Sample Source
Ovarian cancer tissue (n = 12) and normal ovarian tissue (n = 12) samples were collected from gynecology department in The First Affiliated Hospital of Harbin Medical University, from 3 month to 9 month in 2020. None of the patients were treated before undergoing surgery. The surgically resected specimens were immediately placed in liquid nitrogen and then transferred to a -80°C refrigerator for storage. All patients in the study provided written informed consent for the biological study. The research protocol (including specimen collection) was reviewed and approved by the Biomedical Ethics Committee of The First Affiliated Hospital of Harbin Medical University (Batch Number: 20200159), All procedures were conducted in accordance with the Guidelines of the World Medical Association Declaration of Helsinki. The clinicopathological staging and typing of the patients met the Joint Council on Cancer (AJCC) typing criteria.
Public Data Acquisition and Preprocessing
Using R software (Version 4.1.0, http://r-project.org/) the “GEOquery” package [24] from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) was applied to download the GSE18520 and GSE66957 ovarian expression datasets. The samples in these datasets were sourced from Homo sapiens, and the platform is based on the GPL570 (HG-U133_Plus_2) Affymetrix Human Genome U133 Plus 2.0 Array. The GSE18520 dataset includes 63 samples from 53 ovarian cancer patients and 10 normal samples. GSE66957 includes 57 samples and 12 Normal-ovarian samples from 69 ovarian cancer patients within the dataset. All these data were included in this study.
In addition, count data of ovarian cancer RNA-Seq, single nucleotide polymorphism (SNP) data, and matching clinical data (n = 379) were downloaded from TCGA database using Genomic Data Commons (GDC) software (https://portal.gdc.cancer.gov/projects/). As there is no normal control for ovarian cancer in TCGA, here the obtained TCGA data were combined with GTEx to obtain normal ovarian control download samples (n = 88) and ovarian cancer samples (n = 427). RNA-Seq count data were obtained through the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/datapages/; the data were corrected in batches).
Screening of Differentially Expressed Genes (DEGs)
The differentially expressed genes (DEGs) of the GSE118520 dataset were downloaded through the R package “limma” [25], following which the package “ggplot2” was used to draw a volcano map of the DEGs to show their differential expressions. DEGs were considered significant when they met the thresholds of P <0.05 and | log2FoldChange | >1. Subsequently, DEGs in ovarian cancer and normal samples in the combined TCGA-GTEx dataset were screened using the R package “Deseq2” [26], using the same thresholds as detailed above. Taking the intersections of the DEGs obtained from the two data sets, the candidate gene of interest was then selected for subsequent analysis.
Mutation and CNV Analysis
The somatic mutation data of TCGA-OV patients were extracted by using the R package “maftools” [27]. Somatic mutation data of patients in the high and low gene expression groups were then collected and analyzed.
To analyze the changes in CNVs in TCGA-OV patients within the high gene expression group, the R package “TCGAbiolinks” [28] was used to download the “Masked Copy Number Segment” data of patients. GISTIC 2.0 analysis of the downloaded CNV fragments was then conducted through GenePattern (https://cloud.genepattern.org) [29].
Weighted Gene Co-expression Network Analysis (WGCNA)
The R package “WGCNA” [30] was used to analyze the GSE18520 and TCGA-OV datasets. The samples were divided into high and PTH2R low expression groups. The standardized data were then used to construct a co-expression network. For all functions in WGCNA, the correlations of double weights were used as the correlation method. A topological overlap metric (TOM) was used for network construction and module identification. The calculation parameters minModuleSize = 50 and mergeCutHeight = 1,000 were used to analyze data. Ultimately, the hub genes were obtained from the intersection of the genes in the module with the highest significance, and using the previously obtained DEGs.
Functional Enrichment Analysis
Gene ontology (GO) analysis is commonly used to conduct large-scale functional enrichment studies, including biological process (BP), molecular function (MF), and cellular component (CC) [31]. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a widely used database that stores information about genomes, biological pathways, diseases, and drugs [32]. Here, GO annotation and KEGG pathway enrichment analyses were performed on the hub gene using the R package “clusterProfiler” [33]. A critical value of false discovery rate (FDR) <0.05 was considered to imply statistical significance.
Gene Set Enrichment Analysis (GSEA) is a calculation that analyzes whether a particular set of genes is statistically different between two biological states. It is commonly used to estimate changes in the activities of pathways and biological processes in sample expression datasets. Here, GSEA was conducted to study the differences in biological processes between groups based on the gene expression profile data set of TCGA-OV patients [34]. The gene set “c2.cp.kegg.v7.2.symbols” was downloaded from the MSigDB database [35] for GSEA, and FDR <0.25 and P <0.05 were considered to represent a significant enrichment.
Drug Sensitivity Analysis
The CellMiner database (https://discover.nci.nih.gov/cellminer/) is a web-based tool that contains genomic and pharmacological information for researchers to use transcripts and drug response data from the NCI-60 cell line [36]. The data were compiled by the National Cancer Institute. CellMiner provides transcriptional expression levels for the drug responses of 22,379 genes, 360 microRNAs, and 20,503 compounds [37]. The mRNA expression profiles and drug activity data including the PTH2R gene were downloaded from the CellMiner database. The correlation between PTH2R gene expression and compound sensitivity was calculated through Pearson’s correlation analysis. P <0.05 was considered to represent statistical significance.
The Genomics of Drug Sensitivity in Cancer (GDSC) database(www.cancerrxgene.org/)can be used to search for tumor drug response data and genome sensitive markers [38]. Here, the pRRophetic algorithm [39], the ridge regression model, and IC50 were used to predict the sensitivities of the high and low PTH2R expression groups to common anticancer drugs.
Immune Cell Infiltration Analysis and Tumor Immunoanalysis
CIBERSORT (http://CIBERSORT.stanford.edu/) and the LM22 characteristic gene matrix were used to predict the proportions of 22 immune cells in all samples within the predicted dataset [40]. CIBERSORT was used to assess the abundances of 22 immune cells in TCGA-OV dataset, and to calculate the correlations between these 22 kinds of immune cells. Then, by integrating candidate gene expression, Pearson’s correlations were calculated between the gene expression and these immune-infiltrating cells, with P <0.05 being considered to represent statistical significance.
Cell Culture
The IOSE-80, SK-OV-3, and A2780 cell lines were bought from the American Type Culture Collection (Manassas, VA, USA). All cells were cultured in high glucose Dulbecco’s modified Eagle’s Medium (DMEM, Corning) treated with 10% fetal bovine serum (FBS; Hyclone) and 1% penicillin/streptomycin solution (Invitrogen) in a 37°C humidity, 5% CO2 incubator.
Real-time Fluorescence qPCR
Total RNA was extracted using RNAiso Plus reagent (Takara Bio, Kusatsu, Japan). RNA concentration and purity were assessed using a NanoDrop 2000 system (Thermo Fisher, Carlsbad, CA, USA). Reverse transcription was then performed using the PrimeScript™ RT reagent kit with gDNA eraser (Perfect Real Time; Takara Bio). The SYBR® Premix Ex AQ ™ II (Tli RNaseH Plus; Takara Bio) in ABI 7500 Fast System (Life Technologies, Carlsbad, CA, USA) was used for real-time qPCR; Primers 5’- GAGGAACAGTGGGGAAAATATCG -3’ (Forward) and 5’- TGGGGTTACAGTGTCGGAAAG’ (Reverse) were used for amplification of the entire human PTH2R coding sequence (GenBank accession number NM_005048), sequences used for human GAPDH were GGAGCGAGATCCCTCCAAAAT -3’ (Forward) and 5’- GGCTGTTGTCATACTTCTCATGG’ -3’ (Reverse). The 2−ΔΔCt method was used to calculate gene expressions.
Plasmid Transfection
The PTH2R gene was amplified from HEK293T by standard PCR and then subcloned into pcDNA3.1-HA vector. All plasmids were sequenced. Lipofectamine 2000 (Invitrogen) was used for transfection, according to manufacturer's instructions.
Cell Proliferation Detection
The CCK-8 assay (CCK-8 SAB Biotech. College Park, MD, USA) was used to detect cell proliferation. According to the manufacturer’s protocols, the cells were seeded into six-well plates at a density of 1.0 × 105 cells per well, and were then cultured in medium supplemented with 5% FBS for 24 h (at 37°C and 5% CO2). Then, 24 h after transfection, the cells were digested with trypsin and inoculated in triplicate into 96-well plates (3 × 104 cells per well). Each well was incubated with 10 µL/well CCK-8 solution for 2 h every day, for a total of 5 d. The optical density at 450 nm was measured on a microplate reader. Three independent replications were performed.
Transwell Invasion and Migration Experiments
Transwell experiments were divided into transwell migration and transwell invasion experiments. The basic operations were as follows: transwell cells were placed into a 24-well culture plate, the chamber is referred to herein as the superior chamber and the culture plate is referred to as the lower compartment. The cells were then digested in a serum-free medium, following which the cell density was adjusted to 1 × 106 cells/mL and the sample was inoculated in the upper chamber. Dulbecco's Modified Eagle Medium (DMEM) containing 10% FBS was then added to the lower chamber. Transwell invasion assays were performed by precoating the upper membrane with 40 µL of matrix glue (BD Biosciences, USA); the cells were fixed with 4% paraformaldehyde and were washed with phosphate buffer solution (PBS) after 24 h. Then, they were stained with 0.1% crystal violet (Solarbio, China) and representative images were observed at x100 and x200 magnification with an optical microscope (Olympus, Tokyo, Japan).
Clone Formation Experiment
The cells were digested with 0.25% trypsin until individual cells were obtained; the cell suspension was then diluted to a concentration of 1 × 104 cells /mL. Then, 1,500 cells from the medium were added to each well of a six-well plate and incubated at 37°C in 5% CO2. When the clones are visible to the naked eye in the six-well plate, cell culture was stopped and the cells were fixed in 4% paraformaldehyde for 15 minutes. Crystal violet staining was then performed, and the number of clones for which there were more than 50 cells was counted using an optical microscope.
Immunohistochemical Test
Immunohistochemistry (IHC) was conducted according to the antibody supplier's instructions. Sections of clinical samples were incubated overnight at 4°C with a PTH2R primary antibody at different dilution ratios. Images were captured at appropriate magnification under an optical microscope (Nikon Microsystems, Shanghai, China). The antibody used in this study was anti-PTH2R (Chemicon International and GenWay Biotech).
Western Blot
Total cellular proteins were extracted using radioimmunoprecipitation assay (RIPA) lysates (Beyotime, Shanghai, China). Proteins were isolated and transferred to polyvinylidene fluoride (PVDF) membranes (Millipore, Temecula, CA, USA) by using 7.5% or 10% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE). The membrane was sealed with primary resistance to PTH2R (Chemicon International and GenWay Biotech) at 4°C overnight, following which the membrane was then washed and incubated with secondary antibodies. Protein bands were detected using enhanced chemiluminescence (Thermo Scientific Carlsbad, CA, USA).
Statistical Analysis
All data processing and analysis were completed by R software (version 4.0.2). For the comparison of the two groups of continuous variables, the statistical significance of the normally distributed variables was estimated using the independent Student t test, and the differences between the non-normally distributed variables were analyzed using the Mann-Whitney U test (i.e. Wilcoxon rank-sum test). The Chi-square test or Fisher's exact test was used to compare and analyze the statistical significance between the two groups of categorical variables. P <0.05 was considered statistically significant.