
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12850-2
10.1016/j.heliyon.2024.e36819
e36819
Research Article
Pan-cancer exploration of PNO1: A prospective prognostic biomarker with ties to immune infiltration
Qin Yinhui a
Li Zhen b
Zhang Xianwei b
Li Junjun c
Teng Yuetai d
Zhang Na e
Zhao Shengyu f
Kong Lingfei klfhnrnyy@163.com
b⁎
Niu Weihong nwh1006@zzu.edu.cn
b⁎⁎
a Department of Pharmacy, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou, 450003, Henan, China
b Department of Pathology, Henan Key Laboratory for Digital Pathology Medicine, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou, 450003, Henan, China
c The Fourth Affiliated Hospital, Zhejiang University School of Medicine, No. N1 Shangcheng Avenue, Hangzhou, 310058, Zhejiang, China
d Department of Pharmacy, Jinan Vocational College of Nursing, Jinan, 250102, China
e Shandong Academy of Chinese Medicine, Jinan, 250014, China
f Shenyang Pharmaceutical University, Shenyang, 110016, China
⁎ Corresponding author. klfhnrnyy@163.com
⁎⁎ Corresponding author. nwh1006@zzu.edu.cn
23 8 2024
15 9 2024
23 8 2024
10 17 e368194 9 2023
21 8 2024
22 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The partner of NOB1 homolog (PNO1) is an RNA-binding protein that participates in ribosome biogenesis and protein modification. The functions of this molecule are largely unknown in cancers, particularly breast cancer. We employed bioinformatics methods to probe the putative oncogenic functions of PNO1 based on expression profiles and clinical data from the cancer genome atlas (TCGA), genotype-tissue expression project (GTEx), human protein atlas (HPA), cancer cell line encyclopedia (CCLE), UALCAN, drug sensitivity in cancer (GDSC) and UCSC XENA databases. Our analyses revealed that PNO1 was overexpressed in 31 malignancies, which excluded kidney chromophobe (KICH) and acute myeloid leukemia (LAML). Prognostic assessments have demonstrated that high PNO1 expression was significantly correlated with poor overall and disease-specific survival in various cancers. The promoter methylation level of PNO1 is significantly decreased in breast invasive carcinoma (BRCA), head and neck squamous cell carcinoma (HNSC), kidney renal papillary cell carcinoma (KIRP), prostate adenocarcinoma (PRAD), thyroid carcinoma (THCA) and uterine corpus endometrial carcinoma (UCEC). Furthermore, inhibition of PNO1 decreased the viability, migration and invasion of breast cancer cells, and these results were confirmed by mouse xenograft models of breast cancer. In addition, we discovered that tumor microenvironment (TME), immune infiltration, and chemotherapy sensitivity were influenced by PNO1 expression. Concordantly, our analyses revealed a significant positive correlation between PNO1 and programmed cell death ligand 1 (PD-L1) expression across breast carcinoma samples. In conclusion, these findings indicate that PNO1 could act as a promising prognostic biomarker and adjunct diagnostic indicator, because it affects tumor growth and invasion. Our study offers valuable new perspectives on the oncogenic role of PNO1 in various types of cancers.

Keywords

PNO1
Breast cancer
Prognosis
Immune infiltration
Biomarker
==== Body
pmc1 Introduction

Burgeoning global population growth and aging have made cancer a primary driver of mortality. Despite advances in tumor therapeutics, patient prognosis remains poor [1]. Ribosomes, macromolecular ribonucleoprotein assemblies essential for mRNA translation into proteins across organisms, are indispensable for cellular growth and proliferation, and are intimately associated with fundamental biological processes, particularly tumorigenesis [2]. PNO1, also known as Dim2 or Rrp2, participates in ribosome and proteasome biogenesis in yeast. PNO1 chaperone NOB1 is involved in 26S proteasome formation. Previous studies have indicated that PNO1 loss in yeast impairs cytoplasmic maturation and ribosome synthesis [3,4]. Increasing evidence suggests that PNO1 is upregulated in colorectal cancer (CRC), esophageal cancer, lung adenocarcinoma, hepatocellular carcinoma (HCC), urinary bladder carcinoma (UCC), and glioma. PNO1 exerts oncogenic effects through critical roles in ribosome biogenesis, cell proliferation, autophagy, and apoptosis [[5], [6], [7], [8], [9], [10]]. Moreover, studies have indicated that high PNO1 expression correlates with poor prognosis and that PNO1 inhibition disrupts global ribosome biogenesis in CRC [11,12]. Recent studies have revealed that PNO1 suppresses HCC cell apoptosis by enhancing autophagy through the MAPK pathway, thereby promoting HCC progression [13,14].

To date, no systematic pan-cancer analysis of PNO1 has been conducted. In this study, we performed an extensive pan-cancer analysis of PNO1 using data from multiple sources including TCGA, CCLE, HPA, cBioPortal, and GTEx. We comprehensively assessed the PNO1 expression in normal human tissues, tumor tissues, and various cell lines. Furthermore, we investigated the associations between PNO1 expression and survival prognosis, DNA methylation, and gene copy number alterations (CNA) across 33 cancer types. Next, we analyzed the impact of PNO1 expression on TME and the relationship between PNO1 and various immune biomarkers. The biological effects of PNO1 on breast cancer cells were determined using both in vitro and in vivo assays. Finally, interaction and competing endogenous RNA (ceRNA) networks of PNO1 were predicted and constructed. Our results revealed a putative role for PNO1 in tumorigenesis and progression of multiple cancers. Collectively, our data suggest that PNO1 can be used as a new prognostic marker for breast cancer and may facilitate tumor development and advancement.

2 Materials and methods

2.1 Data acquisition and analysis

The present study sourced data on PNO1 expression from three prominent sources: TCGA, GTEx, and CCLE, along with supplementary clinical data obtained from the UCSC XENA website. We investigated the expression levels of PNO1 in a diverse range of 31 normal tissues and 33 distinct tumor types.

2.2 Pan-cancer analysis of PNO1 expression levels and survival prognosis

To explore the role of PNO1 expression levels across various cancer types. To analyze the differential mRNA expression of PNO1 in various tumor cells and normal tissues, the R packages edgeR and ggplot was used to process data from TCGA and GTEx databases. To further examine the protein expression of PNO1 in normal and tumor tissues, we used the web-based platform UALCAN to access data from the CPTAC and ICPC datasets [15]. Moreover, to evaluate the prognostic significance of PNO1 expression levels, we categorized pan-cancer samples into high and low expression groups based on the median gene expression value and used forest plots and Kaplan-Meier analysis to assess the overall survival (OS) and disease-specific survival (DSS) of patients [16]. Finally, we performed a univariate Cox regression analysis to calculate the hazard ratios (HRs) with 95 % confidence intervals (CI).

2.3 Correlation between PNO1 expression and DNA methylation

DNA methylation represents a crucial epigenetic modification that orchestrates gene expression and is implicated in the etiology of cancer. In this study, we utilized the UALCAN platform to conduct a thorough and integrated analysis of the TCGA dataset. The latest 2022 update to UALCAN has introduced functionalities for examining DNA methylation at initiation sites [15,17]. Capitalizing on this advancement, our research focused on assessing the methylation status of the PNO1 gene promoter across 19 distinct cancer types.

2.4 Correlation analysis of PNO1 with immune infiltration and the TME

We conducted a comprehensive analysis to elucidate the relationship between PNO1 expression and tumor-infiltrating immune cells using the CIBERSORT method in R. Data from the ImmuCellAI database, which included 24 immune cell subtypes in patients with various types of cancer, we embarked on a robust correlation analysis. Pearson's correlation test was used to determine the correlation between PNO1 expression and 18 T-cell subtypes, as well as 6 other immune cell types including B cells, NK cells, monocytes, macrophages, neutrophils, and dendritic cells (DC).

Subsequently, a co-expression analysis of PNO1 and immune-related genes was conducted, which included genes encoding major histocompatibility complex (MHC) proteins, immune activation, and immunosuppressive proteins, as well as chemokine and chemokine receptor proteins. A heatmap was created to visualize the results, with the strength of the correlation indicated by the intensity of the color, where red indicated a positive correlation and blue indicated a negative correlation. Further, TME-related scores of patients for 33 types of tumors, including immune signature (TMEscore A), stromal activation signature (TMEscore B), and mismatch DNA repair (MMR) signatures, were assessed using the ESTIMATE algorithm. Another heatmap was used to show the correlation between PNO1 expression levels and TME-related scores [18].

2.5 Target miRNA prediction and ceRNA network construction

In our quest to decipher the regulatory mechanisms influencing PNO1 expression, we predicted the targets of differentially expressed miRNAs using three databases, namely miRWalk (http://mirwalk.umm.uni-heidelberg.de/), DIANA-microT (http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=microT_CDS/index) and miRDB (http://mirdb.org/miRDB/). Building upon this foundation, we further explored the intricate web of non-coding RNAs associated with PNO1. Through starBase v2.0 (https://starbase.sysu.edu.cn/), we identified relevant miRNA targets, as well as long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) that participate in the complex regulatory network surrounding PNO1. To visualize this network, we employed Cytoscape 3.9.1, which enabled us to construct a detailed lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network. This graphical representation illuminates the multifaceted interactions at play, providing a clearer understanding of the non-coding RNA dynamics that influence PNO1 expression.

2.6 Interaction of PNO1 with genes and chemicals

The GeneMANIA database (http://www.genemania.org) is a user-friendly website that can be used to predict the major biological functions and networks of genes based on a wealth of genomics and proteomics data [19]. The Sandbox platform (http://vip.sangerbox.com), an interactive integrated clinical information analysis tool, facilitated our examination of the correlations between PNO1 and RNA-modified genes. Specifically, we focused on genes involved in N1-methyladenosine (m1A), 5-methylcytosine (m5C), and N6-methyladenosine (m6A) modifications, drawing upon data from TCGA and GTEx databases [20]. In a parallel analysis, we analyzed the relationship between gene expression and drug response (half maximal inhibitory concentration (IC50) data for 198 cytotoxic drugs using information from the Genomics of GDSC database (https://www.cancerrxgene.org/).

2.7 Patients and samples

Breast cancer tissues and paired precancerous tissues (normal) were surgically resected from 65 patients and were obtained from September 2020 to April 2021 at People's Hospital of Zhengzhou University. Patients with breast cancer diagnosed according to clinical and histological diagnostic criteria for breast cancer in China [21].

2.8 Cell lines and constructs

Human breast cancer cell lines MCF7 and MDA-MB-231(MDA231) were purchased from American Type Culture Collection (ATCC) and were cultured in Dulbecco's Modified Eagle's Medium (DMEM) supplemented with 10 % inactivated fetal bovine serum and 1 % penicillin-streptomycin (Gibco, China). The culture conditions were 37 °C, 95 % humidity, and 5 % CO2 in a constant temperature incubator. To construct a plasmid overexpressing PNO1, the total length sequence of PNO1 was inserted into a pCDNA3.1(+) vector in Qingke Biotechnology Co., Ltd. PNO1 small interfering RNAs (siRNAs; siPNO1#1 and siPNO1#2) were purchased from RiboBio (Guangdong, China). The sequences of the PNO1 siRNAs were as follows: PNO1 siRNA#1:5′-CAGCTAACAGATACACACCATTGAA-3’; PNO1 siRNA#2:5′-CCAAGGATGTTAGTGCTCTGACAAA-3’.

2.9 Cell proliferation and cell cycle

Cell viability was determined by the CCK8 assay using the Cell Counting Kit-8 (CCK8, Dojindo Molecular Technologies). Cells were meticulously plated in 96-well plates containing 100 μL of culture medium at a density of 1000 cells/well. To evaluate the cell viability, the medium was removed and each well was added with 100 μL of fresh medium supplemented with 10 μL of CCK8 (cell counting kit-8, Dojindo, Japan) at predetermined time (0, 1, 2, 3, and 4 days). Subsequent to a 2-h incubation at 37 °C with 5 % CO2, absorbance was measured at 450 nm.

For cell cycle analysis, cells at logarithmic growth stage were harvested and centrifuged at 800 rpm for 5 min. The supernatant was discarded, and the cells were washed twice with chilled PBS, followed by fixation in pre-cooled 75 % ethanol at 4 °C for 24 h. After centrifugation, the supernatant was discarded and was then washed twice with PBS. Then the cells were digested with 10 μg/mL RNase A at 37 °C for 30 min and stained with 10 mg/mL propidium iodide (PI) at room temperature for 30 min in the dark. The final analysis was conducted using a Beckman Flow Cytometer (Beckman, Fullerton, CA, USA).

2.10 Wound healing assay

A monolayer of breast cancer cells was established in 6-well plates and wounded with a sterile 10 μL pipette tip. Cells were cultured in serum-free medium. The cells were then cultured in serum-free medium to assess wound closure. The scratch area was quantified, and cell images were captured at 0, 36, and 48 h. The wound healing rate was computed as follows: wound healing rate (%) = (Initial scratch area - Final scratch area)/Initial scratch area × 100 %.

2.11 Transwell assays

Cellular invasiveness was evaluated using a Transwell assay. Matrigel-coated Transwell chambers (Costar, USA) with 8 μm pore size (Corning Inc., Tewksbury, MA, USA) were used for this assay. Cells were seeded in the upper chambers of a serum-free medium, whereas the lower chambers were filled with DMEM supplemented with 20 % FBS. After incubation, the cells that had invaded through the matrigel and the membrane were fixed with 4 % paraformaldehyde and stained with crystal violet. The number of invading cells was quantified by counting five random fields per chamber under a microscope.

2.12 CD8+ T cell cytotoxicity assay

CD8+ T cells were isolated from the peripheral blood of healthy individuals using a PBMC separation reagent (FACs, Nanjing, China) and CD8 microbeads (Miltenyi, Germany), cultured in RPMI-1640 medium, and activated with CD3 antibodies (Invitrogen, USA), CD28 antibodies (Invitrogen, USA), and interleukin-2 (IL-2, R&D Systems, USA) for 72 h. Breast cancer cells with PNO1 knockdown were seeded in 96-well plates. Activated CD8+ T cells were introduced into these cancer cells and co-cultured for an interval of 4–6 h. Following the co-culture period, a crystal violet assay was performed, and the absorbance at 570 nm was quantified to determine the survival fraction of the cancer cells.

2.13 Immunohistochemistry

The protein expression of PNO1 was detected using an automated immunohistochemical (IHC) staining machine (Roche, Burgess Hill, UK) in 65 pairs of breast cancer and paracancerous tissues collected at the People's Hospital of Zhengzhou University. The average grey value (staining intensity) of positive cells and the percentage of positive area (stained area) were used as IHC measurement indicators to determine the product of the two. Staining intensity was scored as 0 (negative), 1 (weak), 2 (moderate), and 3 (strong). The color area was judged as 0–10 % is 1, 11–50 % as 2, 51–80 % as 3, and >80 % as 4. Images were captured using a Olympus-BX53 microscope (Olympus).

2.14 Mouse model

Five-week-old BALB/c nude mice were purchased from Beijing Vital River Laboratory Animal Technology for the experiment. Approximately 3 × 106 MDA231 cells transfected with Lv-shNC or Lv-shPNO1 were suspended in 150 μL PBS and subcutaneously injected in the dorsal flanks of the mice. The study involved measuring tumors in mice every 4 days and the tumor volume was calculated as length × width2. Following a 24-day observation period, the mice were euthanized and tumor tissues were fixed in 4 % paraformaldehyde, paraffin-embedded, and sectioned at 3 μm thickness for further analysis.

IHC staining was performed according to established protocols, as previously described [22]. PNO1 (1: 150 dilution; Proteintech, 21059-1-AP), Ki67(Shanghai Abways Biotechnology Co., Ltd., RMA-0731), CyclinD1 (Fuzhou Maixin Biotech.Co.Ltd., RMA-0541) and CD34 (Fuzhou Maixin Biotech.Co.Ltd. Kit-0004) antibodies were used in our study. Images were acquired using an Olympus-BX53 microscope (Olympus, Tokyo, Japan). The immunohistochemical staining intensity and area were quantified by measuring the average grey value of positive cells and the percentage of positive area on the images, respectively, using Image J software (National Institutes of Health, Bethesda, MD, United States) [23].

2.15 Statistical analysis

Data were analyzed using GraphPad Prism and SPSS software. For normally distributed data, Student's t-test facilitated intergroup comparisons. ROC analysis was conducted using the pROC package in R (v4.2.1) and visualization was achieved using ggplot2. Pearson's chi-square test was used to assess the association between the clinicopathological characteristics and PNO1 expression. Survival outcomes were visualized using Kaplan–Meier curves with Cox proportional hazards models for univariate and multivariate analyses. Significance is indicated by *P < 0.05, **P < 0.01, and ***P < 0.001.

3 Results

3.1 Abnormally high PNO1 expression in various tumors, including BRCA

Using the GTEx data set, we analyzed the PNO1 expression levels in 31 types of normal human tissues under physiological conditions (Fig. 1A). From low to high, overall PNO1 expression is not high in normal tissues, the lowest expression levels occurred in the blood, and the highest expression levels occurred in the bone marrow. Next, we analyzed PNO1 expression across 33 different types of cancer and ranked them in the order of increasing expression levels (Fig. 1B). PNO1 is expressed across all types of tumors, with the greatest degree of expression observed in KICH and the lowest degree of expression in LUSC. We conducted a comparative analysis of PNO1 expression levels in 33 cancer patients using matched normal samples obtained from TCGA and GTEx datasets. According to the analysis of available data, the expression of PNO1 in various tumors was significantly altered compared to that in normal tissues. Specifically, in bladder urothelial carcinoma (BLCA), BRCA, cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), HNSC, kidney renal clear cell carcinoma (KIRC), KIRP, brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), PRAD, rectum adenocarcinoma (READ), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), testicular germ cell tumors (TGCT), thymoma (THYM), UCEC, and uterine carcinosarcoma (UCS), PNO1 expression was observed to be higher than in normal tissues, after excluding tumors with no or little normal tissue data. However, the expression of PNO1 was shown to be lower in KICH and LAML (Fig. 1C). A noteworthy increase in PNO1 expression was found in BRCA, CHOL, COAD, ESCA, HNSC, KIRC, LIHC, LUAD, LUSC, READ, STAD, and THCA, which was also observed when paired tumor samples were compared to corresponding normal samples; however, PNO1 expression was decreased in KICH (Fig. 1D). Our analysis, using the online resource UALCAN, revealed a significant increase in PNO1 protein expression across various tumor tissues, such as BRCA, COAD, OV, clear cell renal cell carcinoma (ccRCC), UCEC, LUAD, HNSC, GBM, and LIHC, compared to normal tissues (Fig. 1E). Moreover, we analyzed PNO1 expression levels in paired tumor and normal tissues as well as at various clinical TNM stages. Our findings show that PNO1 expression is significantly higher in paired tumor tissues than in normal tissues for several cancers, including BRCA, CHOL, COAD, ESCA, HNSC, LIHC, LUAD, LUSC, READ, and STAD, as evidenced by the results obtained from TCGA database (Supplementary Fig. S1). In addition, a significant increase in PNO1 expression levels was observed as the clinical stage progressed in several cancers, including KICH, LUAD, HNSC, and PAAD (Supplementary Fig. S2).Fig. 1 Expression of PNO1 in normal tissue and different tumors. (A) Expression of PNO1 gene in various normal human tissues. (B) PNO1 expression profile in 33 types of cancer. (C) Comparison of PNO1 expression between tumor and normal samples (Based on TCGA and GTEX Databases). (D) PNO1 expression levels in paired normal and tumor tissues. (E) PNO1 protein expression in various cancer types, including BRCA, colon cancer, ovarian cancer, clear cell RCC, UCEC, lung cancer, pancreatic cancer, head and neck, glioblastoma and liver cancer in UALCAN. *P < 0.05, **P < 0.01, ***P < 0.001.

Fig. 1

3.2 Prognostic implications of PNO1 expression in multiple cancer types

To investigate the expression levels and prognostic role of PNO1 in various cancers, we assessed the expression and prognostic impact of PNO1 in various cancers using survival analysis. We employed a Cox proportional hazards model to investigate the association between PNO1 expression and overall survival (OS) in various cancer types. Based on our findings, elevated PNO1 expression levels significantly correlated with poorer OS in patients with ACC, BRCA, HNSC, LGG, LIHC, and LUAD. Conversely, in patients with KIRC and READ, increased PNO1 expression levels were shown to be inversely correlated with OS (Fig. 2A). Furthermore, a Kaplan-Meier analysis was conducted to support our findings. Our results showed that patients with high PNO1 expression exhibited worse OS in BRCA, ESCA, HNSC, LUAD, LIHC, KICH, PAAD, THCA, and UVM in OS (Fig. 2B). Regarding the association between PNO1 expression and DSS, forest plots showed that PNO1 high expression was correlated with poor DSS in ACC, BRCA, KICH, KIRP, LGG, LIHC, and PAAD (Supplementary Fig. S3A). Furthermore, Kaplan-Meier analysis revealed a positive correlation between PNO1 expression and poor DSS in patients with ACC, KICH, KIRP, PAAD, LIHC, MESO, and LGG (Supplementary Figs. S3B–H); however, in patients with LGG, low PNO1 expression was associated with poor DSS (Supplementary Fig. S3H). Fig. 2C illustrates the diagnostic performance of PNO1 for various cancers. PNO1 exceeded an AUC of 0.7 in 23 cancers. These cancers include BRCA (AUC = 0.758), CESC(AUC = 0.822), CHOL (AUC = 1.000), COAD (AUC = 0.938), CRC (AUC = 0.928), DLBC (AUC = 0.860), ESCA (AUC = 0.894), GBM (AUC = 0.993), LGG (AUC = 0.765), HNSC (AUC = 0.834), KICH (AUC = 0.921), LAML (AUC = 0.914), LIHC (AUC = 0.879), LUAD (AUC = 0.774), LUSC (AUC = 0.910), OA (AUC = 0.904), PAAD (AUC = 0.703), READ (AUC = 0.895), SARC (AUC = 0.753), THYM (AUC = 0.846), STAD (AUC = 0.955), TGCT (AUC = 0.878) and UCS (AUC = 0.885). These findings demonstrated that PNO1 is a reliable and accurate diagnostic marker for these cancers.Fig. 2 Correlation between PNO1 expression and Overall Survival (OS), and ROC analysis. (A) Forest plot. (B) Kaplan-Meier analysis diagram. (C) PNO1 ROC analysis in BRCA, CESC, CHOL, COAD, CRC, DLBC, ESCA, GBM, LGG, HNSC, KICH, LAML, LIHC, LUAD, LUSC, OA, PAAD, READ, SARC, THYM, STAD, TGCT and UCS.

Fig. 2

3.3 Methylation and genetic alterations of PNO1 across different cancer types

Previous studies have shown that reduced methylation and hypermethylation of gene promoter regions are associated with numerous human malignancies [24]. We evaluated PNO1 promoter methylation in various cancers using TCGA data from the UALCAN database. There was a significant decrease in the promoter methylation level of PNO1 in BRCA, HNSC, KIRP, PRAD, THCA, and UCEC (Fig. 3A). In addition, PNO1 promoter methylation was significantly elevated in ESCA, KIRC, PAAD, SARC and COAD, but not in GBM, READ, STAD, TGCT, THYM, CESC, PCPG, and CHOL (Supplementary Fig. S4). Next, we investigated the frequency of PNO1 alterations and the relationship between CNA and PNO1 expression. Data from TCGA database revealed that PNO1 gene amplification is the most frequent genetic alterations in UCS, LUSC, BLCA, DLBC and OV (Fig. 3B). Furthermore, a significant positive correlation was observed between CNA and PNO1 expression in the LUSC, SRAC, CHOL, LUAD, HNSC, MESO, BLCA, ESCA, CESC, TGCT, KICH, COAD, SKCM, STAD, READ, UCS, OV, KIRC, LIHC, UCEC, UVM, KIRP, PCPG, GBM, PRAD, LAML, PAAD, THCA, and LGG samples (Fig. 3C). These findings highlight the impact of changes in PNO1 promoter methylation and copy number alterations on its expression levels across various cancers, suggesting a pivotal role for PNO1 in tumorigenesis and progression.Fig. 3 Analysis of DNA methylation and mutation features of PNO1 across various cancer types. (A) Analyzing the promoter methylation level of PNO1 using UALCAN website. (B) Analyzing the PNO1 expression changes, such as amplification, mutation, and deletion, using cBioPorta. (C) Analyzing the correlation between PNO1 and CNA. *P < 0.05, **P < 0.01, ***P < 0.001.

Fig. 3

3.4 Relationship between PNO1 and immune cell infiltration and TME

Recent studies have revealed that immune responses and immune environment are crucial regulators of tumor progression. Immunotherapy has led to brought breakthroughs in cancer treatment, and new targets and biomarkers are essential for further enhance the efficacy of immunotherapy [[25], [26], [27]]. Therefore, a comprehensive understanding of the immune infiltration status in patients with tumors is important to devise appropriate personalized immunotherapy strategies. To investigate whether PNO1 influences the immune response, we analyzed the association of PNO1 with tumor immune cells and immune-related genes. We first generated a heatmap to display the correlation between PNO1 expression and 26 types of immune cells in 32 tumors (excluding LAML). These results indicate that PNO1 positively correlates with various types of immune cells in multiple tumors (Fig. 4A). We used the ImmuCellAI database [28] to investigate the relationship between PNO1 expression and immune cell infiltration. Our results are consistent with our previous findings, demonstrating a positive correlation between PNO1 expression and multiple immune cell types (Fig. 4B). To further elucidate the role of PNO1 in tumor immunity, we performed a co-expression analysis of PNO1 and immune-related genes in 33 tumors. Our heatmap analysis revealed a consistent pattern of co-expression between PNO1 and immune-related genes (Fig. 4C–G), suggesting a potential mechanism by which PNO1 affects tumor immunity through the modulation of immune checkpoint genes. The multifaceted role of TME in cancer pathogenesis, including tumor progression and therapeutic resistance, has been well documented in numerous peer-reviewed studies [29,30]. The TME score is a robust prognostic biomarker and predictor of response to immune checkpoint inhibitors [18]. In 33 types of cancer, a marked association was observed between PNO1 expression and nucleotide excision repair as well as the DNA damage response, indicating a positive correlation. PNO1 expression was positively correlated with mismatch repair in 33 types of malignant tumors, except for UCS. PNO1 expression also showed a positive correlation with TMEscore B in 25 tumor types, indicating a higher level of immune cell infiltration and activation. Moreover, PNO1 expression was associated with TMEscore A in 19 tumor types, suggesting a lower level of immunosuppression and higher antigenicity (Fig. 4H).Fig. 4 Correlation analysis between PNO1 expression and immune cells and immune-related genes. (A) The CIBERSORT algorithm was utilized to analyze the correlation between PNO1 expression and infiltration levels. (B) The ImmuCellAI database was employed to analyze the relationship between PNO1 expression and infiltration levels of multiple immune cell types. Analysis was performed to investigate the correlation between PNO1 and genes associated with immune activation (C), immune suppression (D), MHC (E), chemokines (F), and chemokine receptors (G). (H) The relationship between PNO1 and TME-related biological processes in 33 different types of tumors was analyzed, and the results were presented in the form of a heatmap. *P < 0.05, **P < 0.01, and ***P < 0.001.

Fig. 4

We further analyzed the relationship between PNO1 expression, tumor mutational burden (TMB), and microsatellite instability (MSI) to evaluate the potential of PNO1 as a predictive factor for immune checkpoint inhibitors (ICIs) efficacy. TMB [31] and MSI [32] are novel biomarkers that can predict the ICIs response. All tumor types were selected from TCGA dataset and the correlation coefficients of PNO1 expression with TMB and MSI were calculated. The results showed that PNO1 expression correlated with TMB in LUAD, BRCA, LGG, STAD, THCA, SKCM, HNSC, THYM, COAD, LUSC, KIRC, BLCA, SARC and UVM. Moreover, PNO1 expression correlated with MSI in UCEC, LUAD, KIRC, STAD, PCPG, MESO, LGG, and PRAD (Supplementary Fig. S5A). Pearson's correlation analysis revealed a significant negative correlation between PNO1 expression and stromalscore, immunescore, and ESTIMATEScore in most tumors, including BRCA (Supplementary Fig. S5B). The correlation between PNO1 expression and immunotherapy response was examined using the Tiger website (http://tiger.canceromics.org/#/home). Patients with high PNO1 expression in various cohorts showed a considerable increase in expression levels under treatment (Supplementary Table 1). The TIDE database analysis indicated that patients with high PNO1 expression who underwent PD-1 therapy had poorer progression-free survival (PFS) (Supplementary Fig. S5C). These results suggest that high PNO1 expression may be related to the immune response in some tumors; however, further investigation is required to elucidate the underlying mechanisms. These findings imply that PNO1 plays a role in various tumor types by modulating the extracellular matrix and immune regulation, offering valuable insights for the development of novel therapeutic strategies.

3.5 PNO1 interacting chemicals and genes

Analyzing of the correlation between gene expression and IC50 is a crucial research endeavor that can provide valuable insights into tumor cell response to anti-cancer drugs. Using the GDSC cancer drug sensitivity genomics training set, we discovered a positive correlation between PNO1 expression and IC50 for 153 anti-tumor inhibitors (Fig. 5A). The top five included elephantin, PRIMA-1MET, AMG-319, nelarabine, and entinostat (Fig. 5B–F). These findings suggested a potential association between PNO1 and drugs resistance. Our research methodology contributes to a better understanding of the efficacy and resistance mechanisms of anti-cancer drugs, which are critical for developing personalized cancer treatments.Fig. 5 Association between PNO1 expression and drug sensitivity. (A) Correlation analysis was performed to investigate the relationship between PNO1 gene expression and 192 drugs in the GDSC database. The results were presented using a volcano plot, which provided a graphical representation of the correlation coefficients and negative log10 of p-values. (B–F) Spearman rank correlation analysis is to be performed between the expression levels of PNO1 gene and the IC50 values of drugs, and the top 5 drugs that are most correlated with PNO1 expression are to be listed (Elephantin, PRIMA-1MET, AMG-319, Nelarabine and Entinostat).

Fig. 5

3.6 Construction of the molecular interaction network associated with PNO1

It is well established that miRNA regulates gene expression including reducing mRNA stability and translation by targeting 3′-untranslated regions (UTRs) of mRNAs. The ceRNA hypothesis suggests that ncRNAs, including competing endogenous RNAs (circRNAs) and long noncoding RNAs (lncRNAs), compete with mRNAs for binding to microRNA-responsive elements (mREs). This competition inhibits miRNA-mediated gene silencing and modulates gene expression [33]. We screened eight target miRNAs using three online databases (Fig. 6A), among which only three target miRNA molecules were predicted as lncRNAs and circRNAs using StarBase. In our analysis, 34 lncRNAs and 35 circRNAs were identified as putative miRNA targets of PNO1. Using these predictions, a ceRNA network was constructed (Fig. 6B) to provide a foundation for investigating the pharmacological agents that modulate PNO1 expression. We analyzed PNO1 protein interaction networks using the GeneMANIA database. As a result, 20 genes were closely correlated with PNO1, among which, recombinant RIO kinase 1 (RIOK1) was the most highly correlated gene. In addition, functional prediction results showed that PNO1 is closely related to ribosome biogenesis, preribosome, rRNA metabolism, RNA methylation and RNA methyltransferase activity (Fig. 6C). TCGA expression data showed that PNO1 expression was closely related to the ferroptosis-related molecules (Fig. 6D), m1A, m5C and m6A-related enzymes in pan-cancer (Fig. 6E).Fig. 6 Gene correlation analysis and interaction network construction of PNO1. (A) The intersection of the predictions was among the target miRNA molecule of PNO1. (B) A competing endogenous RNA (ceRNA) network was created centered around PNO1, with miRNAs, lncRNAs, and circRNAs represented in yellow, green, and blue, respectively. (C) The GeneMANIA online tool can be used to generate an interaction network diagram for the PNO1 gene, which would display the top 10 genes or proteins most closely related to PNO1. Perform a correlation analysis between the expression of PNO1 and genes that are associated with the ferroptosis pathway genes (D), as well as genes related to m1C, m5C, and m6A-related genes (E), separately.

Fig. 6

3.7 PNO1 knockdown inhibits proliferation, migration, invasion and immune escape of breast cancer cells

Breast cancer is a pervasive malignancy worldwide, and its prevalence is steadily increasing. Early detection and treatment of breast cancer is paramount for improving prognosis and reducing the burden of this disease [1,34]. According to the latest literature, the function and mechanism of PNO1 in breast cancer remain unclear. To investigate the function of PNO1 in the development of breast cancer, we initially established PNO1 knockdown in ER receptor-positive breast cancer cells (MCF7) and triple-negative breast cancer cell lines (MDA231) using siRNA. To overexpress PNO1, we employed transfection methods using PNO1 overexpression vectors. Successful PNO1 overexpression and knockdown were each validated by qPCR (Supplementary Figs. S6A and B). Changes in proliferation migration, and invasion were evaluated by CCK8, colony formation assays, flow cytometry, transwell assays, and scratch wound assays. As expected, PNO1 depletion markedly reduced the proliferative ability (Fig. 7A), decreased the colony number of siPNO1 cells (Fig. 7B), induced G1/S cell cycle arrest (Fig. 7C), and inhibited the migratory and invasive abilities of MCF7 and MDA231 cells (Fig. 7D and E). Our CCK8 assays reveal that PNO1 overexpression significantly enhanced cell proliferation (Supplementary Fig. S7). Moreover, qPCR analysis demonstrated that PNO1 knockdown significantly reduced the expression of CyclinD1, CyclinE, and CDK4, whereas PNO1 overexpression exerted the opposite effect (Fig. 7F and G). Our results indicated that PNO1 modulation has a significant impact on key cellular mechanisms. Specifically, PNO1 knockdown markedly reduced the expression levels of the immune checkpoint protein CD274 and the ferroptosis regulator GCLM. This downregulation was accompanied by an increase in the methylation regulators, m6A writers METTL3 and METTL14, and a decrease in the demethylase FTO and m6A readers YTHDF1 and YTHDF2 (Supplementary Fig. S6C). In contrast, PNO1 overexpression inversely upregulated GCLM and FTO, along with YTHDF1 and YTHDF2, and suppressed METTL3 and METTL14 expression (Supplementary Fig. S6D). Cytotoxicity assays demonstrate that cells with PNO1 knockdown are more sensitive to the chemotherapeutic agent Entinostat, as evidenced by a notable increase in the cell inhibition rate (Supplementary Fig. S8).Fig. 7 Impact of PNO1 expression on growth, cell cycle, and migration abilities of MCF7 and MDA231 cells. (A–B) The viability and proliferation of MCF7 and MDA231 cells were assessed by CCK8 and colony formation. (C) Cell cycle was detected using flow cytometry in PNO1 knockdown cells. (D–E) The migration and invasion of MCF7 and MDA231 cells were evaluated by wound healing and transwell assays. (F) The mRNA levels of CyclinD1, CyclinE, and CDK4 were analyzed by qPCR after PNO1 expression was decreased in MCF7 and MDA231. (G) The mRNA levels of CyclinD1, CyclinE, and CDK4 were analyzed by qPCR after PNO1 expression was overexpressed in MCF7 cells.

Fig. 7

CD8+ T cells were isolated and pre-activated from peripheral blood (Fig. 8A), and a co-culture system was established that included MDA231 breast cancer cells with PNO1 knockdown and pre-activated CD8+ T cells (Fig. 8B). These findings demonstrated that the knockdown of PNO1 in MDA231 cells significantly reduced the ability of tumor cells to evade immune surveillance by CD8+ T cells (Fig. 8C). ELISA assays corroborated these observations, revealing a substantial increase in the secretion of granzyme B and IFN-γ by CD8+ T cells when co-cultured with PNO1-knockdown tumor cells (Fig. 8D).Fig. 8 Knocking down PNO1 reduces the immune escape ability of tumor cells. (A) Schematic diagram of the sorting and Activation process of CD8+ T Cells. (B) Schematic representation of the tumor cell killing process mediated by CD8+ T Cells. (C) CD8+ T cells were co-cultured with tumor cells that knocked down PNO1, stained with crystal violet, absorbance value was determined at 570 nm, and the number of surviving cells was detected. (D) CD8+ T cells were co-cultured with PNO1-knockdown tumor cells, and an ELISA assay was used to measure the levels of IFN-γ, granzyme B and TNF-α in the cell supernatant.

Fig. 8

PD-L1, which serves as an immunosuppressive ligand expressed on tumor cells, engages PD-1 in tumor-infiltrating T cells to attenuate their cytotoxicity and cytokine production, thereby impairing antitumor immunity. Molecular docking analysis showed that PD-L1 (PDB: 5dxw) interacted with PNO1 (PDB: 6G18), and the interacting amino acids included GLU- 73A, PRO-79, VAL-80, PRO-81, ALA-82, TYR-85, LYS-89, TRP-92, MET-93, PHE-96, VAL-100, LEU-105, GLN-106, ILE-107, ARG-108, PHE-109, ASN-110, LEU-111, LYS-112, SER-113, ARG-114, ASN-115, ILE-118, ARG-119, and THR-120 (Fig. 9A). A comprehensive analysis of the TCGA and GEO databases established a robust positive correlation between PNO1 expression and PD-L1 levels (Fig. 9B). Co-IP assays further confirmed the interaction between PNO1 and PD-L1 (Fig. 9C, Supplementary Figs. S9–S12). After knocking down PNO1, the expression of PD-L1 is significantly reduced (Fig. 9D, Supplementary Figs. S13–S15). Upon restoration of PNO1 expression, PD-L1 expression was upregulated compared to the knockdown-only condition, indicating that PNO1 expression can partially reverse the downregulation of PD-L1 following its knockdown (Fig. 9E, Supplementary Figs. S16–S18). In summary, PNO1 plays a crucial role in immune evasion of breast cancer cells by modulating the activity of CD8+ T cells. These findings suggest potential new targets for the treatment of breast cancer.Fig. 9 Knocking down PNO1 expression decreased PD-L1 expression. (A) The protein docking results of PNO1 (PDB: 6G18) with PD-L1 (PDB: 4I0K), blue shows the PNO1 protein structure, yellow is the PD-L1 protein structure. (B) Correlation analysis of PNO1 and PD-L1 in TCGA-BRCA, GSE86166 and GSE65194. (C) The binding interaction between PNO1 and PD-L1 was confirmed through a CO-IP assay employing an anti-PNO1 antibody. Subsequently, Immunoblotting (IB) analysis was utilized to assess the expression levels of both PNO1 and PD-L1 proteins. (D) WB was performed to examine PD-L1 expression after knock-down of PNO1. (E) WB was performed to examine PD-L1 expression in PNO1-knockdown MCF7 cells following transfection with a PNO1 overexpression plasmid.

Fig. 9

3.8 PNO1 knockdown impairs tumor growth and angiogenesis in breast cancer xenografts

To further explore the in vivo function of PNO1, we established a breast cancer xenograft model by subcutaneously inoculating stable PNO1-knockdown MDA231 cells into nude mice. Tumor growth was monitored, and tumor volume and weight were measured at the end of the experiment. PNO1 knockdown significantly decreased both tumor volume and weight, indicating that PNO1 is essential for the tumorigenicity of breast cancer cells in vivo (Fig. 10A). In addition, we performed IHC staining of the tumor sections to evaluate the expression levels of Ki67, CD34 (markers of angiogenesis), and Cyclin D1, which are markers of cell proliferation, angiogenesis, and cell cycle, respectively. We observed that PNO1 knockdown led to significantly lower levels of these markers in xenograft tumors formed by shPNO1 cells, suggesting that PNO1 is involved in regulating the proliferation, angiogenesis and cell cycle of breast cancer cells in vivo (Fig. 10B). Collectively, these results indicate that PNO1 is a critical regulator of the growth and metastasis of ER receptor-positive and triple-negative breast cancer, and that PNO1 knockdown can effectively suppress tumor growth and angiogenesis in breast cancer xenografts.Fig. 10 Knockdown of PNO1 repressed tumor growth in vivo. (A) Tumor volume (Left) and tumor weight (Right) of nude mice with MDA231 cells were monitored. (B) The expression of PNO1, Ki67 (marker for tumor proliferation), CD34 (markers for tumor metastasis) and CyclinD1 of xenografts in nude mice. The expression of PNO1, Ki67, CD34 and CyclinD1 was analyzed as integrated optic density (IOD). **P < 0.01, and ***P < 0.001.

Fig. 10

The above bioinformatics analysis indicated a potential combination of PNO1 and PD-L1. We examined the protein expression profiles of PNO1 and PD-L1 in breast cancer tissues and corresponding adjacent non-tumor tissues using IHC techniques. The results revealed significantly elevated expression of both PNO1 and PD-L1 in breast tumor tissues compared to that in matched adjacent normal breast epithelium (Fig. 11A). Spearman's rank correlation analysis demonstrated a significant positive association between PNO1 and PD-L1 expression scores (R = 0.3933, P < 0.01) (Fig. 11B). Patients were stratified into high- and low-expression groups based on the median IHC scores for PNO1 and PD-L1. There was substantial concurrence between the PNO1-high and PD-L1-high subgroups (P < 0.001, Fig. 11C), indicating coordinated upregulation. These findings underscore the potential of the PNO1-PD-L1 axis as a driver of breast carcinogenesis and a promising target for therapeutic intervention. Moreover, our analysis of the clinicopathological data established a correlation between elevated PNO1 expression and advanced T and M stages of breast cancer (Supplementary Table 2). An independent validation cohort was formed using clinical data from TCGA, which corroborated the association between high PNO1 expression and aggressive cancer characteristics, including larger tumor size and advanced histological grade, as well as positive ER, PR, and HER2 status (Table 1). Collectively, these findings suggest that PNO1 is a valuable biomarker for prognosis and therapeutic decision-making in breast cancer management.Fig. 11 The expressions of PNO1 and PD-L1 in breast cancer and adjacent non-tumor tissue pairs. (A) PNO1 and PD-L1 protein expression in adjacent non-tumor compared to breast cancer tissue (100 μm, 200 μm). (B) Correlations between PNO1 and PD-L1expression in breast cancer tissues. The R values and p values are from Pearson's correlation analysis. (C) Percentages of specimens exhibiting low or high PNO1 expression were correlated with PD-L1 levels. Two-sided χ2 test. *P < 0.05, **P < 0.01.

Fig. 11

Table 1 The association between PNO1 expression and clinicopathologic features of BRCA in the TCGA.

Table 1Characteristic	PNO1 expression	p	
Low(n = 541)	High(n = 542)	
Age, n (%)			0.062	
 ≤60	316 (29.2 %)	285 (26.3 %)		
 >60	225 (20.8 %)	257 (23.7 %)		
T stage, n (%)			0.044	
 T1	148 (13.7 %)	129 (11.9 %)		
 T2	293 (27.1 %)	336 (31.1 %)		
 T3	81 (7.5 %)	58 (5.4 %)		
 T4	18 (1.7 %)	17 (1.6 %)		
N stage, n (%)			0.845	
 N0	259 (24.3 %)	255 (24 %)		
 N1	175 (16.4 %)	183 (17.2 %)		
 N2	56 (5.3 %)	60 (5.6 %)		
 N3	41 (3.9 %)	35 (3.3 %)		
M stage, n (%)			1.000	
 M0	452 (49 %)	450 (48.8 %)		
 M1	10 (1.1 %)	10 (1.1 %)		
Pathologic stage, n (%)			0.822	
 Stage I- II	305(37.3 %)	405 (38.2 %)		
 Stage III- IV	135 (12.7 %)	125 (11.7 %)		
Histological type, n (%)			< 0.001	
 Infiltrating Ductal Carcinoma	329 (33.7 %)	443 (45.3 %)		
 Infiltrating Lobular Carcinoma	145 (14.8 %)	60 (6.1 %)		
PR status, n (%)			< 0.001	
 Negative	115 (11.1 %)	227 (22 %)		
 Positive	399 (38.6 %)	289 (27.9 %)		
ER status, n (%)			< 0.001	
 Negative	63 (6.1 %)	177 (17.1 %)		
 Positive	453 (43.8 %)	340 (32.9 %)		
HER2 status, n (%)			0.025	
 Negative	299 (41.1 %)	259 (35.6 %)		
 Positive	67 (9.2 %)	90 (12.4 %)		

4 Discussion

Early detection, diagnosis, and treatment are crucial for tumor prevention and treatment of tumors [1]. Pan-cancer analyses have been successfully used to verify diagnostic and prognostic cancer biomarkers. However, to date, no studies have been conducted on the expression and clinical significance of PNO1 in multiple cancers. To our knowledge, this is the first study to explore the molecular mechanism of PNO1 through pan-cancer bioinformatics analysis. Furthermore, we explored the role of PNO1 in breast cancer, both in terms of its impact on cellular processes and its potential clinical significance. The expression of PNO1 was generally increased in a variety of cancer types and was strongly associated with poor patient prognosis. This result is consistent with recent studies showing that PNO1 is highly expressed in various cancers, including ccRCC, COAD, glioma, osteosarcoma, and LUAD, and that high expression levels are associated with poor prognosis [5,8,11,16,[35], [36], [37]]. In addition, our findings indicated that elevated PNO1 expression levels were detected in BRCA, KICH, LIHC, and PAAD. Notably, this heightened expression correlates with poorer prognosis for both OS and DSS across these patient groups. However, PNO1 was expressed at low levels in KICH and LAML, whereas in KIRC and READ, patients with high PNO1 expression had a better prognosis. This suggests that PNO1 may play a complex role in different cancers, and that its expression level and function may be regulated by tumor-specific factors.

Promoter methylation, a critical mechanism in epigenetic regulation, plays a significant role in controlling gene expression and ensuring gene silencing stability [38]. Our findings from TCGA database analysis, using the UALCAN platform, indicate that PNO1 promoter methylation levels vary significantly between normal and tumor tissues across several cancer types. Specifically, decreased methylation levels in cancers, such as BRCA, HNSC, KIRP, PRAD, THCA, and UCEC, suggest an epigenetic activation mechanism leading to PNO1 overexpression. Conversely, elevated promoter methylation in cancers, such as ESCA, KIRC, PAAD, SARC, and COAD, did not uniformly suppress PNO1 expression, indicating the presence of other regulatory mechanisms that may override the effects of methylation. Furthermore, copy number alterations (CNA) are significant contributors to aberrant PNO1 expression. Gene amplification, the most frequent genetic alteration observed in UCS, LUSC, BLCA, DLBC, and OV, was strongly correlated with increased PNO1 expression. The positive correlation between CNA and PNO1 expression in a wide range of cancers underscores the role of genetic changes in PNO1 dysregulation. Together, these insights suggest that the high expression of PNO1 in various cancers results from a multifaceted regulatory mechanism involving epigenetic modifications such as promoter methylation and genetic alterations such as CNA. The combined effect of these regulatory layers contributes to tumorigenesis and progression by enhancing PNO1 expression, which is involved in the maturation of the 60S ribosomal subunit, cell cycle regulation, and DNA replication [39].

Our research provides compelling evidence that PNO1 is a key regulator of breast cancer cell proliferation and has significant implications for the development of targeted cancer therapies. By knocking down PNO1, we observed a substantial reduction in cell proliferation and consequential arrest in the S phase of the cell cycle. This effect was directly associated with the suppression of critical cell cycle regulators, including Cyclin D1, Cyclin E, and CDK4, which are essential for the transition from G1 to S phase [40]. The positive correlation between PNO1 expression and the IC50 of entinostat further supports the role of PNO1 in modulating cellular response to chemotherapy. Cytotoxicity assays revealed that cells with reduced PNO1 expression exhibited increased sensitivity to entinostat, which led to increased cell inhibition. This finding is consistent with reports that entinostat can shift the balance of cell cycle phases by reducing S phase cells and increasing G1 phase cells, while decreasing the expression of Cyclin D1 and anti-apoptotic proteins, such as Mcl-1 and XIAP [41,42]. Similar observations in CRC highlight the expanded implications of PNO1 in oncogenesis, suggesting an integral role in various cancer types [11]. The correlation between CyclinD1 and cancer progression, including tumor invasion and metastasis, further underscores the critical nature of these cell cycle regulators [43,44]. Moreover, the potential of PNO1 as a diagnostic marker and therapeutic target for lung adenocarcinoma has been confirmed in recent studies, which have shown that PNO1 knockdown hinders cell growth, invasion, and apoptosis, especially through the Notch signaling pathway. In addition, the transcriptional activation of PNO1 by E2F1, which promotes malignant progression and inhibits cell death in pancreatic cancer, emphasizes its multifaceted role in cancer biology [45]. Thus, our analysis sheds light on the key function of PNO1 in breast cancer proliferation and highlights its importance as a target for cancer therapy, suggesting its potential for broader applications in oncology. To validate our findings, we performed IHC analyses of breast cancer xenografts, which confirmed a significant reduction in Ki67 and CyclinD1 expression in PNO1-silenced groups. These results not only illuminate the pivotal function of PNO1 in breast cancer cell proliferation, but also highlight its significance as a therapeutic target, indicating its potential using in a broad spectrum of oncological applications.

In recent years, tumor immunotherapy has received widespread attention, and a series of research advances have been made. The TME is a critical factor that drives tumor progression, facilitates tumor metastasis, and influences the efficacy of tumor immunotherapy [46,47]. The findings of this study suggest a strong correlation between elevated PNO1 expression and increased stromal score in the TME of 26 cancerous tissues and a high immune score in the TME of 19 cancer tissues. In most tumors, PNO1 expression exhibited a significant positive correlation with the transcriptional levels of immune chemokines, including CXCL8, CXCL10, and CXCL5. In the TME, the inflammatory response to a plethora of cytokines plays a pivotal role in facilitating tumor growth and progression [48]. Extensive evidence in the literature has provided evidence for the crucial involvement of HLA-I, HLA-II, CD276, CD28, and BTNL2 in anti-tumor immune responses [[49], [50], [51]].

Our study uncovered a significant role of PNO1 in the immune evasion of breast cancer cells, with a particular focus on its interaction with PD-L1, a key player in immune checkpoint pathways. The results showed that PNO1 expression positively correlated with TMB across a spectrum of tumors, including BRCA and LUAD, suggesting a broad impact of PNO1 on tumor biology. Notably, PNO1 expression is closely associated with various scores that reflect the TME, such as stromal, immune, and ESTIMATE scores, indicating its involvement in the tumor-immune interface. Clinical observations have revealed that high PNO1 expression is correlated with a reduced response to immunotherapies, highlighting the potential of PNO1 as a biomarker for predicting treatment outcomes. Interestingly, we observed that manipulating PNO1 levels directly affected PD-L1 expression, with knockdown leading to decreased PD-L1 expression, while restoring PNO1 expression partially reversed. This relationship highlights the regulatory role of PNO1 in regulating PD-L1 expression, and thus extends to the immune escape capacity of breast cancer cells. The link between PNO1 and immune evasion was further supported by the strong correlation between PNO1 and PD-L1 expression in clinical breast cancer specimens. IHC analysis confirmed that both PNO1 and PD-L1 were significantly upregulated in breast cancer tissues compared to adjacent normal tissues. Given the established connection between high PD-L1 expression and poor prognosis in patients with breast cancer, our findings suggest that PNO1 may be a key factor in this process. Engagement of PD-1 by PD-L1 in tumor-infiltrating lymphocytes leads to T cell dysfunction, which is a critical step in the attenuation of antitumor immunity [52]. The significant association between PNO1 and PD-L1 levels in our study provides a foundation for further exploration of the PNO1-PD-L1 signaling axis and its implications in immune evasion in breast cancer. In addition, the close association between PNO1 expression and immune-related genes, such as CD274, HLA-I, HLA-II, CD276, CD28, and BTNL2, suggests that targeting PNO1 may offer promising opportunities for the development of novel immunotherapeutic strategies.

The modulation of PNO1 expression by MYC and EBF1 is a crucial factor in the initiation and progression of tumors as well as their prognoses. This regulation is achieved through the THBS1/FAK/Akt and p53/p21 signaling pathways, which play key roles in cellular processes including proliferation, apoptosis, and migration. Therefore, the regulation of PNO1 may have significant implications for the growth and metastasis of tumor cells [5,11]. In the present study, PNO1 was found by IHC to be located primarily in the cytoplasm of breast cancer tissues, thus providing the possibility for ceRNA mechanistic research. However, the role of PNO1 as a ceRNA and related miRNAs, circRNAs, and lncRNAs has not yet been reported. In this study, we used three online databases to screen three miRNAs associated with PNO1. Three miRNAs targeting circRNAs, mRNA, and lncRNAs were predicted and an intuitive ceRNA network diagram, including PNO1, circRNAs, and lncRNAs, was constructed. To thoroughly investigate the molecular mechanisms upstream and downstream of PNO1, we constructed an interaction network of 20 genes and screened a series of anti-tumor inhibitors closely related to PNO1.

PNO1, a protein that plays a crucial role in ribosome synthesis and RNA processing, plays an essential role in the regulation of protein synthesis and function within cells. RNA processing involves the modification of RNA molecules post-transcriptionally, enabling them to function effectively within cells [53,54]. Recent reports have indicated that RNA methylation modifications (m1A, m6A, and 5 mC) provide important information on gene regulation and play critical roles in fundamental biological processes [55]. Our current research shows that high PNO1 levels are strongly correlated with a variety of RNA-modified genes, including m1A-, m5C-, and m6A-related genes, in various cancers. Moreover, PNO1 closely correlated with ferroptosis pathway genes, including IREB2, GCLM, ACSL3, NFE2L2, CS, EMC2, ACACA, NCOA4, TFRC, HMGCR, GCLC, ACSL4, ATP5MC3, SQLE, ABCC1, and FANCD2. The knockdown of PNO1 notably diminishes the expression of GCLM, a ferroptosis-related gene, while simultaneously increasing the expression of m6A methyltransferases (METTL3 and METTL14) and reducing the levels of the demethylase FTO and m6A readers (YTHDF1 and YTHDF2). In contrast, PNO1 overexpression elevated GCLM, FTO, YTHDF1, and YTHDF2 expression, and decreased METTL3 and METTL14 levels. This overexpression also fosters cell proliferation, as evidenced by our CCK8 assay, and sensitizes tumor cells to the chemotherapeutic agent entinostat, thereby enhancing the cell inhibition rate. These actions highlight the involvement of PNO1 in critical aspects of cancer progression, including cell cycle progression, TME modulation, immune response, RNA modification, and the cellular response to chemotherapy. Taken together, this study points out a new direction for elucidating the molecular mechanism of the PNO1 gene in tumorigenesis and progression.

5 Conclusions

In conclusion, this is the first study to demonstrate the significance of PNO1 in 33 types of cancers. Our results provide evidence that elevated PNO1 expression is present in various cancers and that high PNO1 expression indicates worse clinical outcomes in several tumor types. Enrichment analysis revealed that PNO1 was associated with the cell cycle in breast cancer. Further experiments confirmed that PNO1 arrest cell cycle G1/S progression. Moreover, PNO1 expression is associated with TME, immune cell infiltration, RNA-modified genes, ferroptosis pathway genes, and tumor chemotherapeutic drugs. Although these findings warrant further investigation, our data and analyses provide important insights into the epigenetic regulation of PNO1 and open new avenues for targeting these immune-related genes.

Data availability statement

Data will be made available on request.

Ethical approval and consent to participate

Paraffin-embedded clinical breast cancer specimens of this study were obtained from the Department of Pathology, Henan Provincial People's Hospital. This study was approved by the Medical Ethics Committee of Henan Provincial People's Hospital (NO. 2021-036). Informed consent was obtained from all subjects involved in the study. This study followed the Declaration of Helsinki and the Ethical Review Guidelines for Human Biological Samples of the Chinese Academy of Medical Sciences. The animal experiments in this study were approved by the Institutional Animal Care and Use Committee (IACUC) of Henan Experimental Animal Center (NO. ZZULAC2023061313).

Consent for publication

Not applicable.

Funding statement

The present study was supported by the 10.13039/100017632 Henan Provincial Key R&D and Promotion Special (Science and Technology Tackling) Project (242102311196 ), the Provincial-ministerial Co-construction Project of 10.13039/100017632 Henan Provincial Medical Science and Technology Tackling Plan (SBGJ202103025 ), Joint Construction Project of Henan Medical Science and Technology Research Program (LHGJ20220053 ), 10.13039/501100014764 China International Medical Foundation (Z-2021-46-2101-2023 ), and Scientific Research Staring Foundation for Doctor of 10.13039/501100014208 Henan Provincial People's Hospital (ZC20220349 ).

CRediT authorship contribution statement

Yinhui Qin: Writing – review & editing, Writing – original draft, Funding acquisition, Data curation. Zhen Li: Visualization, Formal analysis, Data curation. Xianwei Zhang: Writing – original draft, Methodology, Data curation. Junjun Li: Writing – original draft, Data curation, Conceptualization. Yuetai Teng: Writing – review & editing, Writing – original draft, Visualization, Data curation. Na Zhang: Writing – review & editing, Writing – original draft, Visualization, Formal analysis, Data curation. Shengyu Zhao: Investigation. Lingfei Kong: Writing – review & editing, Writing – original draft, Visualization, Data curation. Weihong Niu: Writing – review & editing, Writing – original draft, Visualization, Validation, Funding acquisition, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36819.
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