Background
Clear cell renal cell carcinoma (ccRCC) is the most common type of renal malignancy in adults. Due to a lack of early-warning signs, approximately 30% of ccRCC patients present with metastatic disease at diagnosis [
1,
2]. Unfortunately, there are no satisfactory treatment options for patients with advanced-stage disease, as ccRCC is inherently resistant to radiotherapy and chemotherapy [
2]. Thus, understanding ccRCC pathogenesis and establishing novel potential therapeutic approaches are of great clinical importance.
Apart from genetic variation, the involvement of lipid metabolism in the tumourigenesis and progression of ccRCC has recently been suggested. Ohno et al. [
3] conducted a study enrolling 364 patients with ccRCC and concluded that higher preoperative levels of blood cholesterol are associated with better cancer-specific survival. The results of previous studies also indicated that serum cholesterol, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol levels are correlated with the risk and pathological characteristics of RCC [
4,
5]. In addition, several enzymes controlling the synthesis and metabolism of fatty acids and cholesterols are reported to play important roles in the tumourigenesis and progression of this disease [
6‐
8].
As a main component of the cell membrane, phospholipids are essential for maintaining normal cellular structures and biological functions [
9‐
11]. The involvement of phospholipids in the development and progression of some malignancies has been reported [
12]. Several studies have investigated the alterations of phospholipids in ccRCC. Lin et al. [
13] performed a LC-MS-based serum metabolomics analysis in ccRCC patients and healthy controls, and indicated that RCC is closely associated with disturbed phospholipid catabolism and sphingolipid metabolism. Cifkova et al. [
14] and Saito et al. [
15] investigated the phospholipid compositions in RCC and normal renal tissues, and found significantly difference among two groups. However, the mechanisms and effects of phospholipid alterations in ccRCC are not well understood.
In the present study, we performed liquid chromatography tandem mass spectrometry (LC/MS/MS) to compare the phospholipid composition of ccRCC and adjacent normal renal tissues, and conducted functional analyses in ccRCC cell lines to elucidate the biological effects of LPCAT1, which plays important role in the metabolism of phospholipid, in the development and progression of ccRCC.
Methods
Patients and clinical specimens
The protocol utilized in the present study was reviewed and approved by the Ethics Committee of Peking University People’s Hospital (No. 2016PHB073), and informed consent was obtained from all participants. Thirty patients who underwent partial or radical nephrectomy during February 2016 to May 2016 were enrolled in the present study. The mean age of the patients was 59.4 ± 11.5 years (ranging from 37 to 81 years), and 70.0% of the subjects were males. All participants were pathologically diagnosed with ccRCC, and none of the patients received antitumour therapy prior to surgery. Paired ccRCC and normal renal tissues were snap frozen in liquid nitrogen immediately after resection and stored at -80 °C. Subsequently, the resected specimens were used for lipidomics analysis and RNA or protein isolation.
Analysis of tissue and cell lipidomics by LC/MS/MS
The LC/MS/MS system used in the present study included an Ultimate 3000 ultra-high-performance liquid chromatograph and a hybrid quadrupole orbitrap mass spectrometer Q Exactive (Thermo Fisher Scientific, Waltham, MA). Full scan phospholipid quantification was performed, and the data were normalized based on the tissue weight or cell number. The normalized data were further exported to MetaboAnalyst 3.0 for multivariate analysis. Both principal component analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA) were used for modelling the difference between tumour and normal tissues. Details are provided in the Additional file
1.
RNA extraction and quantitative real-time PCR (qRT-PCR)
Total RNA from tissue and cell samples was extracted using TRIzol Reagent (Invitrogen, Carlsbad, CA, USA). Complementary DNA was generated from 1.5 μg of total RNA using SuperScript III (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. Subsequently, qRT-PCR was performed on an Opticon 2 PCR instrument (Bio-Rad Laboratories, Hercules, CA, USA) using Brilliant II SYBR® Green QPCR Master Mix (Agilent Technologies, Santa Clara, CA, USA). The transcription levels of target genes were normalized to GAPDH and calculated using the 2
-△△Ct method. The experiments were repeated at least three times. The primer sequences used in the present study were summarized in Table
1.
LPCAT1 | 5’ | CACAACCAAGTGGAAATCGAG |
| 3’ | GCACGTTGCTGGCATACA |
LPCAT2 | 5’ | AAAGCGAACAACATCAGGAG |
| 3’ | GAGCTGGCAGAAAGTAAGCA |
LPCAT3 | 5’ | ATCACTGCCGTCCTCACTAC |
| 3’ | AGTCAACAGCCAAACCAATC |
LPCAT4 | 5’ | TTGTGGATGTGGAGTTCCTT |
| 3’ | ACTTTTCCCAGTTCCCAGAG |
GAPDH | 5’ | GGGCTGCTTTTAACTCTGGT |
| 3’ | TGATTTTGGAGGGATCTCGC |
Protein extraction and western blotting
Total protein from tissue and cell samples was lysed in RIPA buffer (Solarbio, Shanghai, China) containing freshly added protease and phosphatase inhibitor cocktail (Thermo Fisher Scientific, Waltham, MA), and the protein concentration was quantified using the BCA Protein Assay Kit (Solarbio, Shanghai, China). Subsequently, equal quantities of protein were separated using sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto nitrocellulose membranes. After blotting with 5% non-fat milk or BSA, the membranes were incubated with anti-LPCAT1 (1:1000; Proteintech, Chicago, IL, USA) or anti-GAPDH (1:3000; Cell Signalling Technology, Danvers, MA, USA) overnight at 4 °C, followed by incubation with horseradish peroxidase-conjugated secondary antibodies for 1 h at room temperature. The immunoreactive bands were visualized using a chemiluminescence kit according to the manufacturer’s instructions (ECL; Millipore, Billerica, MA, USA). The signal intensities were quantitated using Quantity One software (Bio-Rad Laboratories, Hercules, CA, USA), and GAPDH was used as an internal control. The experiments were repeated at least three times.
Human ccRCC tissue array and immunohistochemistry (IHC)
A ccRCC tissue microarray (TMA) was purchased from Shanghai Outdo Biotech (Shanghai, China) to detect the expression of LPCAT1. This TMA contained 180 spots from 150 ccRCC patients, including paired ccRCC tissues and corresponding normal renal tissues from 30 patients and 120 ccRCC tissues from 120 patients. The demographic and clinicopathological data for each patient were available. The follow-up time ranged from 5.4 to 7.5 years, and all patients were followed up until August 2015. IHC was performed as previously described [
16], and the rabbit polyclonal LPCAT1 antibody (Proteintech, Chicago, IL, USA) was used at a dilution of 1:600. The colorectal adenocarcinomas specimens were used as positive control. The intensity was quantified by two pathologists blinded to the clinical status of the patients, and the results were categorized according to the staining intensity of LPCAT1 as follows: -, negative; +, weak; ++, moderate; and +++, intense.
Cell culture and siRNA transfection
ACHN and 769P cell lines were purchased from the Cell Bank of the Chinese Academy of Sciences, Beijing, China. The cells were routinely cultured in MEM/EBSS or RPMI 1640 media supplemented with 10% fetal bovine serum, 100 U of penicillin and 100 μg/mL of streptomycin at 37 °C in a humidified 5% CO2 atmosphere. To knock down LPCAT1 expression, ACHN and 769P cells were transfected with siRNAs using Lipofectamine® 3000 (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. The following sequences were used for the two LPCAT1 siRNAs and the non-specific control siRNA (NC): si-LPCAT1-1: 5’-GAUCCAGUAUAUACGGCCUTT-3’ (sense), 5’-AGGCCGUAUAUACUGGAUCTT-3’ (antisense); si-LPCAT1-2: 5’-CCUGCCUAAUUACCUUCAATT-3’ (sense), 5’-UUGAAGGUAAUUAGGCAGGTT-3’ (antisense); and si-NC: 5’-UUCUCCGAACGUGUCACGUTT-3’ (sense), 5’-ACGUGACACGUUCGGAGAATT-3’ (antisense).
Cell proliferation assay
Cell proliferation was analysed using a Cell Counting Kit-8 (CCK-8, Dojindo Laboratories, Kumamoto, Japan) and a colony formation assay. For the CCK-8 assay, the cells were seeded onto 96-well plates at a density of 2000 cells per well and subsequently transfected with the corresponding siRNAs. At the indicated time points, 10 μL of CCK-8 was added to each well and incubated at 37 °C for 2 h. Subsequently, the absorbance of live cells was measured at 450 nm using a microplate reader (Bio-Rad Laboratories Inc., Tokyo, Japan). For the colony formation assay, the cells were seeded onto 6-well plates at a density of 2000 cells per well and transfected with the indicated siRNAs. The transfected cells were cultured for 2 weeks, and subsequently, the cells were fixed with 4% paraformaldehyde and stained with crystal violet. The experiments were repeated at least three times.
Cell cycle assay
The cell cycle assay was performed using a cell cycle staining kit (Multi Sciences, Hangzhou, China) according to the manufacturer’s instructions. Briefly, the cells were harvested and washed with PBS. After incubating with DNA staining solution and permeabilization solution for 30 min at room temperature, the cells were measured using a CytoFLEX flow cytometer (Beckman Coulter, Miami, FL, US). The cell cycle distribution was analysed using ModFit software (Verity Software House, Topsham, ME). The experiments were repeated at least three times.
Cell migration and invasion assay
The cell migration and invasion assays were performed using cell culture inserts and a Matrigel Invasion Chamber (8 μm pore size; Corning). A total of 5 × 104 cells were resuspended in 200 μL of serum-free medium and seeded into the upper chamber; 600 μL of medium containing 10% FBS was filled in the bottom chamber. The transwell plates were cultured at 37 °C for 20 h (migration) or 36 h (invasion), and the cells on the upper surface of the membrane were removed. The cells that passed through the membrane were fixed with 4% paraformaldehyde and stained with crystal violet. The migrating or invading cells were counted under a microscope in five random fields. All assays were repeated at least three times.
Statistical analysis
Continuous variables are reported as the means ± standard error of the mean (SEM), and the categorical variables are presented as proportions. Student’s t-test or χ
2 test was used to compare the differences between different experimental groups. The association between LPCAT1 expression and the clinicopathological characteristics was tested using the Kruskal-Wallis test or the Cochran-Mantel-Haenszel test, as appropriate. The Kaplan-Meier method and the log-rank test were performed to detect the survival differences between the groups. All statistical analyses were performed using SPSS software, version 13.0 (SPSS Inc., Chicago, IL, USA). All p values were 2-tailed, and p < 0.05 was considered statistically significant.
Discussion
As a relatively new concept in oncogenesis, lipid profile alterations have been implicated in the development and progression of various cancers, such as breast cancer, hepatocellular carcinoma and prostate cancer [
20‐
23]. ccRCC is characterized by sterol storage in the tumour cytoplasm, suggesting that alterations in lipid metabolism may also be involved in the formation and progression of this disease. Phospholipid is a main component of the cell membrane, and several studies have reported the alterations of phospholipids in ccRCC. Lin et al. [
13] performed a LC-MS-based serum metabolomics analysis to discriminate RCC patients from healthy controls, achieving 100% sensitivity in detection, and indicated that RCC is closely associated with disturbed phospholipid catabolism and sphingolipid metabolism. Cifkova et al [
14] used hydrophilic interaction liquid chromatography coupled to electrospray ionization mass spectrometry to analysis the differences among lipid species in RCC and surrounding normal tissues from 20 kidney cancer patients. They found the lipid class concentrations were significantly different between the two groups, with PE, LPC and SM showing the highest variance. Levels of PE, SM and LPC were decreased in ccRCC tissues compared to surrounding normal tissues. Saito et al [
15] compared levels of 326 lipids (including phospholipids, sphingolipids, neutral lipids and eicosanoids) in ccRCC and surrounding normal renal tissues, and found the cancerous tissues contain higher levels of ether-type PCs, cholesterol esters and triacylglycerols, and lower levels of PE, phosphatidylinositol (PI) and SM. The total levels of the PC lipids were comparable between the two groups, but 48% of the PC molecules were present in significantly higher levels in cancerous tissues. In addition, they suggested that the PE synthesis pathway is suppressed in ccRCC since several enzymes involved in this process were reduced in cancerous tissues. However, the mechanisms and effects of phospholipid alterations, especially PC species, in ccRCC are not well understood.
In the present study, we investigated the phospholipid compositions of cancer and adjacent normal renal tissues from 30 ccRCC patients and revealed obvious phospholipid alterations in cancer tissues compared with normal tissues. We found levels of PE and SM were significantly reduced in cancerous tissues, which were in line with previous studies [
14,
15]. Besides, we noticed multiple LPC species decreased and corresponding PC species increased in cancerous tissues. These findings were consistent with the serum phospholipid alterations in RCC patients reported previously, which found a distinct decline of LPC in ccRCC patients [
13,
24]. Therefore, we propose selective changes in PC and LPC compositions might fuel the tumourigenesis of ccRCC.
LPC and PC can convert to each other through consecutive deacylation and reacylation reactions, referred to as the Lands cycle. The crucially important enzymes controlling these biochemical processes are LPCATs and PLA
2. LPCATs are responsible for the conversion of LPC to PC, and PLA
2 catalyses the generation of LPC from PC [
25]. To date, 4 LPCAT subtypes have been identified [
26]. Among these subtypes, LPCAT1 has attracted much attention from oncology researchers. Several studies have reported that LPCAT1 is overexpressed in several kind of cancer tissues and contributes to the development and progression of cancers. Thus, we hypothesized that the accumulation of PC and reduction of LPC in ccRCC tissues likely reflects the overexpression of LPCATs, and this accumulation might promote the tumourigenesis and progression of ccRCC.
To confirm this hypothesis, we evaluated the expression of LPCATs in ccRCC tissues and matched normal renal tissues. The results showed that LPCAT1 is up-regulated at both transcript and protein levels in ccRCC tissues, while the LPCAT2, LPCAT3 and LPCAT4 levels were comparable between the two groups. Similarly, higher LPCAT1 expression was reported in several other malignant tumours compared to normal tissues, including colorectal adenocarcinoma [
12], prostate cancer [
23,
27,
28], hepatocellular carcinoma [
22], gastric cancer [
29], breast cancer [
30] and oral squamous cell carcinoma [
31].
In addition, we evaluated the association of LPCAT1 expression and clinicopathological characteristics and clinical outcomes. The results showed LPCAT1 expression in ccRCC was significantly correlated with unfavourable pathological features (higher tumour grade, higher TNM stage and larger tumour size) and clinical outcomes. In accordance with our findings, Zhou et al. [
28] indicated the expression of LPCAT1 in metastatic prostate cancer was higher than primary prostate cancer, and the LPCAT1 level was correlated to the tumour grade and stage.
Next, we performed functional analyses in ccRCC cell lines to further evaluate the effect of LPCAT1. The in vitro knockdown experiment revealed that LPCAT1 plays a crucial role in the development and progression of ccRCC. The down-regulation of LPCAT1 could not only suppress cellular proliferation and induce cell cycle arrest but also inhibit the migration and invasion of ccRCC cells.
Taken together, these results indicated that LPCAT1 overexpression contributes to the development and progression of ccRCC. However, the underlying mechanisms remain unclear. Previous studies have indicated that the overexpression of LPCAT1 facilitates the conversion of LPC to PC, and the increased synthesis of membrane PC is required in tumourigenesis. Alterations of membrane PC levels can influence cell proliferation and membrane fluidity, which facilitate cancer cell growth and metastases [
32,
33]. In the present study, we observed significant phospholipid alterations and the accumulation of several PC species in ccRCC tissues, and knockdown of LPCAT1 could deplete PCs and inhibited proliferation, migration and invasion abilities of ccRCC cell lines. Thus, the overexpression of LPCAT1 in ccRCC breaks the balance of phospholipid metabolism. Alterations of phospholipids enhance cell proliferation and membrane fluidity and eventually lead to the development and progression of ccRCC.
In conclusion, we elucidated LPCAT1 exerts an important role in the development and progression of ccRCC, likely via alterations in the phospholipid profile. To our knowledge, this study is the first to analyse LPCAT1 expression in ccRCC tissues and examine its impact on the development and progression of this disease. These findings will provide a foundation for potential novel therapeutic approaches and highlight the important role of phospholipid metabolism in ccRCC biology.
Acknowledgements
We thank all the patients for their participation in this study. We thank Huixin Liu, Xiaowei Zhang and Xiongjun Ye at Peiking University People’s Hospital for useful discussion.