The role of PCMT1 in prognosis tumor immune microenvironment and therapeutic responses across cancers

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.

Conclusion

Multi-omics analysis suggests that PCMT1 may serve as a potential prognostic biomarker and a novel immunotherapy target for pan-cancer.

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 proteins, such as PD-1 and CTLA-4, can reinvigorate the immune system’s ability to recognize 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 various 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 isomerization, 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 progression, 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, influencing cell migration and invasion by modulating EMT-related genes, including E-cadherin, 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 proliferation and invasion [22], its systematic role in shaping the tumor immune microenvironment (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 cytokines [24]. For instance, a recent multi-omics study demonstrated that FAT4 mutations 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 unexplored [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 analysis 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 conducted 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 various cancers and provide novel insights into its role in tumor immunity and therapeutic responses.

Materials and methods

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. Normal 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.

Table 1.

List of abbreviations for cancers

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

Expression analysis of PCMT1 in tumor tissues

We extracted gene expression data from the TCGA and GTEx databases using the command-line tool wget, subsequently merging and normalizing the data using the normalizeBetweenArrays 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 relationship 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 tissues 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" 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.

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 Progression-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.

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 fibroblasts (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 previous 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 (10.1016/j.cell.2018.03.034/attachment/37f8d8d0-af00-404b-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.

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, glioblastoma, 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 (complete/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, survival risk, and cytotoxic T lymphocyte (CTL) presence across various cohorts undergoing immunotherapy, utilizing the "Query Gene" module on the TIDE website.

Results

Features of PCMT1 expression in tumor tissues

Firstly, we assessed PCMT1 expression combined using TCGA and GTEx data. Compared 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 differential 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 Kidney 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. Notably, 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).

Fig. 1.

Differences in RNA and protein expression levels of PCMT1 in different tumors. A Differential expression of PCMT1 mRNA between tumor group and normal group in TCGA and GTEx databases. B Differential expression of PCMT1 protein between normal tissue and BRCA, COAD, GBM, HNSC, KIRC, LIHC, LUAD, OV, PAAD and UCEC tissues. Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples. (*p < 0.05, ** p < 0.01, ***p < 0.001)

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 expressions (Fig. 2).

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

Subsequently, cBioPortal was utilized to investigate the genetic alterations of PCMT1 across various tumor types. Notably, uveal melanoma patients exhibited the highest frequency 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).

Fig. 3.

Gene alteration of PCMT1 in pan-cancer. A The proportion of various alteration types of PCMT1 in different tumors. B The mutated site of PCMT1 in pan-cancer by lollipop

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.

Fig. 4.

Survival forest plot based on univariate Cox regression analysis. A Overall Survival (OS), B Progression Free Survival (PFS), C Disease Free Survival (DFS), and D Disease Specific Survival (DSS). Items highlighted in yellow indicate that PCMT1 expression was negatively correlated with the survival indicator and items highlighted in blue indicate that PCMT1 expression was positively correlated with the survival indicator (p < 0.05)

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 infiltration 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, macrophages, 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 expression. Figure 5B showed that the expression level of PCMT1 was associated with the infiltrations of 4 immunosuppressive cells (CAFs, TAMs, MDSCs, and Tregs). Although correlations between PCMT1 and CAF/M2-TAMs/Tregs were not completely consistent 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.

Fig. 5.

The correlation of PCMT1 expression with immune cells, immunosuppressive cells and factors, MSI, and TMB. A, B display the heat maps showing the association of PCMT1 expression with 6 immune cell types and 4 immunosuppressive cell types in different TCGA tumor types. C, D are the radar maps displaying the correlation between PCMT1 expression and two immune biomarkers (MSI and TMB). E presents the relationship between PCMT1 expression level and CAFs-associated/immunosuppressive factors. (*p < 0.05, **p < 0.01, ***p < 0.001)

Subsequently, we evaluate the correlations between PCMT1 and CAFs-associated factors and immunosuppressive factors. As shown in Fig. 5E, there were significant associations between PCMT1 and most CAFs-associated factors and immunosuppressive factors, suggesting the roles of PCMT1 in tumor microenvironment. There were significant positive correlations between PCMT1 and most CAFs-associated/immunosuppressive 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 expressions 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 negatively 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 negatively 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 correlated with RNAss only in KICH, while positively correlated with RNAss in 18 tumor types (Fig. 6B).

Fig. 6.

The association between PCMT1 expression and tumor stemness was visualized, including DNAss (A) and RNAss (B)

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).

Fig. 7.

Relationship between PCMT1 expression and treatment response. A The 30 drugs most associated with PCMT1 expression in the GDSC database. The bluer the circle, the more sensitive cells with high PCMT1 expression are to the drug. The size of the circle represents false detection Rate (FDR). The smaller the FDR, the more reliable the results are. B The ROC curves showing the association between chemotherapy response and PCMT1 expression in breast cancer, glioblastoma, and OV cohorts. C Kaplan–Meier analysis showing differences in response to immunotherapy between the PCMT1 high expression group and the PCMT1 low expression group. A simple linear regression model showing the correlation between PCMT1 expression and CTL in the indicated cohorts

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 website. In bladder cancer, GBM, KIRC and melanoma, relations between PCMT1 and prognosis and the relation between PCMT1 and CTL were consistent (Fig. 7C). The higher the CTL level, the better the patient’s prognosis.

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 correlation between PCMT1 and tumor progression [34]. In this study, we conducted a comprehensive 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 supporting resistance to apoptosis and therapy [35, 36]. However, research on PCMT1 in mesenchymal tumors, such as sarcomas, remains limited. The role of PCMT1 in sarcomas could potentially differ due to the unique protein repair demands of the microenvironment 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-damaged 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 contribute to the observed inconsistency [39]. Future studies integrating single-cell translatome 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 functional 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 increasingly supports the potential of therapies targeting mutated genes as a promising strategy 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 microenvironment 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, neutrophils, 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 biological 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-β and IL-6, thereby establishing physical and cytokine barriers that suppress CD8⁺ T-cell trafficking and confer resistance to PD-1 blockade [49]. In hepatocellular carcinoma, high CAF density has been shown to impair antigen presentation via TGF-β/Smad activation and to predict poor ICB response [22]. Integrating these data, we propose that PCMT1 may stabilize TGF-β 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 associated 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 immunotherapeutic biomarkers, TMB and MSI, both evaluated through comprehensive genomic profiling [50, 51]. These biomarkers are recognized predictors of immunotherapy efficacy [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 analysis 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 Afatinib, 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. Specifically, patients with bladder cancer and melanoma exhibiting high levels of PCMT1 expression have shown favorable clinical responses to PD-1 immunotherapy. Conversely, patients with GBM and KIRC with elevated PCMT1 expression have demonstrated 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 efficacy 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 immunotherapy. Specifically, patients with bladder cancer and melanoma who demonstrated high levels of PCMT1 expression showed favorable clinical responses to PD-1 immunotherapy. 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 limitations, our study, which utilized a diverse array of patient samples from multiple databases, 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 contributing to advancements in cancer therapy.

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 immunohistochemistry 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 capture the complexity of the tumor immune microenvironment. Fifth, drug sensitivity predictions 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 functional 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.

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. Conversely, 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/datapages/).

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.