Fatty acid metabolism related gene MECR contributes to the progression of prostate cancer

Abstract

Background

Prostate cancer (PCa) is the most common urological malignancy and second only to lung cancer in incidence among men. Its prognosis varies widely due to its heterogeneity. Research indicates that fatty acid metabolism may play a role in tumor development.

Methods

The gene expression profiles of PCa cell lines (GSE6919) in GEO database were analyzed to identify differentially expressed genes and their significance in relation to progression-free interval. The R package was employed to assess overall survival significance and clinicopathological features. The study investigated the effects of gene mutations and methylation on PCa and their correlation with immune cell infiltration in the tumor microenvironment, utilizing cBioPortal and UALCAN resources. TIMER was used in the TCGA project to compare the expression of MECR in tumours and in adjacent normal tissue for different tumours or for specific tumour subtypes. Furthermore, we examined the impact of hub genes on PCa progression through RT qPCR, immunohistochemistry, and cellular assays.

Results

The MECR gene, which plays a role in fatty acid metabolism, has been implicated in the development and progression of PCa. Its expression levels are significantly associated with clinical features, survival outcomes, and prognosis in PCa. Comprehensive analyses of MECR mutations and methylation levels further underscore its involvement in the progression of prostate cancer. Additionally, MECR is closely associated with the immune microenvironment and immune cell infiltration in PCa. Furthermore, the in vitro and in vivo data indicated that MECR plays a role in PCa proliferation, migration, and invasion.

Conclusion

MECR has significant potential for research and application in the assessment of PCa prognosis and the regulation of the immune microenvironment.

Introduction

Prostate cancer is the most common urological malignancy, surpassed in incidence among men only by lung cancer [1]. PCa is the second most prevalent cancer worldwide and the fifth leading cause of cancer deaths in men [1]. Despite its frequent detection, the prognosis for patient survival is highly individualized due to the considerable heterogeneity of this malignancy [2]. The management of prostate cancer predominantly relies on clinical decision-making, which incorporates factors such as tumor stage, patient life expectancy, overall health status, and patient preferences [3, 4]. Epidemiological data indicate that PCa incidence rates in China are lower compared to those in Europe and America. However, a significant proportion of Chinese patients present with advanced or late-stage PCa. Cancer treatment is a complex and evolving field involving a variety of therapeutic approaches and strategies, with the main therapeutic measures currently focusing on surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy [5]. For individuals diagnosed with advanced, recurrent, or castration-resistant prostate cancer (CRPC), non-surgical or palliative treatments frequently constitute the primary therapeutic options [1]. In recent years, significant progress has been made in the field of cancer treatment, including the discovery of new targets and the development of new drugs [5, 6]. Therefore, the search for new therapeutic targets for prostate cancer is particularly important.

Tumors can spontaneously generate energy and reprogram their metabolism, distinguishing them from normal tissues [7, 8]. This ability is crucial for tumor progression and metastasis. The Warburg effect, in which cancer cells preferentially use glycolysis even in the presence of oxygen, enhances cell proliferation [9], migration, and invasion [10]. Research has demonstrated that lipid metabolism is critically involved in disease progression, drug resistance, and recurrence, with numerous studies corroborating these findings across different levels. For example, Zhao et al. showed that down-regulation of ACC2 could promote proliferation and metastasis of ovarian cancer in vitro and in vivo by enhancing fatty acid oxidation [11]. Chen et al. found that MK1775 inhibited tumour progression by affecting lipid crosstalk between lung adenocarcinomas and tumour-associated macrophages and CD8 + T cells, which in turn enhanced the effectiveness of anti-PD-1 therapy [12]. Specifically, in the context of PCa, various pathways of aberrant lipid metabolism have been identified. These pathways encompass enhanced lipid uptake from the circulating pool [13], increased transfer of fatty acids from stromal adipocytes into PCa cells [14], augmented de novo synthesis of fatty acids and phospholipids [15], and elevated accumulation of phospholipids and cholesteryl esters as stored cholesterol within cytoplasmic lipid droplets. Rossi et al. [16] identified a marked upregulation of fatty acid synthase expression in surgical specimens of PCa relative to adjacent normal tissues. The expression of fatty acid synthase was observed to increase progressively during the malignant transformation of prostate epithelial tissue. These findings implicate fatty acid synthase as a critical factor in the proliferation and invasion of PCa. Furthermore, patients with PCa who have undergone androgen-deprivation therapy (ADT) exhibit a rise in insulin resistance and elevated serum cholesterol levels within 12–24 weeks of initiating treatment [17]. The impact of fatty acid metabolism on PCa progression is multifaceted. Firstly, prostate cancer cells rely on fatty acid oxidation as their main source of energy [18], and secondly, epidemiological studies have found that obese men are at higher risk of developing PCa [19]. Obesity and a high-fat diet may accelerate PCa progression by affecting lipid metabolism.

The link between fatty acid metabolism genes and prostate cancer (PCa) is unclear. This paper aims to investigate their correlation with clinical treatment and prognosis in PCa patients. We aim to explore the link between a PCa risk assessment model and immune status by analyzing TCGA gene expression data and examining fatty acid metabolism gene expression. This research seeks to inform clinical management and identify new therapeutic options.

Materials and methods

Analysis of microarray data and screening of fatty acid metabolism-related differential expressed genes

The Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) was used to analyze fatty acid metabolism-related differentially expressed genes (FAM-DEGs) in PCa. Dataset GSE6919 was chosen for this analysis, and 92 human fatty acid metabolism-related genes were identified from previous reviews [20, 21].

The cut-off condition was set to ensure statistical significance for DEGs, with an adjusted P-value < 0.05 and an absolute value of the fold change |log₂FC|≥ 1. Venn and Volcano diagrams were generated using ImageGP. Z-score normalization was used for data normalization. R package ‘limma’ was used for differential expression analysis.

The Tumour Immune Estimation Resource (TIMER) tool (https://cistrome.shinyapps.io/timer/) has been used in the TCGA project to compare the expression of Mitochondrial trans-2-enoyl-CoA reductase(MECR) in tumours and in adjacent normal tissue for different tumours or for specific tumour subtypes. In detail, the ‘diff exp’ module on the TIMER homepage was used in this article.

Functional enrichment analysis of FAM-DEGs in PCa

The ClusterProfiler software package was utilised to conduct Gene Ontology (GO) and Kyoto Genome Encyclopedia (KEGG) pathway analysis for functional annotation and enrichment pathway exploration. A statistically significant difference was marked by P < 0.05.

Investigation of clinical relevance and construction of the nomogram

An independent t-test was used to examine the correlation between the expression of FAM-DEGs and the clinicopathological factors, such as TNM stage, age, histological grade, and pathological stage.

Moreover, a nomogram was made to put the model risk score into integration to value the predictive possibility of 1-, 3-, and 5-year OS, which offered the survival feasibility of a precise event [22]. A calibration curve describing the OS of 1-year, 3-year and 5-year was made to visualize the scanning rates against probabilities predicted by the nomogram. The nomogram and calibration curves were made via the R package “rms”.

Immune infiltrations analysis

The immune infiltrations analysis of MECR in PCa was conducted using TIMER [23]. TIMER was used to assess the correlation between gene and immune cell infiltration. The correlation of gene expression with TMB and MSI was calculated using R software. The study explored the correlation between MECR gene copy number and the level of immune cell infiltration.

MECR mutation and methylation analysis

The mutational profile of MECR in PCa was clarified by cBioPortal (http://www.cbioportal.org/), while UALCAN (http://www.ualcan.path.uab.edu/) investigated the altered methylation in MECR.

Cell lines and cell cultures

The human PCa cell lines, LNCaP, Du145, PC3, C4-2, 22Rv1 and normal prostate epithelial cells (RWPE-1) were procured from the American Type Culture Collection cell bank. The cells were maintained at 37℃ in a 5% CO₂ humidified atmosphere and cultured in media containing 10% FBS (Gibco) and 1% penicillin/streptomycin. RPMI-1640 (Gibco) was used to culture LNCaP, Du145, PC3, 22Rv1 and C4-2.

RNA extraction and reverse transcription polymerase chain reaction (RT qPCR)

Total RNA was extracted from cultured cells using the RNAeasy Animal RNA Isolation Kit (Beyotime) and subsequently reverse-transcribed to cDNA using SweScript cDNA Synthesis SuperMix (Servicebio), in accordance with the manufacturer's instructions.The expression of mRNAs was normalized using GAPDH. The primer sequences are listed in Table S1. At least three technical replicates were performed for each sample to ensure reproducibility and statistical significance of the results.

Small interfering RNA and short hairpin RNA

The interfering RNA sequences of the MECR siRNAs were designed and synthesised by GenePharma. The siRNAs were transfected via the jetPRIME transfection reagent. Transfection efficiency was verified by RT qPCR. The target sequences of siRNAs were shown in Table S2. All lentiviruses were designed by Sangon Biotech (Shanghai) Co. according to the manufacturer's instructions. Then PC3 cells were transduced with MECR shRNA lentivirus [pLV[shRNA]-EGFP-U6 > hMECR[shRNA#1]]. In summary, lentiviral vectors expressing the non-silencing shRNA control and MECR shRNA were transfected into HEK293T cells, and then lentiviral particles containing shRNA constructs were harvested from cells at 72h and infected into PC3 cells using 8 μg/ml polybrene. Cells were selected by 1 μg/ml puromycin and the absence of off-target effects of shRNA was verified by qRT-PCR.

CCK-8 and colony formation assays

The experimental protocol consisted of seeding cells into 96-well plates in triplicate for the CCK-8 assay and then measuring cell viability according to the manufacturer's instructions. Colony formation was assessed by seeding cells into 6-well plates (500 cells/well) in triplicate, incubating for 10 days, and then fixing, staining and counting the colonies. T-test was used for statistical analysis.

Cell migration and invasion assays

Cell migration and invasion assays are performed using Transwell inserts (Corning) according to the manufacturer's guidelines. Migrating cells are counted and captured by light microscopy. Specific experimental steps have been described in detail in previous articles by our team [24, 25]. T-test was used for statistical analysis.

Wound-healing assay

Cells were inoculated into 6-well plates for wound healing assays and treated with si-/nc- MECR. When the cell density was approximately 70%, a straight line was drawn on the plate with a 200ul tip. The edge of the cell wound was marked at the starting time point and photographed under a microscope, and the cell migration distance was measured and the wound closure rate was analyzed after 0, 24 and 48 h. T-test was used for statistical analysis.

Bisulfite sequencing PCR (BSP)

Genomic DNA was treated with bisulfite, and all unmethylated cytosines were converted to uracil, while methylated cytosines remained unchanged; subsequently, primers were designed at both ends of the CpG island for PCR amplification, and the target product was purified for TA cloning, and positive clones were picked for sequencing for each clone, and finally, the sequences sequenced were aligned with the original sequences, and the number of methylated sites and numbers of methylation sites were counted and the degree of methylation was analysed. The sequences were compared with the original sequences to count the number of methylation sites and the degree of methylation. The BSP primer sequences of MECR were shown in Table S4.

IHC and IHC score

Human PCa tissues were fixed, embedded and sectioned prior to IHC staining. Sections were subsequently dried at 60◦C for 2 h and incubated with primary and secondary antibodies, respectively, overnight at 4◦C and for 1h at room temperature. After DAB development, counter-staining was performed with hematoxylin, followed by dehydration in graded alcohol, condensation in xylene, and covering with Permount for microscopic observations. Specific experimental steps have been described in detail in previous articles by our team [24, 25].

IHC Score was calculated on a 4-point scale based on the intensity of cellular staining: Negative, scoring 0 points;Weakly positive, scoring 1 point;Positive, scoring 2 points;Strongly positive, scoring 3 points. 4 grades based on the percentage of positive cells scored:0% ≤ percentage of positive cells ≤ 25%, scoring 1 point;25% < percentage of positive cells ≤ 50%, scoring 2 points;3 points for 50% < percentage of positive cells ≤ 75%;75% < percentage of positive cells ≤ 100%, 4 points.IHC score = Intensity of cell staining score x Percentage of positive cells score.

IF assay

We conducted immunofluorescence (IF) assays to identify gene expression and cellular co-localisation. The procedure was as follows: the subcutaneous tumour tissue was fixed, dehydrated, paraffin embedded and sectioned. The paraffin sections were then deparaffinised in xylene and antigenically repaired using EDTA. Then the sections were first incubated with primary antibodies at 4°C overnight, followed by incubation with the corresponding secondary antibodies and DAPI. The images were obtained using confocal microscopy.

Animal models

The present study employed a subcutaneous model involving male BALB/c nude mice 4–6 weeks old to investigate tumour growth. Specifically, 5 × 10⁶ infected cells were suspended in 100ul of PBS and subcutaneously injected into the right mid-posterior axilla of the nude mice. After 3 weeks, the mice were euthanized and the subcutaneous tumours were collected for further analysis via IF. To establish a lung metastases model, 1 × 10⁶ cells were injected into the tail vein of each mouse. After 30 days of tumour development, the lung tissues from each mouse were collected and stained with H&E. Pulmonary metastases were counted and survival curves were plotted.

Statistical analysis

The R package 'survival' was used for univariate and multivariate Cox regression analyses [26], and the hazard ratios (HRs) and 95% confidence intervals (CIs) should be presented in a clear and objective manner. Furthermore, the independent t-test and Kruskal Wallis test were used to compare different clinical factors by using SPSS software. Spearman correlation analyses were performed using SPSS software to study the degree of linear correlation between variables. Spearman correlation coefficients (rs) ranged between -1 and 1, with rs > 0 being a positive correlation and rs < 0 being a negative correlation. Statistical significance was indicated by a p-value of less than 0.05.

Results

Screening and identification of fatty acid metabolism-related differential expressed genes (FAM-DEGs) in PCa comparing to normal prostate tissues

The volcano map shows 579 up-regulated and 567 down-regulated DEGs from GSE6919 (Fig. 1A). A Venn diagram analysis of 92 fatty acid metabolism genes identified 13 co-expressed genes (Fig. 1B). Univariate and multivariate Cox regression analyses (Fig. 1C, Figure S1, and Table S3) indicated that MECR and ELOVL1 are independent prognostic factors for KIRC.

Fig. 1

Fatty acid metabolism gene MECR was associated with the progression and prognosis of prostate cancer. A Volcano plots of DEGs (differentially expressed genes) between normal prostate tissues and prostate cancer in GSE6919 samples; B Venn diagram of lipid metabolism genes and GSE6919; C Univariate Cox regression analysis of of the 13 fatty acid metabolism-related DRGs in TCGA database; D, E Comparison of MECR expression in normal prostate tissue and prostate cancer; F, G Comparison of ELOVL1 expression in normal prostate tissue and prostate cancer; H Progress free interval analysis of MECR; I MECR expression levels in different cancer types from the TCGA database analyzed by the TIMER database. (*P < 0.05, **P < 0.01, ***P < 0.001)

To explore the link between FAM-DEGs and PCa, we analyzed MECR and ELOVL1 expression in normal and prostate tumor tissues using the TCGA database. MECR was significantly upregulated in tumor tissues, whereas ELOVL1 showed no notable difference. Thus, MECR may be the key gene in PCa clinically, rather than ELOVL1. Kaplan–Meier analysis is a one-way survival analysis that can be used to study the effect of one factor on survival time [27, 28]. In this study, a Kaplan–Meier analysis showed that higher MECR expression correlates with poor progression-free interval (Fig. 1H).

Additionally, a pan-cancer analysis revealed differential MECR expression in various cancers, including prostate adenocarcinoma, bladder urothelial carcinoma, cholangiocarcinoma, colon adenocarcinoma, and kidney clear cell carcinoma (Fig. 1I).

Elucidation of MECR as a vital prognostic gene in PCa

The survival R package analyzed the link between MECR expression and PCa clinicopathological features. MECR expression was significantly associated with T stage and PSA levels, being higher in T3&T4 stages than in T1&2 (Fig. 2A). No significant correlation was found between MECR expression and lymph node, distant metastasis stages, or age (Fig. 2B–D). We found that patients with PSA ≥ 4 ng/ml and high Gleason scores had higher MECR expression (Fig. 2E, F). This result implies that MECR may allow physicians to better understand the biology of prostate cancer, guide treatment decisions, assess prognosis, and monitor disease progression. Additionally, MECR expression significantly correlated with disease-specific survival (DSS) and progression-free interval (PFI) events. However, there is no significant correlation between MECR expression and overall survival (OS) event, as demonstrated in Fig. 2G–I.

Fig. 2

Validating the clinical relevance of MECR. A–I Verifying the relationship between MECR expression and multiple clinicopathological factors of prostate cancer, including T stage, N stage, M stage, age, PSA, Gleason score, OSS event, DSS event and PFI event; J Nomogram for predicting one-, three-, and five-year PFI in the entire TCGA cohort; K Calibration curves of nomogram on consistency between predicted and observed one-, three-, and five-year survival

A prognostic nomogram was created by integrating clinicopathological factors linked to MECR expression in PCa, yielding a combined index that more accurately assessed patients' PFI (C-index: 0.723, p < 0.001), as shown in Fig. 2J. The calibration curves for the 1-, 3-, and 5-year PFIs align well with the predicted probabilities (Fig. 2K). The bias-corrected Line represents the fit of the corrected predictions to the actual values, and the closer the line is to the ideal line, the more accurate the corrected model's predictions are.

Potential biological function or pathway analysis of FAM-DEGs

To further explore potential biological pathways of fatty acid metabolism-related differential expressed genes in PCa, we conducted the GO, KEGG and GSEA analysis. The Go analysis outcome revealed that the FAM-DEGs were functionally enriched in GO0006631(fatty acid metabolic process), GO0006633(fatty acid biosynthetic process), GO0072330(monocarboxylic acid biosynthetic process) and GO0004312(fatty acid synthase activity) (Fig. 3A). According to KEGG analysis, FAM-DEGs were related to hsa01212(Fatty acid metabolism), hsa00062(Fatty acid elongation), hsa00061(Fatty acid biosynthesis) and hsa01040(Biosynthesis of unsaturated fatty acids) (Fig. 3B).

Fig. 3

Functional and GSEA analysis of MECR. A GO functional analysis of MECR; (B) KEGG functional analysis of MECR; C–E GSEA analysis of MECR. *GO Gene ontology, KEGG Kyoyo Encyclopedia of Genes and Genomes;GSEA, gene set enrichment analysis;

In addition, differential analysis of the MECR single genes combined with GSEA enrichment analysis in the TCGA-PRAD cohort identified pathways mainly associated with the WP_PI3KAKT_SIGNALING_PATHWAY [normalized enrichment score (NES) = − 1.370, p.adj = 0.046, false discovery rate (FDR) = 0.035], WP_ADIPOGENESIS [NES = − 1.561, p.adj = 0.046, FDR = 0.035] and PID_AP1_PATHWAY [NES = − 1.702, p.adj = 0.046, FDR = 0.035](Fig. 3C–E).

The association between MECR mutation and methylation with diagnostic histology features and the immune microenvironment in PCa

In the cBioPortal TCGA PRAD cohort, MECR exhibited a mutation frequency of 1% within the total population, encompassing both amplifications and deep deletions (Fig. 4A). Subsequent analysis revealed that adenocarcinoma histology was more prevalent in the non-mutated MECR group compared to the mutated MECR group (Fig. 4B), with nearly 40% of the non-mutated MECR group reporting a family history of prostate cancer (PCa) (Fig. 4C). The tumor protein 53 (TP53) pathway emerged as the most frequently associated pathway with MECR mutations (Fig. 4D). We used UALCAN to analyze MECR promoter methylation in PCa. MECR promoter methylation was significantly lower in PCa tissues, especially in elderly patients (Fig. 4E, F). It was also lower in stage N1 compared to stage N0 (Fig. 4G). MECR mutations were mainly associated with the TP53 signaling pathway, which was reduced in PCa patients with TP53 mutations (Fig. 4H). We sequenced promoter methylation levels in LNCaP and PC3 cell lines using bisulfite sequencing PCR (BSP) to assess MECR promoter methylation in PCa. Only 0.3% of CpG sites were methylated (Table S5-S6, Figure S4-S5), confirming low methylation levels in these cell lines, as predicted by the ULCAN website.

Fig. 4

MECR genomic alterations in prostate cancer analyzed by the cBioPortal database. A OncoPrint of MECR gene alterations in cancer cohort; (Different colors means different types of genetic alterations and deep deletion accounts for the largest proportion) B Correlation between MECR gene mutations and diagnostic histology; C Correlation between MECR gene mutations and prostate family history; E–H Relevance of MECR promoter methylation to clinicopathology, including expressions, years, N stage and TP53 mutations. (* represents p < 0.05; ** represents p < 0.01; *** represents p < 0.005)

The pattern of immune cell infiltration in a variety of tumours, including breast and renal cancers, is closely related to the prognosis of patient survival [29, 30]. Similarly, immune cell infiltration in prostate cancer is strongly associated with patient prognosis. For example, infiltration of M1-type macrophages and neutrophils correlates with patient prognosis and may serve as a potential target for diagnostic and therapeutic prognosis [31]. Prostate cancer creates an immunosuppressive microenvironment characterized by low lymphocyte and high macrophage infiltration [32,33,34,35]. Despite its low response to immunotherapy due to its "cold" nature and low mutational load, immune checkpoint inhibitors have shown promise in advanced cancers [33, 34]. Thus, exploring whether MECR can accelerate PCa progression by affecting the tumor immune microenvironment is worthwhile. MECR showed a positive correlation with Th2 and NK CD56dim cells, and a negative correlation with Th1 cells, mast cells, NK cells, and neutrophils (Fig. 5A). It was also significantly correlated with immune checkpoints like CD274, HAVCR2, PDCD1LG2, and TIGIT (Fig. 5B). These immune checkpoints may bind to the corresponding receptors on T cells, and this interaction inhibits T cell activation and proliferation, thereby weakening anti-tumour immunity and helping tumour cells evade surveillance and attack by the immune system. At the same time, aberrant activation of these immune checkpoints may be involved in immunotherapy resistance. Additionally, B cells, CD4 + T cells, CD8 + T cells, and endothelial cells were correlated with MECR expression (Fig. 5C and Figure S2 A). To assess immunotherapy's clinical effectiveness across different risk groups, we compared TIDE scores between high and low MECR expression groups, finding no significant differences (Figure S2 B). Further analysis using Spearman correlation and Kruskal–Wallis tests revealed significant correlations between MECR expression and Tumor Mutation Burden (TMB), Microsatellite Instability (MSI), and mRNAsi scores (Fig. 5D–F). Estimate, immune and stromal scores are the most commonly used assessment metrics based on the level of immune cell and stromal cell infiltration in the tumour microenvironment. Then the correlation between MECR and immune infiltration in PCa was verified using estimate, immune and stromal scores (Fig. 5G–I). Additionally, we analyzed MECR's immune infiltration levels across various cancer types (Figure S2 C-E), finding a significant association.

Fig. 5

Exploiting the relevance of MECR to the immune microenvironment in prostate cancer. A lollipop chart of the correlation of MECR expression and immune cells; B, C The difference of expression of immune checkpoint and immune cells in PCa tissues with high and low MECR gene expression. G1 is a high expression group and G2 is a low expression group; D–F correlation of MECR expression with MSI score, TMB score and mRNAsi score; G–I correlation of MECR expression with Immune infiltration, including estimate score, immune score and stromal score

Validation of MECR affecting PCa progression in in-vitro experiments

We validated PCa cell lines via RT qPCR and found MECR expression significantly higher in LNCaP and PC3 cells than in RWPE1 cells (Fig. 6A). Thus, we selected LNCaP and PC3 for further validation. LNCaP cells were extracted from lymph node metastasis of PCa patients and represent characteristics of early stage prostate cancer. Whereas PC3 cells were isolated from human prostate cancer bone metastatic tumours and have androgen-independent properties. Using RT qPCR, we confirmed the knockdown efficiency of small interfering reagents in these cells (Fig. 6B). CCK8 experiments showed that si-MECRs in LNCaP and PC3 had significantly slower growth than nc-MECRs (Fig. 6C, D). Additionally, the si-MECR group had significantly fewer cell clones in LNCaP and PC3 compared to the nc-MECR group (Fig. 6E–H). Wound healing was less efficient in the si-MECR group compared to the nc-MECR group at both 24 and 48 h in LNCaP and PC3 cells (Fig. 6I, J). Additionally, cell migration and invasion were significantly reduced in the si-MECR group, as shown by transwell assays (Fig. 6K–L).

Fig. 6

MECR was proved to promote prostate cancer progression in vitro. A Validation of MECR expression in prostate cancer cell lines; B Knock-down efciency of MECR, respectively LNCaP and PC3; C, D CCK8 assay of MECR in LNCaP and PC3; E–H Clone test of MECR in LNCaP and PC3; I, J wound healing assay of MECR in LNCaP and PC3;scale bar, 100 μm. K, L migration and invasion assay of MECR in LNCaP and PC3;scale bar, 40 μm. *Each experiment was repeated at least 2 times.

Immunohistochemical experiments on benign prostatic hyperplasia (BPH) and PCa samples revealed higher MECR expression in PCa samples, indicated by higher IHC scores. The results indicated that MECR had a higher IHC score in PCa samples compared to BPH, suggesting elevated MECR expression in PCa (Fig. 7A, B). Using the Human Protein Atlas (HPA) database, we confirmed that MECR protein is weakly stained in normal prostate tissues but highly expressed in PCa, particularly in high-grade cases (Fig. 7C, D), aligning with our clinical sample findings.

Fig. 7

Expression of MECR in prostate cancer samples. A Immunohistochemistry of MECR in clinical samples from our centre, including BPH and PCa samples (n = 10/group); scale bar, 100μm; C Immunohistochemistry of MECR in normal prostate tissue, low grade and high grade prostate cancer using HPA database; scale bar, 200μm B, D IHC score of corresponding slices. *Each experiment was repeated at least 2 times.

Validation of MECR affecting PCa proliferation and lung metastatic capacity in in-vivo experiments

A subcutaneous and lung metastasis model were used to examine MECR involvement in tumour proliferation and lung metastatic capacity in vivo. Six mice were used per group. We collected the subcutaneous tumours and performed immunofluorescence (IF) assays to identify the role of MECR in vivo. The IF results showed a significantly lower proportion of Ki-67-positive cells in the MECR knockdown group than the negative control group, suggesting that MECR contributed to PCa cell proliferation in vivo(Fig. 8A, C). The results of the lung metastasis tumours’ haematoxylin–eosin staining (H&E) revealed a reduction in the size of the pulmonary metastatic lesions (Fig. 8B) and a decrease in the number of pulmonary metastatic nodules in the MECR knockdown group (Fig. 8D). Four out of six mice in the sh-MECR group survived in 30 days, but only one out of six mice in the control group survived in 30 days. The survival analysis demonstrated that the MECR knockdown group exhibited a longer overall survival (n = 6/group) (Fig. 8E). These results suggest that MECR may play a role in tumour proliferation and the ability to metastasize to the lungs, but the effect on metastasis to other vital organs still needs to be further verified.

Fig. 8

MECR was proved to promote prostate cancer progression in vivo. A immunofluorescence analysis for Ki-67 of lung metastasis tumour sections (n = 6/group); B The effects of MECR knock out on lung metastasis and the number of metastases were examined by hematoxylin–eosin staining (H&E) staining (n = 6/group); C, D Quantitative bar graph for A-B; E Overall survival analysis of mice in lung metastasis (n = 6/group). In sh-MECR group, 4 out of 6 survived in 30days. In NC group, 1 out of 6 survived in 30 days. * Each experiment was repeated at least 2 times.

Discussion

PCa causes roughly 7% of cancer deaths in men globally and is the third most common cancer [36]. Early-stage PCa has a favorable prognosis [37], but late-stage PCa has higher mortality due to metastasis [24, 38]. About 40–50% of castration-resistant prostate cancer (CRPC) patients who respond to paclitaxel do not see a lasting drop in PSA levels, with remission typically lasting 6 to 9 months [39, 40].

Tumor cell metabolic remodelling is widely observed in the pathophysiological process of tumor diseases [18, 41]. Tumor cells undergo metabolic remodelling to meet their rapid growth needs, which alters both themselves and the tumor microenvironment [42]. Recently, lipid metabolism has emerged as a crucial area for studying metabolic remodelling in tumours [43]. The efficacy of immunotherapy is influenced by a variety of metabolic processes, including the metabolic demands of immune cells, nutrient competition in the tumour microenvironment, metabolic reprogramming, and other mechanisms. Fatty acids, as the fundamental constituents of lipids, play a crucial role in regulating the biological behaviors of tumor growth, proliferation and metastasis via metabolic processes of uptake, synthesis and oxidation [44,45,46]. Few studies have connected PCa prognosis and treatment to fatty acid metabolism genes. This study aims to identify such genes influencing PCa development, offering new insights for treatment.

Because PCa lacks lymphocytes and macrophage infiltration, it is classified as an immunodeficient or 'cold' tumour [32,33,34,35] and is less effective against various immunotherapies, including immune checkpoint inhibitors [33, 34]. On further analysis, MECR was found to have an association with several immune checkpoints. MECR also co-expressed with many molecules in PCa, as shown in Figure S3. Immune checkpoints are a class of immunosuppressive molecules that can regulate immune responses to avoid normal tissue damage and destruction, while mediating immune tolerance during tumourigenesis and progression. Immune checkpoint inhibitors play an important role in cancer therapy by inhibiting immune checkpoint molecules to reactivate T cells [29, 30, 47, 48]. MECR expression significantly affects the response to immune checkpoint blockade and MSI scores in cancer versus paracancerous tumors. This suggests MECR may impair immune cell function and modulate immune checkpoints, allowing tumors to evade immune surveillance.

MECR is an enzyme in the mitochondrial fatty acid synthase (mtFAS) pathway [49]. MECR is highly expressed in mitochondria [50] and is a component of the mitochondrial FAS II pathway. It catalyses the production of octanoic acid from 2E-enoyl-acyl-carrier protein, generating octanoic acid [51, 52]. Mutations in the MECR gene cause disorders of mtFAS II, resulting in neurodegenerative disorders with symptoms such as early-onset dystonia, optic nerve atrophy, and abnormal signalling in the basal ganglia [52, 53]. The property of targeting mitochondria making MECR might serve as a unique biomarker or therapeutic target, comparing to SREBPs, ACSS3 or other lipid metabolism genes. In Hidradenitis elegans cryptic rod nematodes, it was found that nematode lifespan was prolonged after direct knockdown of MECR [54]. Studies related to hepatocellular carcinoma cells have shown that knockdown of MECR promotes apoptosis and inhibits metastasis [55].

MECR is a protein-coding gene crucial for fatty acid synthesis and mitochondrial lipid metabolism [56, 57]. Mutations or abnormal expression can disrupt these processes, highlighting its importance in maintaining normal lipid functions. Therefore, MECR genes play a crucial role in maintaining normal fatty acid synthesis and lipid metabolism. Empirical studies have demonstrated that MECR genes are expressed in adipocytes and tissues such as the liver, where they are integral to the regulation of fatty acid synthesis and metabolic equilibrium [56, 57].

PCa, an androgen-dependent malignant tumor, is characterized by an abnormal increase in androgens, which serves as a signal for the activation of the androgen receptor (AR) pathway, thereby promoting PCa development and progression [58]. It has been observed that lipid metabolism disorders in prostate tumor cells lead to lipid accumulation in the cytoplasm, which can accelerate PCa progression and diminish sensitivity to docetaxel and endocrine therapy [59]. Consequently, fatty acid metabolism is intricately linked to the pathophysiology of PCa. However, the role of MECR in PCa remains unclear.

This study shows that the MECR gene, linked to fatty acid metabolism, plays a role in the development and progression of prostate cancer (PCa). High MECR expression may enhance PCa malignancy, including proliferation, invasion, and migration. Its expression correlates with clinical features, survival, and prognosis, indicating strong clinical relevance. The biological mechanism of MECR in prostate cancer may be more focused on the involvement in the regulation of fatty acid metabolism than classical prostate cancer markers such as PSA, PTEN, and Ki-67. Clinical samples confirmed elevated MECR levels in PCa. Analysis of MECR mutation and methylation levels indicates its role in PCa progression. Promoter sequencing shows low MECR methylation in PCa. MECR is also linked to the immune microenvironment and cells in PCa patients, making it a potential target for future treatment and prognosis. To be specific, this study demonstrates a correlation between MECR and specific immune cells, a finding that is of significant importance for furthering our understanding of the PCa microenvironment. For example, in the tumour microenvironment, natural killer (NK) cells release granzymes and perforin to kill target cells. However, transforming growth factor beta (TGF-β), which is enriched in the TME, inhibits their killing activity. Alterations in MECR expression may affect the function of NK cells, which in turn affects tumour immunosurveillance [60]. Furthermore, the in vitro and in vivo experiments suggest that MECR may play a role in tumour proliferation, migration and the ability to metastasize to the lungs.

The study has several limitations. Firstly, we only chose GSE6919 for analysis in this article. Inadequate sample size may lead to biased data. So we should include additional information regarding the number of sequencing samples, replications of in vivo experiments, and replications of in vitro experiments to avoid data bias. Secondly, the estimates of immune cell infiltration provided by the TIMER database are based on computational algorithms extrapolated from tumour transcriptome profiles. It should be noted that these estimates may differ from the actual immune cell composition. Thirdly, the specific mechanisms by which MECR affects prostate cancer have not been elucidated in this study. Fourthly, this article lacks an assessment of vital organs other than the lungs, which is a major limitation. In the future, we hope to add experiments such as IVIS imaging to further assessed other vital organs in which metastatic lesion would have been formed. Finally, further exploration of the role of MECR in other metabolic processes besides lipid metabolism may also be conducted in the future.

Conclusion

MECR has significant potential for research and application in the assessment of tumour prognosis, the regulation of the immune microenvironment, the development of biomarkers and the identification of immunotherapy targets. Nevertheless, further research is required to gain a more profound comprehension of the precise mechanism of action of MECR and to substantiate its efficacy as a biomarker and therapeutic target in clinical practice.