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).