Methods
2.1 Data sets
The Patient clinical annotation and gene expression data used in this study were obtained from publicly available databases. The TCGA lower grade glioma and glioblastoma (GBMLGG) dataset, which included genomic data and phenotypic data, was obtained from the University of California, Santa Cruz, Xena browser (https://xenabrowser.net/). Another cohort of glioma patients (LGG and GBM) was obtained from Chinese Glioma Genome Atlas (CGGA, http://www.cgga.org.cn/) and the mRNA sequencing data (RSEM) and clinical data were downloaded.
2.2 Differential expression analysis
Gene Expression Profiling Interactive Analysis (GEPIA) is an interactive web platform for gene expression analysis, which includes 9,736 tumors and 8,587 normal samples from TCGA and GTEx databases and its gene expression data have been re-computed from raw RNA-Seq data based on the UCSC Xena project and a uniform pipeline for solving the imbalance between tumor and normal data27. The differential expression analysis of PDIA4 between gliomas and normal brain tissues was performed using GEPIA.
2.3 Survival analysis
Kaplan–Meier survival analysis and the Cox proportional hazard model were used to estimate the prognostic value of PDIA4 based on TCGA and CGGA datasets using R language packages (survival and survminer).
2.4 Gene ontology (GO) enrichment analysis
The functional enrichment analysis, including gene ontology (GO) analysis comprised of cellular component (CC), molecular function (MF), and biological process (BP), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, were performed via the cluster Profiler package in R language28. Enriched ontological terms with adjusted P value < 0.05 were regarded as statistical significance.
2.5 Analysis of stromal and immune infiltration
Analysis of stromal and immune infiltration was performed as described in our previous article29. The scores, calculated by the ESTIMATE algorithm30, were downloaded from https://bioinformatics.mdanderson.org/estimate/. The pre-calculated TCGA data based on xCell31 was downloaded from http://xcell.ucsf.edu/. Then the correlation between PDIA4 expression and ESTIMATE scores and 64 cell types from the TCGA glioma dataset were analyzed using R language.
2.6 Protein-protein interaction (PPI) analysis
The Search Tool for the Retrieval of Interacting Genes32, an online database, was used to identify proteins that can interact with PDIA4 and construct PPI networks.
2.7 Cell lines and culture
The human glioma cell lines (U87, U251, and T98G) and the normal glial cell line HEB were cultured in DMEM with 10% FBS and antibiotics (100 μg/ml penicillin and 100 μg/ml streptomycin), and maintained in standard culture condition.
2.8 RNA extraction and Real-time RT-PCR
Total RNA was extracted from cell lines or human tissues by Trizol reagent (Invitrogen) according to the manufacturer’s protocol. Then, total RNA was quantified and 1 μg of RNA was reverse-transcribed with the Reverse Transcription Kit (Thermo Fisher Scientific). Q-PCR was performed using SYBR Premix Ex Taq II (Takara Bio). β-Actin mRNA was used to normalize the expression of genes. The primers used were showed as follows: PDIA4: F: 5’- GGCAGGCTGTAGACTACGAG-3’and R: 5’- TTGGTCAACACAAGCGTGACT-3’ GAPDH: F: 5’-GGGAGCCAAAAGGGTCAT-3’ and R: 5’-GTCCTTCCACGATACCAA-3’.
2.9 Statistical analysis
Statistical computations and the creation of figures were performed with several packages (ggplot2, survival, survminer, corrplot) in the statistical software environment R, version 3.5.3 (http://www.r-project.org).