PLA2G16 expression predicts prognosis and gemcitabine sensitivity in patients with pancreatic cancer

Data acquisition

To investigate the impact of PLA2G16 expression on pancreatic cancer patients, we acquired a pancreatic cancer patient dataset comprising clinical information and gene expression profiles from the publicly accessible repository, The Cancer Genome Atlas (TCGA) (Blum, Wang & Zenklusen, 2018). Specifically, we focused on tumor tissues that received gemcitabine treatment, and any missing cases were appropriately handled. The RNA sequencing data underwent a transformation process to convert the gene expression values to a format that closely approximated microarray results, enabling compatibility and comparability in subsequent analyses. According to the expression level of PLA2G16, tumor tissues were divided into high expression group and low expression group to evaluate the effect of PLA2G16 expression on tumors. To complement our study, SW1990 cell samples after gemcitabine treatment were retrieved from the Gene Expression Omnibus (GEO) database. The dataset (GSE172303) comprised cell lines exhibiting sensitivity to gemcitabine and cell lines demonstrating resistance to gemcitabine. We specifically focused on screening the expression levels of PLA2G16 in relation to the cell line samples.

Correlation analysis between overall survival and PLA2G16 expression in patients with pancreatic cancer

To study the relationship between the expression of PLA2G16 and clinicopathological features in pancreatic cancer, we employed GEPIA, an online tool widely utilized for gene expression analysis in cancer research (Tang et al., 2017). By leveraging GEPIA, we conducted survival curve analysis based on differential PLA2G16 expression to examine its correlation with the prognosis of pancreatic cancer patients.

Gene differential expression and functional enrichment analysis

Using RStudio, differentially expressed genes (DEGs) between PLA2G16 high expression and low expression group were identified and subsequently visualized through the generation of a volcano plot, which displays the significance and fold change of gene expression differences between the groups. Additionally, a heat map was constructed to provide a comprehensive overview of the expression patterns of the top DEGs, allowing for the visualization of potential gene expression signatures associated with PLA2G16 expression levels in pancreatic cancer. To further understand the biological processes and pathways associated with DEGs between the PLA2G16 high and low expression groups, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the DAVID website. Terms in GO and KEGG with an adj.P.Val below 0.05 were considered significantly enriched. To visualize and interpret the enriched terms from the GO and KEGG analyses, a bioinformatics website (https://www.bioinformatics.com.cn) was employed.

Cell culture

The human pancreatic cancer cell lines PANC-1 and BxPC-3 were obtained from Sun Yat-sen University and the Cell Bank of the Chinese Academy of Sciences (Shanghai, China), respectively. PANC-1/GR and PANC-1 cells were cultured in DMEM medium (Cat# L110KJ; Shanghai Yuanpei), while BxPC-3/GR and BxPC-3 cells were maintained in RPMI-1640 medium (Cat# L210KJ, Shanghai Yuanpei). Both media were supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. For resistant cell lines (PANC-1/GR and BxPC-3/GR), 5 μM gemcitabine was added to maintain drug resistance. All cells were incubated at 37 °C in a humidified 5% CO₂ atmosphere.

Construction of drug-resistant cells

BxPC-3 and PANC-1 cells within 5–10 passages after thawing to minimize genetic drift were treated with gemcitabine (Cat#HY-17026; Med Chem Express, Monmouth Junction, NJ, USA) at an initial concentration of 0.50 μM (1/4 IC₅₀ for BXPC-3) or 0.49 μM (1/4 IC₅₀ for PANC-1). The medium was replaced every 48 h with fresh medium containing the same concentration of gemcitabine. The gemcitabine concentration was incrementally increased by 0.50 μM every 1–2 weeks until reaching 50 μM. Drug-resistant cells (BxPC-3/GR and PANC-1/GR) were continuously cultured for 3 months under these conditions.

CCK-8 assays

Cell viability was assessed using the Cell Counting Kit-8 (Meilunbio, Dalian, China). PANC-1/GR cells were cultured in complete DMEM medium (10% FBS, 1% penicillin/streptomycin) at 37 °C/5% CO₂ until 70–80% confluent, then harvested using 0.25% trypsin, neutralized with complete medium, and centrifuged to obtain a cell pellet. After resuspension with cell culture medium and counting, cells were adjusted to 5,000 cells/100 μL in complete medium and seeded into 96-well plates (100 μL/well), followed by overnight incubation to allow adhesion. To calculate the resistance index of different cell lines, cells were then treated with gemcitabine at concentrations of 0, 4, 8, 16, 32, 64, and 128 μM for 48 h. Subsequently, 10% CCK-8 reagent was added to each well, and plates were incubated for 2 h at 37 °C. Absorbance at 450 nm was measured using a multifunctional microplate reader. Cell viability (%) is calculated by normalizing absorbance values against untreated controls, and IC₅₀ is derived by fitting dose-response data to a sigmoidal curve using GraphPad Prism 8.0.

Western blot analysis

Cells were cultured under standard conditions. Upon reaching a confluency of 80%, the cells were harvested, and protein extraction was performed. Subsequently, Western blot analysis was carried out. Cells were collected to prepare whole-cell lysates using cell lysis buffer supplemented with protease and phosphatase inhibitors. The protein content was measured using BCA Protein Assay Kit. Then, 20 µg of protein was separated by SDS-PAGE and transferred to PVDF membrane. By continuous shaking, the membrane was then incubated in 5% skim milk for an hour at room temperature. The membranes were then incubated with the anti-PLA2G16 antibody (Cat#A16018; Abclonal, Woburn, MA, USA) and anti-β-actin antibody (Cat#AC038; Abclonal, Woburn, MA, USA) overnight at 4 °C, followed by a PBST wash (3 × 10 min) and incubation with the appropriate secondary antibody for 2 h at room temperature. After that, the membrane was again washed with PBST (3 × 10 min) on a shaker. Finally, the protein bands were recorded using ECL detection reagents.

RT-qPCR analysis

Total RNA was extracted from cultured cell lines using TRIzol reagent (Precision Biology, Durham, NC, USA), and concentrations were determined using a NanoDrop ND-2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The measured concentrations of total RNA were reverse transcribed into cDNA using 4× gDNA wiper Mix and 5× HiScript III qRT SuperMix (Vazyme, Beijing, China). 2× ChamQ Universal SYBR (Vazyme, Beijing, China), CFX96 Touch (Bio-Rad, Hercules, CA, USA) were used for PCR detection. Assay results were normalized to GAPDH expression and calculated using the 2^−∆∆CT method. The primers used were as follows:

human PLA2G16, 5′-CCAGGTCAACAACAAA-CATGATG-3′ (forward) and 5′-CCCGCTGGATGATTTTGC-3′ (reverse);

human GAPDH, 5′-TGATGACATCAAGAAGGTGGTGAAG-3′ (forward) and 5′-TCCTTGGAGGCCATGT-GGGCCAT-3′ (reverse).

siRNA transfection

The targets sequences of siPLA2G16 were GGAGUCAUGUUCUCAAGAATT (siPLA2G16-1) and GCGAGCACUUUGUGAAUGATT (siPLA2G16-2). Negative controls and siRNAs were synthesized by GenePharma (Shanghai, China). Transient transfection was performed using Lipo2000 reagent (Vazyme, Beijing, China). RT-qPCR was conducted to detect transfection efficiency.

Colony formation assays

For the colony formation assay, approximately 4 × 10³ PANC-1/GR cells per well were seeded in 6-cm cell culture dishes and maintained for approximately 15 days. Following incubation, cells were washed 2–3 times with PBS and fixed with 4% paraformaldehyde. After removing the fixative with PBS, the cells were stained with crystal violet. Images were captured using an appropriate camera, and colonies were quantified using ImageJ. The resulting data were subsequently analyzed using GraphPad Prism 8.0 for statistics analysis.

Wound-healing assay

Approximately 7 × 10⁵ PANC-1/GR cells per well were seeded in 6-well plates and cultured until the cell density exceeded 90%. A 200 μL pipette tip was then used to scratch the bottom of the wells, creating a cell-free area. The cells were subsequently cultured for 24, 48, and 72 h. Scratch area was monitored using a photographic microscope (Nikon, Tokyo, Japan), and scratch width was quantified using ImageJ software.

Genetic mutation analysis

The potential involvement of PLA2G16 mutations was investigated using data extracted from the TCGA dataset through CBioPortal (https://www.cbioportal.org/). The mutation and structural variant analysis of the PLA2G16 gene was conducted utilizing the “OncoPrint” module available on the cBioPortal platform.

Immune infiltration analysis

To evaluate the immune response and assess its correlation with survival and molecular subpopulations in pancreatic cancer, we employed CIBERSORT (Gentles et al., 2015), a deconvolution algorithm based on gene expression analysis. By evaluating gene expression changes relative to the entire gene set, CIBERSORT is able to accurately estimate the frequency of 22 tumor immune-infiltrating cell types (TIICs) in a sample.

Statistical analysis

The data were processed and analyzed using GraphPad Prism 8.0. Cell viability was evaluated using five biological replicates per group. Data for the colony formation and wound-healing assays were derived from three independent experiments. Statistical differences between two groups with a normal distribution were assessed using Student’s t-test. For comparisons involving more than two groups with a normal distribution, analysis of variance (ANOVA) was performed, followed by Dunnett’s multiple comparisons test. A p-value of less than 0.05 was considered statistically significant.

PLA2G16 is highly expressed in pancreatic cancer

Figure 1A illustrates the expression of PLA2G16 in pancreatic cancer, as extracted from GEPIA, revealing a significant elevation in PLA2G16 expression within tumor tissues compared to normal tissues. Additionally, PLA2G16 protein expression was evaluated utilizing immunohistochemistry (IHC) staining data acquired from the Human Protein Atlas (HPA). In normal pancreatic tissue, PLA2G16 expression predominantly remained low in exocrine glandular cells (depicted in Fig. 1B, left). Conversely, among 10 cases of pancreatic adenocarcinoma (PAAD) in the HPA dataset, four cases exhibited high or medium expression of PLA2G16 (as illustrated in Fig. 1B, right). These cumulative findings corroborate the consistently heightened expression of PLA2G16 in individuals diagnosed with pancreatic cancer, underscoring the potential significant role of PLA2G16 in the pathogenesis of pancreatic cancer.

Additionally, genetic mutations are significant contributors to tumorigenesis and prognosis. To explore the potential impact of PLA2G16 mutations in pancreatic cancer, we examined the mutation frequency and structural variants of the PLA2G16 gene using data obtained from the cBioPortal database, encompassing six studies and 1,174 samples. As depicted in Figs. 1C–1E, the mutation frequency of PLA2G16 in pancreatic cancer appears relatively low, at 0.3%, with amplification identified as the most prevalent genetic alteration, followed by deep deletion.

The expression of PLA2G16 strongly correlates with the OS of pancreatic cancer patients

We then conducted an analysis to investigate the association between PLA2G16 expression and the survival rate of patients with pancreatic cancer, aiming to elucidate the significant role of PLA2G16 in this disease. Clinical data from 178 patients diagnosed with pancreatic ductal adenocarcinoma (PDAC) sourced from The Cancer Genome Atlas (TCGA) are summarized in Table 1. Then, we from cBioPortal database (https://www.cbioportal.org/) to download the TCGA database related clinical information and treatment strategies, conducting clinical data in the download of disease specific survival statistics. Four of these cases had no relevant clinical data despite having gene expression information in the TCGA database. Thus, a total of 174 patients received chemotherapy, endocrine therapy, radiation therapy, and adjuvant therapy, including 102 patients who received gemcitabine. Cox regression analysis and univariate correlation analysis were performed on 174 patients. As shown in Fig. 2A, a statistically significant relationship between PLA2G16 expression and overall survival (OS) in pancreatic cancer patients was confirmed. This analysis encompassed variables such as age, gender, tumor grade, and PLA2G16 expression, revealing that age and PLA2G16 expression stand as independent prognostic indicators (refer to Table 2 and Fig. 2A). Furthermore, patients with pancreatic cancer exhibiting high PLA2G16 expression demonstrated notably shorter OS (Fig. 2B) and disease-free survival (Fig. 2C) periods compared to those with low PLA2G16 expression. Finally, we plotted overall survival among the 102 patients who received gemcitabine (Fig. 2D). Log-rank survival analysis showed that in patients treated with gemcitabine, the overall survival of the PLA2G16 high expression group was significantly lower than that of the low expression group (p < 0.05). Kaplan–Meier survival curves showed that patients with low PLA2G16 expression had a longer survival time, while patients with high PLA2G16 expression had a significantly lower survival rate, suggesting that high PLA2G16 expression may be associated with poor prognosis after gemcitabine treatment. This result suggests that PLA2G16 may be a potential biomarker affecting gemcitabine efficacy or resistance, providing a potential clinical basis for individualized treatment. These results underscore the clinical significance of PLA2G16 as a potential prognostic marker in pancreatic cancer.

PLA2G16 contributes to gemcitabine resistance in pancreatic cancer cells

To complement our study, we retrieved cell samples from the GEO database, including gemcitabine-sensitive or gemcitabine-resistant cell lines (GSE172303). The downloaded original data package contained the FPKM data processed by the original author, and there were three duplicate data. Subsequently, the data were imported into GraphPad Prism 8.0 for statistical analysis. The analysis of PLA2G16 expression levels shown in Fig. 3A showed that the expression level of PLA2G16 was significantly upregulated in gemcitabine-resistant SW1990 cells (SW1990/GR) compared with gemcitabine-sensitive cells (SW1990). To corroborate this finding, we constructed BxPC-3/GR and PANC-1/GR cells and they were subjected to RNA sequencing. The outcomes confirmed a significant elevation in PLA2G16 expression in the gemcitabine-resistant cells relative to their parental counterparts (Figs. 3B and 3C), thereby suggesting a substantive association of PLA2G16 with gemcitabine resistance in pancreatic cancer.

We then examined the expression levels of PLA2G16 protein in sensitive and resistant cells of two cell lines, BxPC-3 and PANC-1. The results showed that the expression of PLA2G16 was significantly increased in drug-resistant cells (Fig. 3D), further proving that PLA2G16 may be a biomarker of drug resistance, and its high expression can be used to identify drug-resistant populations. Subsequently, we measured the IC₅₀ of gemcitabine in pancreatic cancer cell lines by CCK-8 assay to calculate the resistance index of different cell lines. The results indicate that the IC₅₀ values of BxPC-3 and PANC-1 cells were 1.98 and 1.96 μM, respectively (Fig. 3E). In contrast, the IC₅₀ values of the drug-resistant BxPC-3/GR and PANC-1/GR cells were 52.02 and 83.50 μM, respectively (Fig. 3F). Consequently, the resistance indices were calculated as 26.27 for BxPC-3 and 42.6 for PANC-1. As a resistance index exceeding 15 is indicative of a high level of drug resistance, these findings confirm the successful establishment of drug-resistant cell lines.

To further substantiate the potential impact of PLA2G16 on the proliferation and gemcitabine resistance in pancreatic cancer cells, PLA2G16 knockdown was conducted on PANC-1/GR cells using siRNA-PLA2G16. The effect of knockdown was verified by RT-qPCR, revealing a noteworthy decrease in PLA2G16 expression in the knockdown groups (Fig. 3G). Subsequently, CCK-8 assay showed that after PLA2G16 knockdown, cell proliferation could not be completely inhibited, but slowed down. Due to the small number of initial cells, there was no significant difference in growth and proliferation between day 2 and day 3. However, with the passage of time, the proliferation rate of PLA2G16 knockdown group was significantly lower than that of NC group on Day 4 and Day 5, and the difference was statistically significant (Fig. 3H), suggesting the possible influence of PLA2G16 expression on cell viability in pancreatic cancer cells. Furthermore, PANC-1/GR cells (PLA2G16 knockdown group and the control group) were exposed to different concentrations of gemcitabine and cell viability was assessed after 72 h of treatment. The determination of IC₅₀ values for each group unveiled that PANC-1/GR cells with PLA2G16 knockdown exhibited significantly heightened sensitivity to gemcitabine, with notably lower IC₅₀ values (siPLA2G16-1: 28.52 μM, siPLA2G16-2: 6.817 μM) compared to the control group (85.80 μM) (Fig. 3I). Thus, PLA2G16 plays a key role in the regulation of gemcitabine resistance in pancreatic cancer cells. Additionally, PLA2G16 knockdown reduced colony formation (Fig. 3J) and wound healing (Fig. 3L) in PANC-1/GR cells. After the clone formation assay and scratch assay were repeated three times each, the results were visualized using imageJ and imported into Graphpad Prism8.0 for significance analysis. The results showed that after 10 days of proliferation, the growth and proliferation rate of the knockdown group was significantly lower than that of the control group (Fig. 3K), and the scratch healing rate of the knockdown group was significantly higher than that of the control group at 48 h and 72 h (Fig. 3M). These results collectively suggest that PLA2G16 may be a new therapeutic target for pancreatic cancer.

PLA2G16 is implicated in various biological processes in pancreatic cancer

To further elucidate the pivotal role of PLA2G16 in the management of pancreatic cancer, clinical and gene-expression data from TCGA were utilized, totaling 102 primary tumor samples who underwent gemcitabine treatment. These samples were subsequently categorized into two groups based on median PLA2G16 expression level: a high-expression group (51 cases) and a low-expression group (51 cases). Differential gene expression analysis between these two groups was conducted using the limma package in R software, identifying a total of 119 genes exhibiting differential expression, with 77 genes up-regulated and the remainder down-regulated. The resultant DEGs were visualized using a volcano plot (depicted in Fig. 4A) and a heatmap (showcasing the top 20 genes with the most noticeable up-regulation and down-regulation, as depicted in Fig. 4B). To further delineate the functional implications of PLA2G16 in pancreatic cancer cells, the DAVID database was used for GO and KEGG enrichment analysis of 119 DEGs. The top ten enriched categories, including KEGG pathways such as pancreatic secretion, fat digestion and absorption, protein digestion and absorption, alpha-linolenic acid metabolism, linoleic acid metabolism, arachidonic acid metabolism, glycerolipid metabolism, ether lipid metabolism, bile secretion and the Ras signaling pathway, are presented in Figs. 4C–4F. The significance of these enriched pathways suggests a potential involvement of PLA2G16 in the pathogenesis of pancreatic cancer, possibly through the modulation of lipid-related biological processes. These findings offer valuable insights into the molecular mechanism and potential role of PLA2G16 in the development and progression of pancreatic cancer.

The expression of PLA2G16 correlates with immune cell infiltration in pancreatic cancer

To investigate the relationship between PLA2G16 expression and immune infiltration in pancreatic cancer, the gene expression profiles of the aforementioned 102 samples in each respective group were subjected to analysis using the CIBERSORT computational tool. This facilitated the estimation of the densities of 22 distinct immune cell types in both the high and low PLA2G16 expression cohorts. As illustrated in Fig. 5, T cells CD8, T cells CD4 memory activated, regulatory T cells (Tregs), monocytes, M0 macrophages, macrophages M2 emerged as the primary immune cell populations influenced by PLA2G16 expression in pancreatic cancer. Comparative analysis between the high and low expression cohorts revealed a significant increase in Tregs (p = 0.0091) and macrophages M0 (p = 0.0140) within the PLA2G16 high expression group, while T cells CD8 (p = 0.0250), T cell CD4 memory activated (p = 0.0045), monocytes (p = 0.0054), and macrophages M2 (p = 0.0210) exhibited a decrease in the PLA2G16 high expression cohort compared to the low expression cohort. These findings underscore the potential impact of PLA2G16 expression on immune cell composition in pancreatic cancer.