumors has not been clarified in the past. Only a limited number of studies have identified a correla- tion between PCMT1 and tumor progression [34]. In this study, we conducted a com- prehensive analysis of the expression profile and prognostic significance of PCMT1 in pan-cancer, as well as its potential role in tumor immunology.
Initially, we evaluated the mRNA expression levels of PCMT1 across different tumors. Our findings indicated that PCMT1 was differentially expressed in 23 out of 33 tumor types. Specifically, PCMT1 expression was elevated in most epithelial-origin tumors, such as BRCA, COAD, LUAD, LUSC, OV, STAD and etc., and closely related to the poor prognosis, considering PCMT1 as a prognostic predictor in these carcinomas. These findings align with previous studies indicating that elevated levels of PCMT1 may enhance cancer cell survival by repairing stress-induced protein damage, thereby sup- porting resistance to apoptosis and therapy [35, 36]. However, research on PCMT1 in mesenchymal tumors, such as sarcomas, remains limited. The role of PCMT1 in sar- comas could potentially differ due to the unique protein repair demands of the micro- environment or the distinct metabolic profiles characteristic of mesenchymal tumors. Notably, PCMT1 expression was significantly downregulated in two carcinomas, CHOL and KIRC, and was positively associated with prognosis, warranting further investigation.
Additionally, we found that the PCMT1 protein levels in tumor tissues of BRCA, COAD, GBM, HNSC, KIRC, LIHC, LUAD, LUSC, PAAD, UCEC were significantly lower than normal tissues, which is not in tandem with PCMT1 RNA expressions. Notably, the discrepancies between PCMT1 mRNA abundance and its protein levels in several tumor types are not uncommon in cancer transcriptomics and may reflect post-transcriptional regulation, protein turnover dynamics, or assay-specific limitations. PCMT1 is a stress- responsive repair enzyme that is rapidly consumed upon binding to isoaspartyl-dam- aged substrates; thus, its protein half-life may be short even when mRNA levels are high [37]. In addition, miRNA-mediated suppression or ubiquitin-proteasome degradation triggered by chronic proteotoxic stress [38], could further uncouple transcription from translation. Technical factors-such as antibody epitope masking by post-translational modifications or sample-specific degradation during tissue processing-may also con- tribute to the observed inconsistency [39]. Future studies integrating single-cell trans- latome profiling, pulse-chase proteomics, and miRNA interactome screening will be required to dissect the exact mechanisms governing PCMT1 protein stability and to clarify whether the mRNA signal or the protein signal more accurately reflects its func- tional impact in the tumor microenvironment.
Genetic alterations are a fundamental factor in the development of cancer, involving modifications in genetic content, gene disruption, and phenotypic variations [40, 41]. Typically, cancer genomes accumulate four to five driver mutations, integrating both coding and noncoding genomic elements, and their inherent instability is a molecular genetic hallmark of tumorigenesis across various cancers [42]. Recent evidence increas- ingly supports the potential of therapies targeting mutated genes as a promising strat- egy for cancer treatment [43]. Our study identified that PCMT1 is frequently mutated in a variety of cancers, with most alterations presenting as amplifications in patients with ACC, BRCA, SARC, ESCA, and COAD. These findings further highlight the potential of PCMT1 as a viable therapeutic target.
Tumor-infiltrating immune cells (TIICs) are key to the tumor immune microenvi- ronment and act as biomarkers for prognosis and immunotherapy response in various cancers [44, 45]. This study explored the link between PCMT1 expression and TIICs, immune scores, and stromal scores in several malignancies. We found a strong positive correlation between PCMT1 levels and the presence of B cells, CD8 + T cells, neutro- phils, macrophages, and dendritic cells in PAAD, PRAD, LIHC, and THYM. T cells play a crucial role in recognizing tumor antigens, and CD4+ T cells are essential for tumor immunity. When CD4+T cell function is impaired, cancer cells can escape immune detection [46, 47]. Interestingly, PCMT1 levels are generally negatively correlated with CD4+T cells, indicating PCMT1 may help cancer cells evade the immune system by modulating T-cell differentiation or cytokine signaling. We also discovered a strong link between PCMT1 and CAF infiltration, suggesting PCMT1 may aid carcinogenesis by promoting CAF invasion and extracellular matrix remodeling, as previously reported in dynamic network biomarker studies of thyroid cancer progression [48].While its bio- logical significance needs further exploration, PCMT1 shows promise as a biomarker for pan-cancer detection and immune regulation. We also discovered a strong link between PCMT1 and CAF infiltration, suggesting PCMT1 may aid carcinogenesis by promoting CAF invasion.
We observed a consistent positive correlation between PCMT1 expression and CAF infiltration (FAP+/ACTA2+) across 6 epithelial tumors (Fig. 5E). This finding aligns with a recent pan-cancer study demonstrating that mesenchymal stem-cell-derived CAFs secrete TGF-B and IL-6, thereby establishing physical and cytokine barriers that sup- press CD8+ T-cell trafficking and confer resistance to PD-1 blockade [49]. In hepatocel- lular carcinoma, high CAF density has been shown to impair antigen presentation via TGF-B/Smad activation and to predict poor ICB response [22]. Integrating these data, we propose that PCMT1 may stabilize TGF-ß pathway components, enhance ECM deposition, and sustain immunosuppressive factor secretion, collectively fostering an immune-excluded phenotype. Preliminary validation from the TIDE cohorts revealed that high PCMT1 expression is negatively correlated with CTL abundance and is asso- ciated with poorer anti-PD-1 outcome in bladder cancer, GBM, KIRC, and melanoma (Fig. 7C), underscoring the potential of PCMT1-CAF axis as a pan-cancer predictor of ICB resistance.
What’s more, we investigated the association between PCMT1 and two immunother- apeutic biomarkers, TMB and MSI, both evaluated through comprehensive genomic profiling [50, 51]. These biomarkers are recognized predictors of immunotherapy effi- cacy [52, 53]. Patients with elevated levels of TMB or MSI exhibited favorable clinical responses to PD-1/PD-L1 blockade [54, 55]. Our results demonstrated that PCMT1 expression is positively correlated withTMB or MSI in most of the detected cancers, such as ACC, UCS, UCEC, STAD, OV, LUAD, UCEC, STAD, READ, and HNSC.This implies that patients with high PCMT1 expression may be better candidates for immunotherapy. Notably, the influence of PCMT1 on tumor prognosis and response to immunotherapy is inconsistent across various tumor types, with the exception of LUSC. This variability may be attributed to differences in sample sizes across various databases, necessitating further investigation to elucidate the underlying mechanisms.
To elucidate the clinical implications of PCMT1, we conducted a comprehensive anal- ysis of the associations between PCMT1 expression and responses to chemotherapy and immunotherapy. Our investigation across various cancer cell lines revealed that elevated PCMT1 expression is associated with decreased sensitivity to the EGFR inhibitors Afa- tinib, Gefitinib, and Trametinib. These findings imply that PCMT1 may enhance EGFR activity, thereby facilitating tumor progression and contributing to drug resistance. Notably, there is a paucity of research examining the relationship between PCMT1 and EGFR, highlighting a potential area for future exploration. Furthermore, our study demonstrated that increased PCMT1 levels enhance the activity of UNC0638, a specific G9A inhibitor. Previous studies have shown that G9A inhibitors are potent inducers of autophagy. Consequently, PCMT1 may amplify chemotherapy-induced autophagy, thereby augmenting the cytotoxic efficacy of chemotherapeutic agents.
To date, the mechanisms by which PCMT1 influences sensitivity to chemotherapy and immunotherapy remain largely unexplored. Furthermore, PCMT1 expression displays differential patterns across various cancer types in response to immunotherapy. Spe- cifically, patients with bladder cancer and melanoma exhibiting high levels of PCMT1 expression have shown favorable clinical responses to PD-1 immunotherapy. Con- versely, patients with GBM and KIRC with elevated PCMT1 expression have demon- strated resistance to PD-1 immunotherapy. Given the observed correlations between PCMT1 expression and clinical therapeutic outcomes, the development of targeted
pharmacological agents designed to modulate PCMT1 expression to enhance the effi- cacy of immunotherapy represents a promising area for further research.
To date, the mechanisms by which PCMT1 modulates sensitivity to chemotherapy and immunotherapy remain largely unexplored. What’s more, the expression of PCMT1 exhibited differential patterns across various cancer types in response to immuno- therapy. Specifically, patients with bladder cancer and melanoma who demonstrated high levels of PCMT1 expression showed favorable clinical responses to PD-1 immu- notherapy. In contrast, patients with GBM and KIRC with elevated PCMT1 expression exhibited resistance to PD-1 immunotherapy. Given the observed associations between PCMT1 expression and clinical therapeutic outcomes, the development of targeted pharmacological agents aimed at modulating PCMT1 expression to enhance the efficacy of immunotherapy represents a promising avenue for further investigation.
In the present study, we performed a comprehensive pan-cancer analysis of PCMT1, investigating its potential roles across various cancer types. However, it is important to acknowledge certain limitations inherent in our research. All analyses were based on publicly available data, with samples obtained retrospectively, which may introduce case selection bias and potentially affect the results. Therefore, the findings of this study necessitate validation through in vivo and in vitro experiments. Despite these limita- tions, our study, which utilized a diverse array of patient samples from multiple data- bases, provides novel perspectives and insights into cancer treatment. This research establishes a foundation for future investigations aimed at elucidating the potential role of PCMT1 in tumor immunity, encouraging further experimental exploration, and con- tributing to advancements in cancer therapy.
5 Limitations
However, it is important to acknowledge several inherent limitations in our study. First, all analyses were conducted using publicly available retrospective datasets (e.g., TCGA, GTEx, GDSC), which may introduce selection bias and limit the generalizability of our findings. Second, PCMT1 expression was evaluated based on bulk RNA-seq data, lacking single-cell or spatial resolution, which restricts insights into cell-type-specific expression patterns. Third, although we validated our findings using protein-level data from CPTAC and HPA, direct experimental validation such as Western blot or immuno- histochemistry on matched tumor samples was not performed, limiting the robustness of the expression-prognosis correlation. Fourth, immune infiltration analyses relied on computational deconvolution algorithms, which are inferential and may not fully cap- ture the complexity of the tumor immune microenvironment. Fifth, drug sensitivity pre- dictions were based on cancer cell line data, which may not accurately reflect in vivo drug responses or account for tumor heterogeneity and microenvironmental factors. Finally, our study identifies correlations rather than causal relationships; thus, the func- tional role of PCMT1 in tumor progression, immune evasion, and therapeutic resistance requires further validation through in vitro and in vivo functional assays. Prospective clinical studies are also needed to confirm the clinical utility of PCMT1 as a prognostic or predictive biomarker.
6 Conclusion
In summary, we performed a comprehensive pan-cancer analysis to elucidate the roles of PCMT1 across various cancers using data from public databases. Our findings revealed that PCMT1 is highly expressed in most epithelial-origin tumors, with the exceptions of CHOL and KIRC. Furthermore, PCMT1 expression was significantly associated with cancer prognosis, immune cell infiltration, and therapeutic response in multiple cancer types. These results suggest that PCMT1 may serve as a potential prognostic biomarker and a novel target for enhancing the sensitivity of immunotherapy in various cancers, particularly through its interaction with CAFs and immunosuppressive pathways. Con- versely, in mesenchymal-origin tumors, such as SARC and GBM, there appears to be no significant difference in PCMT1 expression or prognostic value, indicating that the role of PCMT1 in these cancer types warrants further investigation.
Author contributions
B.Wang, R. Yao, E. Kou, H. Zhu and H. Zhao analysed and discussed the data. B.Wang, S. Huang, and R.Ren wrote the paper. M. Zhang, L. Wang and Y. Zhu revised the paper. All authors have approved the final version of this manuscript.
Funding
This research was funded by the Shanghai Collaborative Innovation Project, grant number XTCX-KJ-2023-44.
Data availability
The datasets generated and/or analysed during the current study are available in UCSC Xena (https://xenabrowser.net/d atapages/).
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 19 August 2025 / Accepted: 25 December 2025
Published online: 05 January 2026
References
1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN esti- mates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-49.
2. Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. CA Cancer J Clin. 2025;75(1):10-45.
3. Santucci C, Carioli G, Bertuccio P, Malvezzi M, Pastorino U, Boffetta P, et al. Progress in cancer mortality, incidence, and survival: a global overview. Eur J Cancer Prev. 2020;29(5):367-81.
4. Torre LA, Siegel RL, Ward EM, Jemal A. Global cancer incidence and mortality rates and trends-an update. Cancer Epide- miol Biomarkers Prev. 2016;25(1):16-27.
5. Zhang Y, Zhang Z. The history and advances in cancer immunotherapy: understanding the characteristics of tumor- infiltrating immune cells and their therapeutic implications. Cell Mol Immunol. 2020;17(8):807-21.
6. Cheng B, Lv J, Xiao Y, Song C, Chen J, Shao C. Small molecule inhibitors targeting PD-L1, CTLA4, VISTA, TIM-3, and LAG3 for cancer immunotherapy (2020-2024). Eur J Med Chem. 2025;283:117141.
7. Huang FJ, Fang YY, Wen JY, Li JJ, Lin Q, Su QY, et al. From PD-1/PD-L1 to tertiary lymphoid structures: paving the way for precision immunotherapy in cholangiocarcinoma treatment. Hum Vaccin Immunother. 2025;21(1):2444697.
8. Rui R, Zhou L, He S. Cancer immunotherapies: advances and bottlenecks. Front Immunol. 2023;14:1212476.
9. Saleh R, Elkord E. Acquired resistance to cancer immunotherapy: role of tumor-mediated immunosuppression. Semin Cancer Biol. 2020;65:13-27.
10. Desrosiers RR, Fanélus I. Damaged proteins bearing L-isoaspartyl residues and aging: a dynamic equilibrium between generation of isomerized forms and repair by PIMT. Curr Aging Sci. 2011;4(1):8-18.
11. González-Recio I, Goikoetxea-Usandizaga N, Rejano-Gordillo CM, Conter C, Rodríguez Agudo R, Serrano-Maciá M, et al. Modulatory effects of CNNM4 on protein- l -isoaspartyl- O -methyltransferase repair function during alcohol-induced hepatic damage. Hepatology. 2024. https://doi.org/10.1097/HEP.0000000000001156.
12. Furuchi T, Homma H. Role of isomerized protein repair enzyme, PIMT, in cellular functions. Yakugaku Zasshi. 2007;127(12):1927-36.
13. Simko V, Belvoncikova P, Csaderova L, Labudova M, Grossmannova K, Zatovicova M, et al. PIMT binding to C-terminal Ala459 of CAIX is involved in inside-out signaling necessary for its catalytic activity. Int J Mol Sci. 2020. https://doi.org/10.3 390/ijms21228545.
14. DeVry CG, Clarke S. Polymorphic forms of the protein L-isoaspartate (D-aspartate) O-methyltransferase involved in the repair of age-damaged proteins. J Hum Genet. 1999;44(5):275-88.
15. Jia Y, Liu N, Viswakarma N, Sun R, Schipma MJ, Shang M, et al. PIMT/NCOA6IP deletion in the mouse heart causes delayed cardiomyopathy attributable to perturbation in energy metabolism. Int J Mol Sci. 2018. https://doi.org/10.3390/ijms19051 485.
16. Yang H, Lowenson JD, Clarke S, Zubarev RA. Brain proteomics supports the role of glutamate metabolism and sug- gests other metabolic alterations in protein l-isoaspartyl methyltransferase (PIMT)-knockout mice. J Proteome Res. 2013;12(10):4566-76.
17. Dong L, Li Y, Xue D, Liu Y. PCMT1 is an unfavorable predictor and functions as an oncogene in bladder cancer. IUBMB Life. 2018;70(4):291-9.
18. Saito H, Yamashita M, Ogasawara M, Yamada N, Niisato M, Tomoyasu M, et al. Chaperone protein L-isoaspartate (D-aspar- tyl) O-methyltransferase as a novel predictor of poor prognosis in lung adenocarcinoma. Hum Pathol. 2016;50:1-10.
19. Zhang J, Li Y, Liu H, Zhang J, Wang J, Xia J, et al. Genome-wide CRISPR/Cas9 library screen identifies PCMT1 as a critical driver of ovarian cancer metastasis. J Exp Clin Cancer Res. 2022;41(1):24.
20. Guo J, Du X, Li C. PCMT1 is a potential prognostic biomarker and is correlated with immune infiltrates in breast cancer. Biomed Res Int. 2022;2022:4434887.
21. Zhang K, Li JY, Li K. Silencing PCMT1 enhances the sensitivity of breast cancer cells to paclitaxel through the PI3K/Akt/ STMN1 pathway. Chem Biol Drug Des. 2024;103(6):e14559.
22. Deng X, Ma N, He J, Xu F, Zou G. The role of TGFBR3 in the development of lung cancer. Protein Pept Lett. 2024;31(7):491-503.
23. Ye W, Wang J, Zheng J, Jiang M, Zhou Y, Wu Z. Association between higher expression of Vav1 in hepatocellular carcinoma and unfavourable clinicopathological features and prognosis. Protein Peptide Lett. 2024;31(9):706-13.
24. Zhang H, Zhang G, Xu P, Yu F, Li L, Huang R, et al. Optimized dynamic network biomarker deciphers a high-resolution heterogeneity within thyroid cancer molecular subtypes. Med Res. 2025. https://doi.org/10.1002/mdr2.70004.
25. Li Q, Chu Y, Yao Y, Song Q. FAT4 mutation is related to tumor mutation burden and favorable prognosis in gastric cancer. Curr Genomics. 2024;25(5):380-9.
26. Ke X, Li K, Jiang A, Zhang Y, Wang Q, Li Z, et al. Cloud-based GWAS platform: an innovative solution for efficient acquisition and analysis of genomic data. Med Res. 2025. https://doi.org/10.1002/mdr2.70040.
27. Lakins MA, Ghorani E, Munir H, Martins CP, Shields JD. Cancer-associated fibroblasts induce antigen-specific deletion of CD8 (+) T cells to protect tumour cells. Nat Commun. 2018;9(1):948.
28. Mao X, Xu J, Wang W, Liang C, Hua J, Liu J, et al. Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives. Mol Cancer. 2021;20(1):131.
29. Ding X, Liu H, Yuan Y, Zhong Q, Zhong X. Roles of GFPT2 expression levels on the prognosis and tumor microenvironment of colon cancer. Front Oncol. 2022;12:811559.
30. Bonneville R, Krook MA, Kautto EA, Miya J, Wing MR, Chen HZ, et al. Landscape of microsatellite instability across 39 cancer types. JCO Prec Oncol. 2017;2017:1.
31. Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN, et al. Machine learning identifies stemness fea- tures associated with oncogenic dedifferentiation. Cell. 2018;173(2):338-54 e15.
32. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, et al. Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013;41 (Database issue):D955-61.
33. Liu CJ, Hu FF, Xie GY, Miao YR, Li XW, Zeng Y, et al. GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels. Brief Bioinform. 2023. https://doi.org/10.1093/bib/bbac558.
34. Arneth B. Tumor microenvironment. Medicina (Kaunas). 2019;56:15-24.
35. Shan L, Wang X, Li Y, Li L, Wu S, Xi X, et al. Elevated expression of protein-L-isoaspartate O-methyltransferase-1 (PCMT1) in cervical cancer. Transl Cancer Res. 2022;11(8):2582-90.
36. Belkourchia F, Desrosiers RR. The protein L-isoaspartyl (D-aspartyl) methyltransferase regulates glial-to-mesenchymal transition and migration induced by TGF-ß1 in human U-87 MG glioma cells. Int J Mol Sci. 2022. https://doi.org/10.3390/ij ms23105698.
37. Xia J, Hou Y, Wang J, Zhang J, Wu J, Yu X, et al. Repair of isoaspartyl residues by PCMT1 and kidney fibrosis. J Am Soc Nephrol. 2025;36(7):1278-94.
38. Hipp MS, Park SH, Hartl FU. Proteostasis impairment in protein-misfolding and -aggregation diseases. Trends Cell Biol. 2014;24(9):506-14.
39. Thompson SM, Craven RA, Nirmalan NJ, Harnden P, Selby PJ, Banks RE. Impact of pre-analytical factors on the proteomic analysis of formalin-fixed paraffin-embedded tissue. Proteom Clin Appl. 2013;7(3-4):241-51.
40. Suvac A, Ashton J, Bristow RG. Tumour hypoxia in driving genomic instability and tumour evolution. Nat Rev Cancer. 2025;25(3):167-88.
41. Chaudhary S, Siddiqui JA, Pothuraju R, Bhatia R. Ribosome biogenesis, altered metabolism and ribotoxic stress response in pancreatic ductal adenocarcinoma tumor microenvironment. Cancer Lett. 2025;612:217484.
42. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of inci- dence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394-424.
43. Synnott NC, Murray A, McGowan PM, Kiely M, Kiely PA, O’Donovan N, et al. Mutant p53: a novel target for the treatment of patients with triple-negative breast cancer? Int J Cancer. 2017;140(1):234-46.
44. Liu Y, Liu Z, Yang Y, Cui J, Sun J, Liu Y. The prognostic and biology of tumour-infiltrating lymphocytes in the immunother- apy of cancer. Br J Cancer. 2023;129(7):1041-9.
45. Tanigawa K, Redmond WL. Current landscape and future prospects of interleukin-2 receptor (IL-2R) agonists in cancer immunotherapy. Oncoimmunology. 2025;14(1):2452654.
46. Khalil RG, Mohammed DA, Hamdalla HM, Ahmed OM. The possible anti-tumor effects of regulatory T cells plasticity / IL-35 in the tumor microenvironment of the major three cancer types. Cytokine. 2025;186:156834.
47. Huntington ND, Cursons J, Rautela J. The cancer-natural killer cell immunity cycle. Nat Rev Cancer. 2020;20(8):437-54.
48. Zhang P, Zhang M, Liu J, Zhou Z, Zhang L, Luo P, et al. Mitochondrial pathway signature (MitoPS) predicts immunotherapy response and reveals NDUFB10 as a key immune regulator in lung adenocarcinoma. J Immunother Cancer. 2025. https://d oi.org/10.1136/jitc-2025-012069.
49. Jiang M, Zhu D, Zhao D, Liu Y, Li J, Zheng Z. Integrated analysis of clinical outcome of mesenchymal stem cell-related genes in pan-cancer. Curr Genomics. 2024;25(4):298-315.
50. Kim CG, Ahn JB, Jung M, Beom SH, Kim C, Kim JH, et al. Effects of microsatellite instability on recurrence patterns and outcomes in colorectal cancers. Br J Cancer. 2016;115(1):25-33.
51. Marrelli D, Polom K, Pascale V, Vindigni C, Piagnerelli R, De Franco L, et al. Strong prognostic value of microsatellite instabil- ity in intestinal type non-cardia gastric cancer. Ann Surg Oncol. 2016;23(3):943-50.
52. Yoo SK, Fitzgerald CW, Cho BA, Fitzgerald BG, Han C, Koh ES, et al. Prediction of checkpoint inhibitor immunotherapy effi- cacy for cancer using routine blood tests and clinical data. Nat Med. 2025. https://doi.org/10.1038/s41591-024-03398-5.
53. Vickram S, Infant SS, Manikandan S, Jenila Rani D, Mathan Muthu CM, Chopra H. Immune biomarkers and predictive signatures in gastric cancer: optimizing immunotherapy responses. Pathol Res Pract. 2025;265:155743.
54. Liu Y, Hu P, Xu L, Zhang X, Li Z, Li Y, et al. Current progress on predictive biomarkers for response to immune checkpoint inhibitors in gastric cancer: how to maximize the immunotherapeutic benefit? Cancers (Basel). 2023. https://doi.org/10.33 90/cancers 15082273.
55. Huang D, Li J, He Z, Liang W, Zhong L, Huang J, et al. Pan-cancer and experimental analyses reveal the immunotherapeu- tic significance of CST2 and its association with stomach adenocarcinoma proliferation and metastasis. Front Immunol. 2024;15:1466806.
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