1.1 Data collection
The AS data contained the name of the gene undergoing AS, the ID of the GBM patients in the TCGA SpliceSeq database, the splicing code (the code of AS in TCGA SpliceSeq database), the type of splicing, the location where the splicing occurred, and the Percent Spliced In (PSI) value. The GBM clinical data included patient ID, survival time, survival status, age, and gender. In clinic, 40 GBM patients were recruited from The First Affiliated Hospital of Zhengzhou University were tested by Quantitative Real-Time PCR and Western blot to validate its usability. All patients gave written informed consent and ethical permission was obtained from The First Affiliated Hospital of Zhengzhou University.
1.2 Screening of prognosis-related AS events
The percentages of AS events in GBM patients were supplemented using the Knn function in the R language impute program package and then integrated with the patient's survival time and survival status. Univariate Cox regression analysis was implemented for selecting prognosis-associated AS events in GBM.
1.3 Construction of a prognostic model
To prevent overfitting and improve the accuracy of the model, Lasso regression analysis was adopted for development of a prognostic model by screening prognosis-related AS events. The LASSO model parameters were determined through the 10-fold crossvalidation and selected to yield the minimum mean crossvalidated error. Subsequently, the obtained AS events were incorporated in the multivariate Cox regression analysis for establishment of a prognostic risk model. Calculation formula: risk score = β1X1 + β2X2 + · · · + βnXn, where β represents the regression coefficient of each AS in the model constructed by multivariate Cox regression analysis, and X represents the PSI value for AS events. The risk score of each sample was calculated accordingto the above formula.
1.4 Examination of the prognostic model
To test the accuracy of this prognostic model of GBM patients, the survival and receiver operating characteristic (ROC) curves were drawn based on the risk level. In addition, according to the patient risk value, the heat maps of the survival status of GBM patients and survival-related AS events were plotted. In addition, all patients were divided into two groups as lower and higher levels of risk score. Univariate and multivariate Cox regression analyses were carried out for an in-depth exploration of whether the prognostic model can serve as an independent prognostic factor.
1.5 Establishment and verification of prognostic nomograms
Based on the independent prognostic factors obtained above, a nomogram was constructed for predicting the 1-, 2-, and 3-year OS of GBM patients. A calibration curve was plotted to evaluate the consistency between the results predicted by the nomogram and the observation results so as to assess its distinguishing ability. The closer the calibration curve is to 45°, the better the predictive ability of the nomogram.
1.6 Correlation analysis of tumor microenvironment (TME) and immunity
The "ESTIMATE" method was utilized to compare the differences in TME between the high- and low-risk individuals. Meanwhile, a single sample gene set enrichment analysis (ssGSEA) was performed to compare the differences in immune-related cells and functions between the two groups. We also performed comparison of immune checkpoint-related genes between the two groups.
1.7 Correlation analysis of differentially expressed genes (DEGs) with prognosis and immunity
Among the related genes in the risk model, DEGs were screened with the FDR < 0.05 and |log2FC| ≥ 1 as the threshold, followed by correlation analyses of the DEGs with the prognosis and immunity. In addition, DEGs expression was tested by Real-Time Quantitative PCR (qPCR) and WB (TargetGenes).
1.8 Quantitative Real-Time PCR
Total RNA was extracted from the target tissue samples and thoroughly ground in a mortar under liquid nitrogen. To lyse the cells, 1 ml of Trizol reagent (Life Technology, Grand Island, NY, United States) was added and the sample was incubated for 15 min at room temperature on a shaker. To assess the mRNA expression level, the RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific, Lithuania) was used to synthesis the first-strand cDNA. Quantitative PCR was performed using Roche LightCycler® 480 Real-Time PCR System with SYBR® Green qPCR mix 2.0 kit. The primers used in this study were obtained from TsingKe biological technology (Nanjing, China), including IGF2BP2(forward 5'-AGCTAAGCGGGCATCAGTTTG-3', reverse 5'-CCGCAGCGGGAAATCAATCT-3'), β-actin ( Forward: 5'-CATGTACGTTGCTATCCAGGC-3', Reverse: 5'-CTCCTTAATGTCACGCACGAT-3' ). The relative mRNA levels were calculated by the 2-ΔΔCt method.
1.9 Western blot
Western blot was performed to determine the protein expression level. Samples were isolated and lysed in RIPA buffer with protease inhibitors. Proteins (40 μg) were separated using SDS-PAGE with 10% acrylamide gels. Western blot analysis was performed using antibodies against mouse monoclonal antibody-anti-human IGF2BP2 from Cell Signaling Technology, and mouse monoclonal antibody-anti-human β-actin (sc-47778, Santa Cruz Biotechnology), follow ed by incubation with horseradish peroxidase (HRP)-coupled mouse secondary antibody (1:10,000, NA93, GE Healthcare,). To confirm equal protein loading, the blots were re-probed with a β-actin antibody, and analysis of the data was performed using NIH ImageJ software.