Data Collection and Processing
The systematic analysis of various types of tumors, both cancerous and normal human tissue, involved obtaining transcription profiling data from TCGA database and GTEx database. These databases were accessed through the UCSC Xena platform 21. For subsequent statistical analysis, the entire dataset was utilized after being transformed using log2(TPM + 1). Additionally, the gene expression patterns of LAP2α in various types of cancer were obtained from GEPIA database 22. The HPA obtained the protein expression patterns of LAP2α in various bodily tissue 23.
Prognostic Analysis
The Cox and Kaplan-Meier analysis were used to demonstrate the correlation between LAP2α expression and overall survival (OS), disease-free survival (DFS) and disease-specific survival (DSS). Among this, we also assessed the impact of LAP2α expression on the cancer patients’ survival with using the Kaplan-Meier database. And the “forestplot” and “survival” R package were used to calculate the log-rank P-value and hazard ratio (HR).
Genomic Alterations Analysis
The genomic alteration data of LAP2α, including alteration rate, mutated site, mutation count and type in cancer tissues, were available from cBioPortal tool 24, which is a widely used informative platform for researching comprehensive genome and epigenetics studies. The genomic alterations contain splice, deep or shallow deletion, missense and structural variant. Using Spearman’s method, we performed correlation analysis between LAP2α expression and tumor mutation burden (TMB), microsatellite instability (MSI), and mutant-allele tumor heterogeneity (MATH) utilizing data from the TCGA database.
Immune Infiltration Analysis
We acquired the LAP2α-associated immune cell infiltration degree of TCGA from TIMER database 25. Next, the individuals with various forms of tumors were segregated into two categories according to their median LAP2α levels of expression in order to examine the correlation between LAP2α expression and infiltration. Using the TIMER database, we assessed the infiltration of immune cells, such as natural killer cells, diverse kinds of T cells, macrophages, dendritic cells, and so on. Additionally, we analyzed the spearman correlation to create a heat map that illustrated the correlation coefficient between the expression of the LAP2α gene and immune checkpoint-associated genes in different tumor types.
Enrichment Analysis
To establish a network of protein-protein interactions (PPI), the STRING database was utilized for identifying LAP2α-related proteins. Next, we utilized the GEPIA2 online tool to investigate the 100 genes that showed the highest correlation with LAP2α expression in the TCGA datasets. Furthermore, the GEPIA2 tool was utilized to investigate the correlations between genes in pairs through the 'Correlation Analysis' module. To explore the underlying functions and pathways of LAP2α, we utilized the “GOplot” package and the “ClusterProfiler” R package. Furthermore, we have investigated the functional condition of LAP2α using the CancerSEA database 26, an accessible online platform that thoroughly examines the association between LAP2α and the diverse functional states in 25 different types of cancer at the cellular level.
Construction of Prognostic Risk Model
LASSO regression analyses were conducted using the R packages “survival” and “glmnet”. Additionally, we developed the risk evaluation model based on the respective coefficients and subsequently computed the risk score for every individual. According to the median risk coefficient value, the TCGA GBM cohort and CGGA cohort were categorized into high or low risk groups. And the R package Kaplan-Meier survival was utilized to carry out the regression analyses.
Tissue microarray and Immunohistochemical (IHC) staining
The tissue microarrays (TMAs) were firstly constructed by the 80 glioma tissues from patients, then incubated with anti-LAP2α antibodies (Huabio, China) for a night.
The following day, we added the relavant secondary antibodies and DAB solution into the microscope slide. Finally, we observed and photographed via a microscope. The LAP2α protein levels was determined as follows: (1) we chose five respective fields of view (FOVs) randomly and calculate positively stained cell scores as 0 (0–5%), 1 (6–25%), 2 (26–50%), 3 (51–75%), and 4 (> 75%). (2) Staining intensity score: the immunohistochemistry staining intensities in each sample were assessed below: negative (0 points), weak (1 point), intermediate (2 points), or high (3 points).
Cell Culture and siRNA Transfection
The cell lines were acquired from the Cell Bank of the Chinese Academy of Science and underwent authentication and testing to ensure absence of mycoplasma contamination. The DMEM was used to culture the U251 and U87 glioma cell lines. And the medium was enhanced with 10% FBS (Gibco, Grand Island, NY, USA). Tsingke (Wuhan, China) provided the siRNAs, and the sequences (siLAP2α-1, siLAP2α-2, siLAP2α-3) are listed below: siLAP2α-1: 5′- GUCUAGAAGUGGCUAAGCATT-3′; siLAP2α-2: 5′- GCUUUCUAGAUCACAUAUUTT-3′; siLAP2α-3: 5′- GCAGAAUGGAAGUAAUGAUTT-3′; The siRNAs were transfected into the two glioma cell lines via Lipofectamine 3000 reagent.
RNA Extraction and qRT‒PCR
The RNA extraction assay was conducted by the RNeasy mini kit (Qiagen). Afterwards, 1 µg RNAunderwent reverse transcription to cDNA. Next, qRT-PCR was conducted with guidelines provided by the PCR Mix manufacturer. The primer sequences were showed below: LAP2α: 5′-TGGGTGCGCACAACATTATGG-3′; 5′-CCTGAGGGCATGTATCAGGA-3′; GAPDH: 5′-GGAGCGAGTTCCCTCCAATTT-3′;5′-GGCTGTTGTCATACTTCTCATGG-3′.
MTT Assay
The 96-well plate was used to seed the cells, with a density of 1×103 cells per well, and they were cultured overnight. Following the specified duration of culturing, MTT was introduced into every well and allowed to incubate for 4 hours at a temperature. Next, the liquid above the sediment was removed, and 200 µl of DMSO was introduced into every well.
Cell Cycle and Apoptosis Assay
For cell cycle assay, the cells were treated with DNA Staining Solution and appropriate permeabilization solution. The apoptosis test was performed using the Annexin V FITC Apoptosis Assay Kit. In total, 106 cells were placed in 6-well dishes, subsequently gathered (including cells in the supernatant), and subjected to a 5-minute treatment with 5 µl of Annexin V-APC and 10 µl of 7-AAD. Shortly after, the specimen was immediately identified using a flow cytometer.
Wound-healing Assay
The U251 and U87 glioma cells (density 2.5 ×105 cells/well) transfected with siRNAs were inoculated in the 6-well plate for 24 h. After that, we used a 200 µL pipetting head to create a scratch on the plate. The serum-free medium was then replaced, and images were captured at 0 hours and 48 hours using an inverted microscope (XDS-100, Cai Kang Optical Instrument Co, Ltd, China).
Statistical Analysis
All the results were obtained from more than three independent experiments. Survival analysis was conducted using the Kaplan–Meier estimation technique, and the count was determined using the log-rank test. To analyze differences between groups using a two-tailed t test, we utilized statistical software such as GraphPad Prism 7 (USA) and R software. The statistical significance was presented in the following manner: ns indicates no statistical significance, *p < 0.05, **p < 0.01, and ***p < 0.001.