Pan‑cancer Analysis of LAP2α as a Potential Prognostic and Immunological Biomarker for Multiple Cancer Types Including Glioma

Pan‑cancer Analysis of LAP2α as a Potential Prognostic and Immunological Biomarker for Multiple Cancer Types Including Glioma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pan‑cancer Analysis of LAP2α as a Potential Prognostic and Immunological Biomarker for Multiple Cancer Types Including Glioma Danwen Wang, Donghu Yu, Yongze He, Feng Tang, Zhiyong Pan, Zefen Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3806677/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract LAP2α, a variant of the lamin-associated polypeptide 2 (LAP2) family, is crucial for in the process of nuclear structure organization and maintaining genomic stability. This research was aimed to investigate the involvement of LAP2α in cancer development, especially in glioma. Based on public datasets, we found LAP2α was upregulated in most cancers, and the survival analysis indicated a distinct correlation between elevated LAP2α expression and unfavorable prognosis among glioma patients with elevated levels of macrophage and neutrophil. LAP2α level evidently associated with the immune checkpoint therapy related genes in cancers. Specifically, we made the tissue microarray covered 80 glioma patients with prognostic analysis, and verified that reducing LAP2α hindered the growth and movement capacity of the glioma cells. Our data suggests that LAP2α may be an important tool for indication of immunotherapy and medical prognosis in pan-cancer, and is expected to have a critical role in the oncogenesis of glioma. LAP2α glioma immune infiltration pan-cancer analysis prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Nowadays, cancer has shown a terrifying morbidity and mortality rate, and it has added enormous living and financial burden to the public 1,2 . Despite notable advancements in the field of cancer prevention and management, such as the utilization of targeted molecular therapy, radiotherapy, and immunological therapy, the clinical outcomes remain unsatisfactory for individuals with tumor recurrence or metastasis 3 . Hence, it is crucial to investigate alternative diagnostic indicators and potential treatment choices to enhance the efficacy of therapies in individuals suffering from cancer. With techniques developing, more and more researchers attempt to use multiple public resources to perform bioinformatic analysis for identifying the critical genes in tumorigenesis 4 . Currently, pan-cancer research has been widely applied to analyzing the common characteristics of different kinds of tumors, in order to investigate deeply the mechanism of tumor development 5,6 . Six distinct isoforms of Lamin-associated polypeptide 2 (LAP2), namely LAP2-α, β, γ, δ, ε, and ζ, were identified through alternative splicing, resulting in the production of proteins with varying molecular weights 7 . As the major spliced isoform of the LAP2 gene (TMPO), lamin-associated polypeptide 2 alpha (LAP2α) was found to bind to intranuclear and peripheral A-type lamins, which might play a part in construction of nucleoskeleton and chromosomal scaffold 8–10 . In particular, the LAP2α isoform directly anchored to chromosome through a unique coiled-coil domain in the C-terminal region, which localized throughout the nucleus transmembrane domain and bind to retinoblastoma protein (pRb) 11 . Previous studies have shown that the kind of distinct C-terminal region binding with A-type lamins determined a definition of lamin A/C position in the nucleoplasm, which might influence postmitotic nuclear assembly with association with chromosomes 12 . Recent studies suggested that LAP2α was involved in the complicated regulatory network modulating pRb hypophosphorylated in the nucleus 13 , specifically promoting pRb-mediated repression of target genes, and accelerating progenitor cell cycle exit and initiation of differentiation in proliferative tissues 14,15 . Furthermore, LAP2α was found necessary to knitted pRB and the nuclear skeleton together, for preventing proteasomal degradation 16 . In addition to these structural roles in nuclear, LAP2α was found to combine with the heat shock protein 70, indicating that LAP2α had a critical role in resistance against stress 17 . Based on these findings, researchers ratiocinated that the level of LAP2α could regulate cell cycle progression, suggesting its potential role in cancer growth. In line with this, recent research has discovered an overexpression of LAP2α in tumors, including lung cancer 18 , medulloblastoma 19 and cervical cancer 20 . Nowadays, the underlying functions and mechanisms of LAP2α in various types of cancer are still not well understood and require additional investigation. This study involved a comprehensive analysis of the expression patterns of LAP2α and its correlation with patient prognosis, TMB, MSI, and immune checkpoint genes across various types of tumors. Furthermore, we conducted an analysis of protein interaction networks and enrichment pathways, which are implicated in the regulation of tumor progression by LAP2α. Furthermore, due to the observation that LAP2α survival analysis showed a clear tendency in glioma, we proceeded to construct a prognostic risk-score model specifically associated with LAP2α for glioma patients. Subsequently, we verified the model's accuracy using a separate dataset. To confirm the tumorigenic factors of LAP2α, we carried out additional in vitro experiments. The collective findings demonstrated that the research of LAP2α could provide novel orientations and strategies for the cancer clinical management. 2. Materials and Methods 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, 10 6 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. 3. Results 3.1 LAP2α Expression Profiles in Human Normal Tissues and Cancers Firstly, we investigated the mRNA expression levels of LAP2α in different organs based on the GTEx and TCGA databases. According to Fig. 1 A, LAP2α expression were highest in thymus, followed by bone marrow and lymph node. In order to identify the distribution of LAP2α expression in the cells of blood, we analyzed through HPA/Monaco/Schmiedel datasets to find that LAP2α expression had an evident enrichment in non-classical monocyte and intermediate monocyte ( Fig. 1 B ) . Meanwhile, we detected the LAP2α mRNA expression in TCGA dataset, which included 32 types of tumors. The results suggested that LAP2α was differentially expressed in 27 of 32 cancer types. Specifically speaking, the expression level of LAP2α was lower in kidney renal papillary cell carcinoma (KIRP), kidney renal clear cell carcinoma (KIRC) and thyroid carcinoma (THCA) than that in para-cancer tissue(P < 0.001), while the expression level of LAP2α was higher in the rest of cancers when compared to normal tissues ( Fig. 1 C ) . Then, we found LAP2α was significantly upregulated in adrenocortical carcinoma (ACC), cholangiocarcinoma (CHOL), glioblastoma multiforme (GBM), brain lower grade glioma (LGG), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC), lung squamous cell carcinoma (LUSC) and esophageal carcinoma (ESCA) via combining the TCGA and GTEx database ( Fig. 1 D ) . Furthermore, we demonstrated the positive association between LAP2α and the advanced pathological stages of the cancers, including lung adenocarcinoma (LUAD), COAD, KIRP, thymoma (THYM), liver hepatocellular carcinoma (LIHC), ovarian serous cystadenocarcinoma (OV) and ACC ( Fig. 1 E ) . 3.2 Correlation Between LAP2α Expression and Prognosis in Various Tumors We conducted univariate Cox survival analysis using data from the TCGA database to investigate the correlation between LAP2α expression level and its clinical significance, specifically focusing on overall survival (OS) and disease-specific survival (DSS). As Fig. 2 A showing, the forest plots showed that LAP2α expression was significantly correlated with poor OS in GBM/LGG (HR = 2.15, P < 0.001), LGG (HR = 1.96, P < 0.001), LUAD (HR = 1.34, P < 0.001), KICH (HR = 6.62, P < 0.001) and mesothelioma (MESO, HR = 1.48, P = 0.01) patients. The Cox regression analysis of DSS suggested that the high LAP2α expression was related to worse DSS in GBM/LGG (HR = 2.14, P < 0.001), LGG (HR = 1.92, P < 0.001), KIRP (HR = 2.08, P < 0.001), KICH (HR = 12.98, P < 0.001), LUAD (HR = 1.39, P < 0.001) and MESO (HR = 1.87, P < 0.001) patients. Subsequently, we also performed the Kaplan–Meier survival analysis to acquire an explicit correlation between LAP2α expression and the outcome of patients with various types of cancer. The cancer survival rates indicated that individuals with elevated LAP2α levels typically have worst OS and DFS in MESO, GBM, LIHC, SARC, ACC and UVM. This suggests that LAP2α could serve as an important prognostic biomarker across various types of cancer (Fig. 2 B). Moreover, we compared the prognostic value of LAP2α expression in different cancers via GEPIA2 database. The analytical result showed that high LAP2α mRNA expression levels were strongly correlated with grave prognosis in OS and DSS of GBM/LGG, MESO and KIRP patients (P values < 0.05, Fig. 2 C). The results above illustrated that patient with high levels of LAP2α had a poorer OS and DSS than those with low expression of LAP2α in MESO, KIRP, especially in GBM/LGG. 3.3 The Genetic Alteration Landscape of LAP2α in Different Tumors Next, we explored the LAP2α alteration status via the cBioPortal online site, which contained all the TCGA cohorts with 32 objects and 10,967 samples. The most common change observed in LAP2α, as shown in Fig. 3 A, was the occurrence of “mutation” in cases of endometrial cancer. Meanwhile, the “amplification” was most predominant mutation types in pleural mesothelioma and prostate cancer. Moreover, we found 158 mutation sites including 123 missenses, 29 truncating, two splices, two fusion and two inframe between amino acids 0 and 694, and the specific mutation sites, case types and numbers were showed in Fig. 3 B. We additionally examined the number of mutations in LAP2α using the information from TCGA databases. Among the 32 cancers, shallow deletion and gain were the most common in terms of LAP2α mRNA expression in pan-cancer ( Fig. 3 C ) . Additionally, we performed the correlation analysis about genomic-instability of LAP2α and progression of various tumors. According to Fig. 3 D, LAP2α expression showed a positive correlation with TMB in LUAD, KICH, ACC, STAD, COAD, GBM, LGG, and BLCA, while negatively in THYM and THCA. In READ, ACC, SARC, STAD, GBM, STES, MESO, and COAD, the expression of LAP2α showed a positive correlation with MSI. However, in DLBC, GBM, PRAD, and HNSC, it exhibited a negative correlation. Besides, LAP2α also positively correlated with MATH in ESCA, MESO, UCS, BLCA, BRCA, TGCT, LAML KICH and ACC, and negatively in DLBC, GBM/LGG, THYM and KIRC. Next, we investigated the correlation between LAP2α levels and particular genomic features, including somatic mutations and copy number variations (CNVs), in the TCGA dataset of glioma. The results indicated the top 15 high-frequency mutated genes, of which TP53 was obviously dominating (37.5%), followed by PTEN (34.4%) and EGFR (32.8%) in gliomas ( Fig. 3 E ) . The findings suggest that alteration in the LAP2α gene could have significant implications in the advancement of tumors, particularly in glioma. 3.4 LAP2α-related Immune Infiltration in Pan-Cancer In view of speculation that LAP2α expression closely associated with alteration-related factors, which could modify the response to immune therapy of tumor, we conducted the correlation analyses between LAP2α level and immune infiltration. The heatmap in Fig. 4 A demonstrated a positive correlation between LAP2α and the infiltration levels of CD4 + T cells, B cells, and M1-like macrophages. Conversely, LAP2α showed a negative correlation with the infiltration levels of regulatory T cells (Tregs) across various cancers, including GBM/LGG. To further identify the effects of disparate levels of LAP2α and immune cells (macrophages and neutrophil) on the survival time of GBM and LGG patients, we performed Kaplan-Meier analysis based on TCGA dataset. The findings indicated that GBM individuals with elevated LAP2α expression and macrophage presence had a poorer overall survival compared to patients with lower macrophage levels (P values < 0.05). The macrophage level did not have the ability to impact the OS of GBM patients, regardless of whether they had high or low expression of LAP2α. Similarly, the groups combined high LAP2α expression and low macrophage level showed better OS in LGG patients. Notably, LGG individuals who exhibited both diminished LAP2α expression and neutrophil level demonstrated superior OS compared to those with elevated neutrophil level, whereas individuals with both elevated LAP2α expression and neutrophil level exhibited the poorest prognosis (P values < 0.05, Fig. 4 B). Given the correlation between elevated LAP2α levels and poorer OS in GBM/LGG, the combination of high LAP2α expression and increased macrophages in a group resulted in a worse overall survival outcome. To ascertain the possible functions of LAP2α in immunotherapy, we examined the association between the expression of LAP2α and PD-1 as well as PD-L1. In nearly all types of cancer, the findings indicated a positive correlation between the expression of LAP2α and PD-1 as well as PD-L1 (Fig. 4 C). Considering that most immune checkpoint-related genes have participated in crucial mechanisms of tumor immune evasion, we further investigated the correlation between LAP2α and immune checkpoint genes using TCGA databases. As Fig. 4 D displaying, LAP2α was strongly linked (P value < 0.05) to inhibitory functions of several immune genes, such as CD276, VEGFA, CD274, IL10, and EDNRB, and activation of HMGB1, ICOS, CD28, ICAM1, CD40 and TNF in diverse cancer types. The profiles indicated that LAP2α, to a certain degree, was involved in the pathways related to immune infiltration and could play a crucial role in tumor immunotherapy. 3.5 Enrichment Analysis of LAP2α-Related Partners To dig into the mode of action in the biological processes of cancer cells, we examined the networks of proteins that bind to LAP2α using the STRING online database. The PPI network identified five LAP2α-binding proteins, namely, lamin B1 (LMNB1), lamin A (LMNA), VRK serine/threonine kinase 1 (VRK1), VRK serine/threonine kinase 2 (VRK2) and BAF nuclear assembly factor 1 (BANF1). Based on the GEPIA2 tools, we conducted the correlation analysis and found that BANF1 (R = 0.29), LMNA (R = 0.2) and VRK2 (R = 0.21) were all obviously associated with the expression of LAP2α (P-values < 0.001, Fig. 5 A). Then we evaluated association between the LAP2α and involved enrichment pathway from TCGA. According to the findings presented in Fig. 5 B, it was indicated that LAP2α exhibited a positive correlation with angiogenesis, cellular reaction to hypoxia, apoptosis, and tumor advancement. Conversely, LAP2α showed a negative association with arginine and proline metabolism, arginine biosynthesis, alanine aspartate and glutamate metabolism, as well as primary bile acid biosynthesis. Furthermore, we studied the functional state of LAP2α in various cancer cell types by using CancerSEA database, which offered informative data on the association between LAP2α and 14 functional states of cancer at the level of individual cells. The diagram illustrated that LAP2α expression positively correlated with cell cycle, DNA damage and repair. Meanwhile, the negative association was observed in process of hypoxia and inflammation in most of the tumors. Intriguingly, the LAP2α expression was significantly associated with cell cycle, DNA damage, DNA repair, epithelial to mesenchymal transition (EMT), invasion, metastasis and proliferation in glioma, which means that LAP2α has the potential to be a biomarker and play a role in the advancement of glioma ( Fig. 5 C ) . For further exploring the biological functions of LAP2α in glioma, we conducted the GO analysis of LAP2α-related genes. The figure showed the enrichment processes of five interacted genes, including nuclear envelope reassembly, nuclear envelope organization and endomembrane system ( Fig. 5 D ) . 3.6 Construction of Prognostic Risk-Score Model in GBM The main clinical characteristics of LAP2α in CGGA and TCGA were shown in Table 1 – 2 . A total of 422 and 665 cases were analyzed in this study, among them, the expression of LAP2α had a high correlation with histology, grade and IDH mutation status in GBM through CGGA and TCGA database. Based on the above discoveries, next we were aimed to evaluate the predictive significance of LAP2α in glioma. by means of constructing a LAP2α-associated prognostic signature model in glioma. Firstly, we conducted the Lasso Cox regression algorithm by integrating the genes from LAP2α-related PPI network ( Fig. 6 A-B ) . Subsequently, we established a risk model with LMNB1, LMNA, VRK2 and BANF1. We obtained the regression coefficients for these genes and used them to calculate the risk score with the formula: RiskScore = 0.3117 ∗ LMNB1 + 0.5928 ∗ LMNA + 0.334 ∗ VRK2 + 0.2234 ∗ BANF1. To better verify the availability of the model, the CGGA database and TCGA database were used respectively as training set and validation set. Additionally, we determined the distribution of risk score, patient outcome, and expression profiles of the included four genes in both the training and validation datasets, as illustrated in Fig. 6 C-D. Furthermore, we generated Kaplan-Meier survival plots in both internal and external datasets. Then we categorized the glioma patients into groups of low-risk and high‐risk based on the median risk score. According to the OS Kaplan‐Meier curve, patients in the high-risk category implied an obviously reduced likelihood of survival compared to those in the low-risk category ( Fig. 6 E-F ) . These findings indicated that the prognostic signature associated with LAP2α could offer a relatively precise prognosis prediction for patients with GBM. Table 1 Association between LAP2α expression and clinicopathologic features in GBM samples from the CGGA database. Characteristics High(N = 211) Low(N = 211) Total(N = 422) P value Histology 1.60E-04 Astrocyte 71(18.25%) 95(24.42%) 166(42.67%) GBM 90(23.14%) 45(11.57%) 135(34.70%) Oligodendroglioma 50(12.85%) 38(9.77%) 88(22.62%) Grade 2.40E-07 II 46(10.90%) 92(21.80%) 138(32.70%) III 72(17.06%) 72(17.06%) 144(34.12%) IV 93(22.04%) 47(11.14%) 140(33.18%) IDH 2.30E-05 Mutant 74(19.32%) 134(34.99%) 208(54.31%) Wildtype 101(26.37%) 74(19.32%) 175(45.69%) MGMT 0.86 Methylated 115(33.53%) 82(23.91%) 197(57.43%) Un-methylated 83(24.20%) 63(18.37%) 146(42.57%) Table 2 Association between LAP2α expression and clinicopathologic features in GBM samples from the TCGA database. Characteristics High(N = 333) Low(N = 332) Total(N = 665) P value Histology 1.40E-45 Astrocyte 70(10.77%) 185(28.46%) 255(39.23%) GBM 199(30.62%) 28(4.31%) 227(34.92%) Oligodendroglioma 51(7.85%) 117(18.00%) 168(25.85%) Grade 1.20E-43 II 47(7.64%) 178(28.94%) 225(36.59%) III 122(19.84%) 120(19.51%) 242(39.35%) IV 141(22.93%) 7(1.14%) 148(24.07%) IDH 3.40E-46 Mutant 123(18.84%) 299(45.79%) 422(64.62%) Wildtype 203(31.09%) 28(4.29%) 231(35.38%) MGMT 4.90E-13 Methylated 188(29.75%) 287(45.41%) 475(75.16%) Un-methylated 115(18.20%) 42(6.65%) 157(24.84%) 3.7 Immunohistochemical Staining of LAP2α in Human Protein Atlas Database and GBM tissue microarray As shown in Supplementary Fig. 1 , LAP2α presented weak staining in normal breast, glioma, lung, melanoma, colon, liver, stomach and urothelial tissues, while had moderate or strong staining in corresponding tumor tissues. To conclude, at the protein level, LAP2α exhibited a higher protein expression in these tumor tissues than the corresponding paracancerous tissues, suggesting that LAP2α could contribute to the development of different cancers, also provide some insights of novel therapeutic targets. We further determined the association between LAP2α level and clinical outcomes in GBM patients by LAP2α microarray contained 80 samples of GBM tissues ( Fig. 7 A ) . And the survival curves showed that the low expression of LAP2α in GBM patients indicating a better OS and FPS, suggesting that LAP2α could be a valuable indicator in patients with GBM ( Fig. 7 B-C ) . Additionally, the expression of LAP2α was positively associated with MGMT status of GBM patients ( Table 3 ) . Table 3 Association between LAP2α expression and clinicopathologic features in GBM samples. Characteristics High(N = 48) Low(N = 32) P value Sex 0.61 Female 22(27.50%) 12(15.00%) Male 26(32.50%) 20(25.00%) Age 56.63 ± 10.54 53.63 ± 13.09 0.2614 Ki-67 32.56 ± 15.02 31.14 ± 20.51 1.20E-43 IDH 1 Mutant 3(4.55%) 2(3.03%) Wildtype 37(56.06%) 24(36.36%) MGMT 0.03 Methylated 26(36.11%) 7(9.72%) Un-methylated 20(27.78%) 19(26.39%) TERT 0.8 Mutant 19(29.23%) 11(16.92%) Wildtype 20(30.77%) 15(23.08%) 3.8 The Effects of LAP2α Knockdown on Proliferation, Migration, Cell Cycle and Apoptosis of GBM Cells In order to confirm the molecular roles of LAP2α in glioma cells, we silenced the LAP2α expression in the U251 and U87 cell lines, individually ( Fig. 8 A ) . Subsequently, the MTT assay findings revealed that the suppression of LAP2α greatly impeded the capacity of cell proliferation in both GBM cell lines ( Fig. 8 B ) . Furthermore, the wound healing experiment demonstrated that the suppression of LAP2α could decelerate the migration speed of GBM cells ( Fig. 8 C ) . Meanwhile, we conducted apoptosis and cell cycle assays to confirm that the decrease in LAP2α caused apoptosis and cell cycle arrest in GBM cells during the G2/M phase ( Fig. 8 D-E ) . 4. Discussion Lamina-associated polypeptide 2 (LAP2), also known as thymopoietin (TMPO), can generate six splice isoforms (α, β, γ, δ, ε, ζ) by means of alternative splicing. According to position of transmembrane domain, these isoforms were reported to be divided into two parts: localization in inner nuclear membrane and in nucleoplasm through unique C-terminus, which can bind to pRb protein 10,27 . Nevertheless, LAP2α have been considered as the latter category, for the unique C-terminus of LAP2α lacking a transmembrane domain, which is replaced by a massive four-stranded antiparallel coiled coil dimer 28 , specifically binding to A-type lamins in nucleoplasm 29 . Recent observations indicated that LAP2α’s C-terminus could play roles in the process of cell cycle and mediate the tumor suppressor via interacting with pRb 30 . Previous studies also reported that LAP2α was overexpressed in proliferating cells and diverse human tumor samples with variational transcript and protein levels 31,32 . It is still uncertain from these studies whether it specifically controls the onset and progression of tumors. Hence, the potential roles of LAP2α in initiation and progression of different kinds of tumors are worthy of determination. This study investigated the oncogenic function and prognostic significance of LAP2α in various types of tumors, including glioma. Specifically, we discovered that LAP2α expression was elevated in different types of cancers compared to their corresponding noncancerous tissues, suggesting tumorigenic potential of LAP2α and broad foreground in the aspects of cancer research. Consistent with this view, a previous investigation showed the overexpression of LAP2α in cervical carcinoma, and further revealed the critical regulation mechanism of LAP2α 20 . Our research discovered a correlation between the presence of LAP2α in different types of cancers and the tumor stage, which may provide vital implications for diagnosis of patients in different stages. The COX regression and Kaplan-Meier analyses suggested that upregulation of LAP2α had high risks of worse OS and DSS in GBM/LGG, MESO, KIRP, KICH, LUAD and ACC. In the TCGA dataset, a strong correlation was observed between elevated LAP2α levels and unfavorable overall survival (OS) and disease-free survival (DFS) in GBM, MESO, LIHC, SARC, ACC, and UVM. Obviously, we can hypothesize that LAP2α is able to serve as a prognostic indicator of GBM patients. As a kind of somatic mutation, TMB is an available predictive biomarker with the potential to lead to flourishing development of tumor precision medicine 33 . Previous research revealed that most individuals with non-small-cell lung cancer and a high burden of somatic nonsynonymous mutations experienced improved objective response rate (ORR) and progression-free survival (PFS) following treatment with pembrolizumab 34 . Therefore, the level of TMB was seen as genomic biomarker that can forecast the response to immune checkpoint inhibitors (ICIs). The loss activity of DNA mismatch repair typically leads to the generation of MSI, which is also acknowledged as a beneficial biomarker for ICIs. It has been shown that the patients with high frequency of MSI were generally accompanied by hypersensibility to immunotherapy and chemotherapeutics in colorectal tumors 35 . MATH is deemed to serve as a clinically practical indicator to identify the correlations between intratumor genetic heterogeneity and outcome of patients. A study found that head and neck squamous cell carcinoma (HNSCC) tumors with a higher MATH score might have more various populations of cells lead to metastasis and low target therapeutic efficiency than their less MATH group 36 . Our study found increase LAP2α expression significantly correlated with TMB, MSI and MATH in GBM/LGG, LUAD, KICH, STES, STAD, and BRCA, with the fact that genome stability strongly associates with progression of cancer, and we further affirm the oncogenic function of LAP2α and prediction ability of the patient's immunotherapy efficacy. Furthermore, we examined that LAP2α played a significant role in the immune response against tumors. According to public databases, our analysis revealed a clear positive correlation between the expression of the LAP2α gene and CD4 + T cells, B cells, and M1-like macrophages in GBM/LGG. As is known to us all, the discovering of immune biomarkers grows in importance in this era of stratified medicine. Additionally, we conducted Kaplan-Meier analysis to find the relationship between LAP2α levels, infiltrated immune cells and clinical outcomes. Several research studies have shown that tumor-associated macrophages (TAMs) and tumor-infiltrating neutrophils (TINs) significantly impact the prognosis and effectiveness of cancer treatments, such as chemotherapy and immunotherapy 37,38 . In this study, it was observed that GBM/LGG patients with both elevated LAP2α expression and increased macrophage levels had a significantly poorer overall survival compared to those with high LAP2α expression and low macrophage levels. The negative correlation between tumor-associated macrophages and clinical prognosis is widely recognized. Based on the findings in Fig. 4 A, there is already evidence of a direct link between LAP2α and macrophages. Additionally, a strong association exists between high LAP2α expression and poorer overall survival in GBM/LGG. Consequently, it is apparent that the level of LAP2α expression and immune infiltration can potentially serve as accurate predictors for the prognosis of GBM/LGG patients. Moreover, immune checkpoints, as a kind of molecules able to regulate functions of T cells, can directly affect the inhibition and stimulation pathways exerted by immune cells 39 . The immune checkpoint signal represented the pathway to immune escape, resulting in tumor aggressiveness. Currently, there is a growing emphasis on investigating the efficacy of ICI, a type of monoclonal antibody that attaches to PD-1 or PD-L1, resulting in enhanced immunogenicity of the tumors 40 . Nevertheless, most tumor patients receiving ICI targeting PD-L1 therapy do not improve their survival time, probably on account of insufficient immune activation to identify tumor-associated antigen 41 . As a result, it is extremely urgent to discover novel checkpoint inhibitors and targeted molecules to enhance the effectiveness of current treatments for immune checkpoint modulation 42 . This study involved analyzing the correlation between LAP2α and immune checkpoint genes across cancers. In diverse types of cancer, the expression of LAP2α was found to be associated with the suppression of various immune checkpoint genes, including CD276, VEGFA, CD274, IL10, and EDNRB, and activation of HMGB1, ICOS, CD28, ICAM1, CD40 and TNF in diverse cancer types. Considering the positive correlation between LAP2α expression and two immune checkpoint genes, CD276 and VEGFA, especially in GBM/LGG, and previous studies proved that CD276 was involved in T-cell receptor signaling pathway and indicated a worse prognosis for glioma patients 43 , our research can provide helpful thoughts for exploration, validation and development of immunotherapies in glioma. As a vital element in cell regulatory network in biological processes, deregulated cell signaling has generally been found in a number of human diseases, including cancer. Functional enrichment analysis of LAP2α was performed in this study using the TCGA dataset. The findings indicated a strong association between LAP2α and processes angiogenesis, cellular response to hypoxia, apoptosis, tumor progression, and diverse amino acid metabolism. Moreover, our analysis of the CancerSEA database revealed a significant association between LAP2α and various processes in glioma, such as cell cycle regulation, DNA damage response, DNA repair mechanisms, and cellular proliferation. In keeping with our analysis, previous study reported that LAP2α played an important role in chromatin reorganization during cell apoptosis 44 . A recent investigation revealed that LAP2α could promote the combination between replication protein and ssDNA produced by damaged chromatin, consequently protect the replication forks and initiate repair of damaged DNA 45 . Another study suggested that DNA damage-related chromosomal instability induced by LAP2α could lead to metastasis of breast cancer 46 . However, it has not yet research about the oncogenetic mechanism of LAP2α in glioma, thus the roles of LAP2α-related regulation mechanisms in tumorigenesis of glioma need to be further validated. As the most common intracranial primary malignancy in adults, GBM brings great disservice and loss to society. Despite present standard treatment, the median patient survival is still less than 14 months 47 . Therefore, there is urgent need to explore more available prognostic indicators as well as credible prognostic models of GBM. The Lasso Cox regression analysis indicated that the risk signature in GBM could enhance sensitivity and precision of prognosis, so as to better provide guidance for anti-tumor therapy. 5. Conclusions Combined with the above results, our pan-cancer analysis revealed distinct expression patterns of LAP2α in tumor and neighboring healthy tissue. Furthermore, LAP2α exhibited a significant correlation with the clinical outcome in the majority of malignancies, suggesting that LAP2α is able to become a promising indicator for the diagnosis and survival of individuals with cancer. Additionally, our research uncovered the infiltration of immune cells that influenced by LAP2α in various types of cancers, indicating its potential as a valuable tool for personalized immunotherapy in the future. Furthermore, the suppression of LAP2α hindered the malignant advancement of GBM cells, leading to decreased cell growth, arrest of the cell cycle in the G2/M stage, heightened rate of cell death, and impeded cell migration. In conclusion, the comprehensive analysis of various types of cancer revealed the significance of LAP2α, particularly in glioma, and also provided novel insights into therapeutic strategy for tumor patients. Declarations Data availability statement The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors. Author contributions D.W. conceived the idea, analyzed the data, and drafted the work. D.W. and D.Y. analyzed the data and performed the visualization. D.Y., F.T., Z.P., and Z.W. collected the data and participated in the revision. C.M. and Z.L. supervised the study and provided funding support. All authors contributed to the article and approved the submitted version. Funding This research was supported by the project of Hubei province and the Translational Medicine Research Fund of Zhongnan Hospital of Wuhan University (YYXKNL2023011, ZNJC202206). Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., and Bray, F. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians 71 , 209–249. https://doi.org/10.3322/caac.21660 . Brinklov, S., Kalko, E.K.V., and Surlykke, A. (2009). Intense echolocation calls from two 'whispering' bats, Artibeus jamaicensis and Macrophyllum macrophyllum ( Phyllostomidae ). J. Exp. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3806677","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":263977024,"identity":"87b4f651-228e-486e-b4d1-24abf5da4f6b","order_by":0,"name":"Danwen Wang","email":"","orcid":"","institution":"Zhongnan Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Danwen","middleName":"","lastName":"Wang","suffix":""},{"id":263977026,"identity":"3ca7e7e4-9371-47d2-99ee-d02822e8cc0f","order_by":1,"name":"Donghu Yu","email":"","orcid":"","institution":"Zhongnan Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Donghu","middleName":"","lastName":"Yu","suffix":""},{"id":263977028,"identity":"f326bf63-9373-4ee8-809f-6104b0f71833","order_by":2,"name":"Yongze He","email":"","orcid":"","institution":"Zhongnan Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Yongze","middleName":"","lastName":"He","suffix":""},{"id":263977030,"identity":"9335eef5-1933-4112-8ce5-94222396c09e","order_by":3,"name":"Feng Tang","email":"","orcid":"","institution":"Zhongnan Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Tang","suffix":""},{"id":263977032,"identity":"5008ae8b-a4e4-4cf9-8a6d-8b83aca9128e","order_by":4,"name":"Zhiyong Pan","email":"","orcid":"","institution":"Zhongnan Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Zhiyong","middleName":"","lastName":"Pan","suffix":""},{"id":263977034,"identity":"408e6d31-d506-4b8c-b1fd-a2cbea0f048d","order_by":5,"name":"Zefen Wang","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Zefen","middleName":"","lastName":"Wang","suffix":""},{"id":263977035,"identity":"146b5300-647c-4dd1-ae04-ffd077a6ef2f","order_by":6,"name":"Chao Ma","email":"","orcid":"","institution":"Zhongnan Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Ma","suffix":""},{"id":263977036,"identity":"48a2f0a5-1ba2-4c7c-a02b-6a950dae2dfe","order_by":7,"name":"Zhiqiang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACPmYGNoYEBgkgk/kAM1joAAEtbAgtbAlEagEjMOAxIFILO/uzBw/KLOTN+dd8ky5sY5Dju5HA+LkAv8PSDRLOSRjunPF2m/TMNgZjyRsJzNIz8Gs5JpHYJsG44cbZbbd52xgSN9xIYGPmwauFsQ2kxX7DjTPPQFrqidDCzAbSkrjhfA8bSEuCAWEtbGwSQL8kb7jBZv6bB+ipmWceNkvj08LPf/yZ5I+yOtsN5w8/NuYps5HnO5588DM+LVC7gFgiAcQCxSljA0ENEC38B4hQOApGwSgYBSMSAABcL0UW/ti9AgAAAABJRU5ErkJggg==","orcid":"","institution":"Zhongnan Hospital of Wuhan University","correspondingAuthor":true,"prefix":"","firstName":"Zhiqiang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2023-12-26 06:59:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3806677/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3806677/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49082558,"identity":"7edba53d-410b-41ee-b8f0-2c3f92b9cfe8","added_by":"auto","created_at":"2024-01-02 20:13:39","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2871599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLAP2α Expression Profiles in Human Normal Tissues and Cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e LAP2α expression levels in normal tissues and cell types.\u003cstrong\u003e (B) \u003c/strong\u003eLAP2α expression levels in blood cell types.\u003cstrong\u003e (C)\u003c/strong\u003eLAP2α mRNA expression in TCGA dataset including 32 types of tumors. \u003cstrong\u003e(D)\u003c/strong\u003eDifferences of LAP2α expression between cancers from the TCGA database and normal samples from the GTEx database. \u003cstrong\u003e(E)\u003c/strong\u003e LAP2αexpression levels were assessed by the main pathological stages of LUAD, COAD, KIRP, THYM, LIHC, OV and ACC. The log2(TPM + 1) for log-scale was used. (*P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3806677/v1/dba645ff0e847cd1c0789353.jpg"},{"id":49082562,"identity":"5db17f91-a9dc-40dc-b17b-0e3034d999ae","added_by":"auto","created_at":"2024-01-02 20:13:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4925658,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Between LAP2α Expression and Prognosis in Various Tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Correlations of LAP2α expression with OS and DSS of patients using the Cox regression survival analysis.\u003cstrong\u003e (B) \u003c/strong\u003eK-M survival curves showed that LAP2α expression was strongly associated with clinical outcomes in different cancers.\u003cstrong\u003e (C) \u003c/strong\u003eThe survival maps and survival curves were depicted to perform OS and DFS analyses in cancers.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3806677/v1/ec5ca6b045c6886032646974.jpg"},{"id":49082561,"identity":"b6ef7072-3612-4e32-b972-e269fafa1fff","added_by":"auto","created_at":"2024-01-02 20:13:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2958105,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Genetic Alteration Landscape of LAP2α in Different Tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eGenetic alteration features (Mutation, Structural Variant, Amplification and Deep Deletion) of LAP2α in 32 different tumors were analyzed in TCGA database by the cBioPortal tool.\u003cstrong\u003e (B)\u003c/strong\u003e The mutation sites of LAP2α in multiple tumors by the cBioPortal tool.\u003cstrong\u003e (C) \u003c/strong\u003eMutation counts and types of LAP2α in 32 cancers.\u003cstrong\u003e (D) \u003c/strong\u003eThe correlation between the expression of LAP2α and TMB, MSI and MATH based on TCGA dataset. \u003cstrong\u003e(E) \u003c/strong\u003eDetection of differential somatic mutations including the top 15 high-frequency mutated genes in gliomas.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3806677/v1/86eab4488334b5e26457528e.jpg"},{"id":49082565,"identity":"919b597d-1528-4e5c-8a1c-1569502acca4","added_by":"auto","created_at":"2024-01-02 20:13:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3991270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLAP2α-related Immune Infiltration in Pan-Cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eRelationship between LAP2α expression and immune cell infiltration in different cancers. \u003cstrong\u003e(B) \u003c/strong\u003eThe effect of different levels of LAP2α and immune cells (macrophages and neutrophil) on the prognosisbased on TCGA dataset. \u003cstrong\u003e(C) \u003c/strong\u003eThe positive association between LAP2α expression and PD-1/PD-L1 infiltration in pan-cancer.\u003cstrong\u003e (D) \u003c/strong\u003eHeatmap illustrating the relationship between LAP2α and checkpoint gene levels. *p \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3806677/v1/696024420be710db31c633f6.jpg"},{"id":49082559,"identity":"3af1aa6f-6521-4c14-94bc-6f185ea52f8e","added_by":"auto","created_at":"2024-01-02 20:13:39","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2769793,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment Analysis of LAP2α-Related Partners\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eThe LAP2α-binding protein analysis constructed via STRING database, and the correlation between LAP2α and selected targeting genes, including LMNB1, LMNA, VRK1, VRK2 and BANF1 (https://string-db.org/, http://gepia.cancer-pku.cn/index.html ).\u003cstrong\u003e (B) \u003c/strong\u003eCorrelation between LAP2α expression and enrichment pathway via the GEPIA2 website. The P-value and partial correlation (R) were generated via the purity-adjusted Spearman’s rank correlation test.\u003cstrong\u003e (C) \u003c/strong\u003eThe interactive bubble chart present correlation of LAP2αwith functional state in 17 cancers from the CancerSEA database.\u003cstrong\u003e (D)\u003c/strong\u003e The enrichment of biological functions in gliomas samples with LAP2α expression in the TCGA database.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3806677/v1/65846e55f65ce67cef24d3f0.jpg"},{"id":49083259,"identity":"e6093ec9-fcee-4ed8-8a3b-7eac133c1438","added_by":"auto","created_at":"2024-01-02 20:21:39","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2165234,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of Prognostic Risk-Score Model in Glioma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A-B)\u003c/strong\u003e LASSO Cox regression analysis of four LAP2α-related risk genes.\u003cstrong\u003e \u003c/strong\u003ePatient status distribution, survival statuses, and signature gene expression levels for glioma patients in training \u003cstrong\u003e(C)\u003c/strong\u003e and validation sets \u003cstrong\u003e(D) \u003c/strong\u003ewere visualized. We performed survival analyses between the high- and low-risk score groups in CGGA GBM cohort\u003cstrong\u003e (E)\u003c/strong\u003e and TCGA GBM cohort \u003cstrong\u003e(F)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3806677/v1/d444f856c9c87ce0cb2be643.jpg"},{"id":49082563,"identity":"149f8d60-46b3-4f09-94ec-e44db16588a8","added_by":"auto","created_at":"2024-01-02 20:13:39","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":788647,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunohistochemical Staining of LAP2α in GBM tissue microarray\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe group with low LAP2α expression showed better prognostic outcomes: \u003cstrong\u003e(A)\u003c/strong\u003e IHC results in GBM tissues.\u003cstrong\u003e (B-C)\u003c/strong\u003e Kaplan-Meier curves of LAP2α in 80 GBM cases.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3806677/v1/9e801f3b150b9ac1c639b2f1.jpg"},{"id":49082564,"identity":"49568de7-ee85-4125-ab57-3e2ad03fa6bd","added_by":"auto","created_at":"2024-01-02 20:13:39","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4000876,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Effects of LAP2αKnockdown on Proliferation, Migration, Cell Cycle and Apoptosis of GBM Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e The mRNA level of LAP2α was weakened by si-LAP2α transfection in U251 and U87 cell lines. \u003cstrong\u003e(B)\u003c/strong\u003e Knockdown of LAP2α inhibited the proliferation of the U251 and U87 cell lines. \u003cstrong\u003e(C) \u003c/strong\u003eWound-healing assays indicated the knockdown of LAP2α could restrain the migration of GBM cells.\u003cstrong\u003e (D, E) \u003c/strong\u003eThe si-LAP2α induced cell apoptosis increasing, cell G2/M phase cycle arrest in U251 and U87 cell lines.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3806677/v1/e014ac4a308dffc4648044f4.jpg"},{"id":49338093,"identity":"959510c4-8305-4346-93b6-83d6344ac3dd","added_by":"auto","created_at":"2024-01-09 00:52:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2247519,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3806677/v1/bfa502ce-d61d-45f4-8bb2-ca9426202ca6.pdf"},{"id":49082566,"identity":"6298289f-2fcd-4f52-b9ea-3c173298cca7","added_by":"auto","created_at":"2024-01-02 20:13:39","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5245337,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3806677/v1/026cdc931a26b0346b8125e7.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pan‑cancer Analysis of LAP2α as a Potential Prognostic and Immunological Biomarker for Multiple Cancer Types Including Glioma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNowadays, cancer has shown a terrifying morbidity and mortality rate, and it has added enormous living and financial burden to the public\u003csup\u003e1,2\u003c/sup\u003e. Despite notable advancements in the field of cancer prevention and management, such as the utilization of targeted molecular therapy, radiotherapy, and immunological therapy, the clinical outcomes remain unsatisfactory for individuals with tumor recurrence or metastasis \u003csup\u003e3\u003c/sup\u003e. Hence, it is crucial to investigate alternative diagnostic indicators and potential treatment choices to enhance the efficacy of therapies in individuals suffering from cancer. With techniques developing, more and more researchers attempt to use multiple public resources to perform bioinformatic analysis for identifying the critical genes in tumorigenesis \u003csup\u003e4\u003c/sup\u003e. Currently, pan-cancer research has been widely applied to analyzing the common characteristics of different kinds of tumors, in order to investigate deeply the mechanism of tumor development \u003csup\u003e5,6\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSix distinct isoforms of Lamin-associated polypeptide 2 (LAP2), namely LAP2-α, β, γ, δ, ε, and ζ, were identified through alternative splicing, resulting in the production of proteins with varying molecular weights \u003csup\u003e7\u003c/sup\u003e. As the major spliced isoform of the LAP2 gene (TMPO), lamin-associated polypeptide 2 alpha (LAP2α) was found to bind to intranuclear and peripheral A-type lamins, which might play a part in construction of nucleoskeleton and chromosomal scaffold \u003csup\u003e8\u0026ndash;10\u003c/sup\u003e. In particular, the LAP2α isoform directly anchored to chromosome through a unique coiled-coil domain in the C-terminal region, which localized throughout the nucleus transmembrane domain and bind to retinoblastoma protein (pRb) \u003csup\u003e11\u003c/sup\u003e. Previous studies have shown that the kind of distinct C-terminal region binding with A-type lamins determined a definition of lamin A/C position in the nucleoplasm, which might influence postmitotic nuclear assembly with association with chromosomes \u003csup\u003e12\u003c/sup\u003e. Recent studies suggested that LAP2α was involved in the complicated regulatory network modulating pRb hypophosphorylated in the nucleus \u003csup\u003e13\u003c/sup\u003e, specifically promoting pRb-mediated repression of target genes, and accelerating progenitor cell cycle exit and initiation of differentiation in proliferative tissues \u003csup\u003e14,15\u003c/sup\u003e. Furthermore, LAP2α was found necessary to knitted pRB and the nuclear skeleton together, for preventing proteasomal degradation \u003csup\u003e16\u003c/sup\u003e. In addition to these structural roles in nuclear, LAP2α was found to combine with the heat shock protein 70, indicating that LAP2α had a critical role in resistance against stress \u003csup\u003e17\u003c/sup\u003e. Based on these findings, researchers ratiocinated that the level of LAP2α could regulate cell cycle progression, suggesting its potential role in cancer growth. In line with this, recent research has discovered an overexpression of LAP2α in tumors, including lung cancer \u003csup\u003e18\u003c/sup\u003e, medulloblastoma \u003csup\u003e19\u003c/sup\u003e and cervical cancer \u003csup\u003e20\u003c/sup\u003e. Nowadays, the underlying functions and mechanisms of LAP2α in various types of cancer are still not well understood and require additional investigation.\u003c/p\u003e \u003cp\u003eThis study involved a comprehensive analysis of the expression patterns of LAP2α and its correlation with patient prognosis, TMB, MSI, and immune checkpoint genes across various types of tumors. Furthermore, we conducted an analysis of protein interaction networks and enrichment pathways, which are implicated in the regulation of tumor progression by LAP2α. Furthermore, due to the observation that LAP2α survival analysis showed a clear tendency in glioma, we proceeded to construct a prognostic risk-score model specifically associated with LAP2α for glioma patients. Subsequently, we verified the model's accuracy using a separate dataset. To confirm the tumorigenic factors of LAP2α, we carried out additional in vitro experiments. The collective findings demonstrated that the research of LAP2α could provide novel orientations and strategies for the cancer clinical management.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e \u003cb\u003eData Collection and Processing\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe 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 \u003csup\u003e21\u003c/sup\u003e. For subsequent statistical analysis, the entire dataset was utilized after being transformed using log2(TPM\u0026thinsp;+\u0026thinsp;1). Additionally, the gene expression patterns of LAP2α in various types of cancer were obtained from GEPIA database \u003csup\u003e22\u003c/sup\u003e. The HPA obtained the protein expression patterns of LAP2α in various bodily tissue \u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrognostic Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe 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\u0026rsquo; survival with using the Kaplan-Meier database. And the \u0026ldquo;forestplot\u0026rdquo; and \u0026ldquo;survival\u0026rdquo; R package were used to calculate the log-rank P-value and hazard ratio (HR).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenomic Alterations Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe genomic alteration data of LAP2α, including alteration rate, mutated site, mutation count and type in cancer tissues, were available from cBioPortal tool \u003csup\u003e24\u003c/sup\u003e, 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\u0026rsquo;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.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImmune Infiltration Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe acquired the LAP2α-associated immune cell infiltration degree of TCGA from TIMER database \u003csup\u003e25\u003c/sup\u003e. 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.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEnrichment Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo 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 \u0026ldquo;GOplot\u0026rdquo; package and the \u0026ldquo;ClusterProfiler\u0026rdquo; R package. Furthermore, we have investigated the functional condition of LAP2α using the CancerSEA database \u003csup\u003e26\u003c/sup\u003e, 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.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConstruction of Prognostic Risk Model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLASSO regression analyses were conducted using the R packages \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;glmnet\u0026rdquo;. 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.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTissue microarray and Immunohistochemical (IHC) staining\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe tissue microarrays (TMAs) were firstly constructed by the 80 glioma tissues from patients, then incubated with anti-LAP2α antibodies (Huabio, China) for a night.\u003c/p\u003e \u003cp\u003eThe 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: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) we chose five respective fields of view (FOVs) randomly and calculate positively stained cell scores as 0 (0\u0026ndash;5%), 1 (6\u0026ndash;25%), 2 (26\u0026ndash;50%), 3 (51\u0026ndash;75%), and 4 (\u0026gt;\u0026thinsp;75%). (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) 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).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCell Culture and siRNA Transfection\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe 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\u0026prime;- GUCUAGAAGUGGCUAAGCATT-3\u0026prime;; siLAP2α-2: 5\u0026prime;- GCUUUCUAGAUCACAUAUUTT-3\u0026prime;; siLAP2α-3: 5\u0026prime;- GCAGAAUGGAAGUAAUGAUTT-3\u0026prime;; The siRNAs were transfected into the two glioma cell lines via Lipofectamine 3000 reagent.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRNA Extraction and qRT‒PCR\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe RNA extraction assay was conducted by the RNeasy mini kit (Qiagen). Afterwards, 1 \u0026micro;g 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\u0026prime;-TGGGTGCGCACAACATTATGG-3\u0026prime;; 5\u0026prime;-CCTGAGGGCATGTATCAGGA-3\u0026prime;; GAPDH: 5\u0026prime;-GGAGCGAGTTCCCTCCAATTT-3\u0026prime;;5\u0026prime;-GGCTGTTGTCATACTTCTCATGG-3\u0026prime;.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMTT Assay\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe 96-well plate was used to seed the cells, with a density of 1\u0026times;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 \u0026micro;l of DMSO was introduced into every well.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCell Cycle and Apoptosis Assay\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor 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, 10\u003csup\u003e6\u003c/sup\u003e cells were placed in 6-well dishes, subsequently gathered (including cells in the supernatant), and subjected to a 5-minute treatment with 5 \u0026micro;l of Annexin V-APC and 10 \u0026micro;l of 7-AAD. Shortly after, the specimen was immediately identified using a flow cytometer.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWound-healing Assay\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe U251 and U87 glioma cells (density 2.5 \u0026times;105 cells/well) transfected with siRNAs were inoculated in the 6-well plate for 24 h. After that, we used a 200 \u0026micro;L 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).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll the results were obtained from more than three independent experiments. Survival analysis was conducted using the Kaplan\u0026ndash;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\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 LAP2α Expression Profiles in Human Normal Tissues and Cancers\u003c/h2\u003e \u003cp\u003eFirstly, we investigated the mRNA expression levels of LAP2α in different organs based on the GTEx and TCGA databases. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, LAP2α expression were highest in thymus, followed by bone marrow and lymph node. In order to identify the distribution of LAP2α expression in the cells of blood, we analyzed through HPA/Monaco/Schmiedel datasets to find that LAP2α expression had an evident enrichment in non-classical monocyte and intermediate monocyte \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Meanwhile, we detected the LAP2α mRNA expression in TCGA dataset, which included 32 types of tumors. The results suggested that LAP2α was differentially expressed in 27 of 32 cancer types. Specifically speaking, the expression level of LAP2α was lower in kidney renal papillary cell carcinoma (KIRP), kidney renal clear cell carcinoma (KIRC) and thyroid carcinoma (THCA) than that in para-cancer tissue(P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the expression level of LAP2α was higher in the rest of cancers when compared to normal tissues \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Then, we found LAP2α was significantly upregulated in adrenocortical carcinoma (ACC), cholangiocarcinoma (CHOL), glioblastoma multiforme (GBM), brain lower grade glioma (LGG), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC), lung squamous cell carcinoma (LUSC) and esophageal carcinoma (ESCA) via combining the TCGA and GTEx database \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Furthermore, we demonstrated the positive association between LAP2α and the advanced pathological stages of the cancers, including lung adenocarcinoma (LUAD), COAD, KIRP, thymoma (THYM), liver hepatocellular carcinoma (LIHC), ovarian serous cystadenocarcinoma (OV) and ACC \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlation Between LAP2α Expression and Prognosis in Various Tumors\u003c/h2\u003e \u003cp\u003eWe conducted univariate Cox survival analysis using data from the TCGA database to investigate the correlation between LAP2α expression level and its clinical significance, specifically focusing on overall survival (OS) and disease-specific survival (DSS). As Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA showing, the forest plots showed that LAP2α expression was significantly correlated with poor OS in GBM/LGG (HR\u0026thinsp;=\u0026thinsp;2.15, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), LGG (HR\u0026thinsp;=\u0026thinsp;1.96, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), LUAD (HR\u0026thinsp;=\u0026thinsp;1.34, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), KICH (HR\u0026thinsp;=\u0026thinsp;6.62, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and mesothelioma (MESO, HR\u0026thinsp;=\u0026thinsp;1.48, P\u0026thinsp;=\u0026thinsp;0.01) patients. The Cox regression analysis of DSS suggested that the high LAP2α expression was related to worse DSS in GBM/LGG (HR\u0026thinsp;=\u0026thinsp;2.14, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), LGG (HR\u0026thinsp;=\u0026thinsp;1.92, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), KIRP (HR\u0026thinsp;=\u0026thinsp;2.08, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), KICH (HR\u0026thinsp;=\u0026thinsp;12.98, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), LUAD (HR\u0026thinsp;=\u0026thinsp;1.39, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and MESO (HR\u0026thinsp;=\u0026thinsp;1.87, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) patients. Subsequently, we also performed the Kaplan\u0026ndash;Meier survival analysis to acquire an explicit correlation between LAP2α expression and the outcome of patients with various types of cancer. The cancer survival rates indicated that individuals with elevated LAP2α levels typically have worst OS and DFS in MESO, GBM, LIHC, SARC, ACC and UVM. This suggests that LAP2α could serve as an important prognostic biomarker across various types of cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Moreover, we compared the prognostic value of LAP2α expression in different cancers via GEPIA2 database. The analytical result showed that high LAP2α mRNA expression levels were strongly correlated with grave prognosis in OS and DSS of GBM/LGG, MESO and KIRP patients (P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The results above illustrated that patient with high levels of LAP2α had a poorer OS and DSS than those with low expression of LAP2α in MESO, KIRP, especially in GBM/LGG.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The Genetic Alteration Landscape of LAP2α in Different Tumors\u003c/h2\u003e \u003cp\u003eNext, we explored the LAP2α alteration status via the cBioPortal online site, which contained all the TCGA cohorts with 32 objects and 10,967 samples. The most common change observed in LAP2α, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, was the occurrence of \u0026ldquo;mutation\u0026rdquo; in cases of endometrial cancer. Meanwhile, the \u0026ldquo;amplification\u0026rdquo; was most predominant mutation types in pleural mesothelioma and prostate cancer. Moreover, we found 158 mutation sites including 123 missenses, 29 truncating, two splices, two fusion and two inframe between amino acids 0 and 694, and the specific mutation sites, case types and numbers were showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB. We additionally examined the number of mutations in LAP2α using the information from TCGA databases. Among the 32 cancers, shallow deletion and gain were the most common in terms of LAP2α mRNA expression in pan-cancer \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Additionally, we performed the correlation analysis about genomic-instability of LAP2α and progression of various tumors. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, LAP2α expression showed a positive correlation with TMB in LUAD, KICH, ACC, STAD, COAD, GBM, LGG, and BLCA, while negatively in THYM and THCA. In READ, ACC, SARC, STAD, GBM, STES, MESO, and COAD, the expression of LAP2α showed a positive correlation with MSI. However, in DLBC, GBM, PRAD, and HNSC, it exhibited a negative correlation. Besides, LAP2α also positively correlated with MATH in ESCA, MESO, UCS, BLCA, BRCA, TGCT, LAML KICH and ACC, and negatively in DLBC, GBM/LGG, THYM and KIRC. Next, we investigated the correlation between LAP2α levels and particular genomic features, including somatic mutations and copy number variations (CNVs), in the TCGA dataset of glioma. The results indicated the top 15 high-frequency mutated genes, of which TP53 was obviously dominating (37.5%), followed by PTEN (34.4%) and EGFR (32.8%) in gliomas \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. The findings suggest that alteration in the LAP2α gene could have significant implications in the advancement of tumors, particularly in glioma.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 LAP2α-related Immune Infiltration in Pan-Cancer\u003c/h2\u003e \u003cp\u003eIn view of speculation that LAP2α expression closely associated with alteration-related factors, which could modify the response to immune therapy of tumor, we conducted the correlation analyses between LAP2α level and immune infiltration. The heatmap in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA demonstrated a positive correlation between LAP2α and the infiltration levels of CD4\u0026thinsp;+\u0026thinsp;T cells, B cells, and M1-like macrophages. Conversely, LAP2α showed a negative correlation with the infiltration levels of regulatory T cells (Tregs) across various cancers, including GBM/LGG. To further identify the effects of disparate levels of LAP2α and immune cells (macrophages and neutrophil) on the survival time of GBM and LGG patients, we performed Kaplan-Meier analysis based on TCGA dataset. The findings indicated that GBM individuals with elevated LAP2α expression and macrophage presence had a poorer overall survival compared to patients with lower macrophage levels (P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The macrophage level did not have the ability to impact the OS of GBM patients, regardless of whether they had high or low expression of LAP2α. Similarly, the groups combined high LAP2α expression and low macrophage level showed better OS in LGG patients. Notably, LGG individuals who exhibited both diminished LAP2α expression and neutrophil level demonstrated superior OS compared to those with elevated neutrophil level, whereas individuals with both elevated LAP2α expression and neutrophil level exhibited the poorest prognosis (P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Given the correlation between elevated LAP2α levels and poorer OS in GBM/LGG, the combination of high LAP2α expression and increased macrophages in a group resulted in a worse overall survival outcome. To ascertain the possible functions of LAP2α in immunotherapy, we examined the association between the expression of LAP2α and PD-1 as well as PD-L1. In nearly all types of cancer, the findings indicated a positive correlation between the expression of LAP2α and PD-1 as well as PD-L1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Considering that most immune checkpoint-related genes have participated in crucial mechanisms of tumor immune evasion, we further investigated the correlation between LAP2α and immune checkpoint genes using TCGA databases. As Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD displaying, LAP2α was strongly linked (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) to inhibitory functions of several immune genes, such as CD276, VEGFA, CD274, IL10, and EDNRB, and activation of HMGB1, ICOS, CD28, ICAM1, CD40 and TNF in diverse cancer types. The profiles indicated that LAP2α, to a certain degree, was involved in the pathways related to immune infiltration and could play a crucial role in tumor immunotherapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Enrichment Analysis of LAP2α-Related Partners\u003c/h2\u003e \u003cp\u003eTo dig into the mode of action in the biological processes of cancer cells, we examined the networks of proteins that bind to LAP2α using the STRING online database. The PPI network identified five LAP2α-binding proteins, namely, lamin B1 (LMNB1), lamin A (LMNA), VRK serine/threonine kinase 1 (VRK1), VRK serine/threonine kinase 2 (VRK2) and BAF nuclear assembly factor 1 (BANF1). Based on the GEPIA2 tools, we conducted the correlation analysis and found that BANF1 (R\u0026thinsp;=\u0026thinsp;0.29), LMNA (R\u0026thinsp;=\u0026thinsp;0.2) and VRK2 (R\u0026thinsp;=\u0026thinsp;0.21) were all obviously associated with the expression of LAP2α (P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Then we evaluated association between the LAP2α and involved enrichment pathway from TCGA. According to the findings presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, it was indicated that LAP2α exhibited a positive correlation with angiogenesis, cellular reaction to hypoxia, apoptosis, and tumor advancement. Conversely, LAP2α showed a negative association with arginine and proline metabolism, arginine biosynthesis, alanine aspartate and glutamate metabolism, as well as primary bile acid biosynthesis. Furthermore, we studied the functional state of LAP2α in various cancer cell types by using CancerSEA database, which offered informative data on the association between LAP2α and 14 functional states of cancer at the level of individual cells. The diagram illustrated that LAP2α expression positively correlated with cell cycle, DNA damage and repair. Meanwhile, the negative association was observed in process of hypoxia and inflammation in most of the tumors. Intriguingly, the LAP2α expression was significantly associated with cell cycle, DNA damage, DNA repair, epithelial to mesenchymal transition (EMT), invasion, metastasis and proliferation in glioma, which means that LAP2α has the potential to be a biomarker and play a role in the advancement of glioma \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. For further exploring the biological functions of LAP2α in glioma, we conducted the GO analysis of LAP2α-related genes. The figure showed the enrichment processes of five interacted genes, including nuclear envelope reassembly, nuclear envelope organization and endomembrane system \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Construction of Prognostic Risk-Score Model in GBM\u003c/h2\u003e \u003cp\u003eThe main clinical characteristics of LAP2α in CGGA and TCGA were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A total of 422 and 665 cases were analyzed in this study, among them, the expression of LAP2α had a high correlation with histology, grade and IDH mutation status in GBM through CGGA and TCGA database. Based on the above discoveries, next we were aimed to evaluate the predictive significance of LAP2α in glioma. by means of constructing a LAP2α-associated prognostic signature model in glioma. Firstly, we conducted the Lasso Cox regression algorithm by integrating the genes from LAP2α-related PPI network \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B\u003cb\u003e)\u003c/b\u003e. Subsequently, we established a risk model with LMNB1, LMNA, VRK2 and BANF1. We obtained the regression coefficients for these genes and used them to calculate the risk score with the formula: RiskScore\u0026thinsp;=\u0026thinsp;0.3117 \u0026lowast; LMNB1\u0026thinsp;+\u0026thinsp;0.5928 \u0026lowast; LMNA\u0026thinsp;+\u0026thinsp;0.334 \u0026lowast; VRK2\u0026thinsp;+\u0026thinsp;0.2234 \u0026lowast; BANF1. To better verify the availability of the model, the CGGA database and TCGA database were used respectively as training set and validation set. Additionally, we determined the distribution of risk score, patient outcome, and expression profiles of the included four genes in both the training and validation datasets, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D. Furthermore, we generated Kaplan-Meier survival plots in both internal and external datasets. Then we categorized the glioma patients into groups of low-risk and high‐risk based on the median risk score. According to the OS Kaplan‐Meier curve, patients in the high-risk category implied an obviously reduced likelihood of survival compared to those in the low-risk category \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE-F\u003cb\u003e)\u003c/b\u003e. These findings indicated that the prognostic signature associated with LAP2α could offer a relatively precise prognosis prediction for patients with GBM.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between LAP2α expression and clinicopathologic features in GBM samples from the CGGA database.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh(N\u0026thinsp;=\u0026thinsp;211)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow(N\u0026thinsp;=\u0026thinsp;211)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal(N\u0026thinsp;=\u0026thinsp;422)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAstrocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71(18.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95(24.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166(42.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90(23.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45(11.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e135(34.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOligodendroglioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50(12.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38(9.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88(22.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.40E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46(10.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92(21.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138(32.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72(17.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72(17.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e144(34.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93(22.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47(11.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e140(33.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.30E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74(19.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134(34.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e208(54.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWildtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101(26.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74(19.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e175(45.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMGMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethylated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115(33.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82(23.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e197(57.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUn-methylated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83(24.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63(18.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146(42.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between LAP2α expression and clinicopathologic features in GBM samples from the TCGA database.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh(N\u0026thinsp;=\u0026thinsp;333)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow(N\u0026thinsp;=\u0026thinsp;332)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal(N\u0026thinsp;=\u0026thinsp;665)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.40E-45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAstrocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70(10.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185(28.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e255(39.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e199(30.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28(4.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e227(34.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOligodendroglioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51(7.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117(18.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e168(25.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.20E-43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47(7.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178(28.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e225(36.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122(19.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120(19.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e242(39.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e141(22.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(1.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e148(24.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.40E-46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123(18.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e299(45.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e422(64.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWildtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e203(31.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28(4.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e231(35.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMGMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.90E-13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethylated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188(29.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e287(45.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e475(75.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUn-methylated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115(18.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42(6.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e157(24.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Immunohistochemical Staining of LAP2α in Human Protein Atlas Database and GBM tissue microarray\u003c/h2\u003e \u003cp\u003eAs shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e, LAP2α presented weak staining in normal breast, glioma, lung, melanoma, colon, liver, stomach and urothelial tissues, while had moderate or strong staining in corresponding tumor tissues. To conclude, at the protein level, LAP2α exhibited a higher protein expression in these tumor tissues than the corresponding paracancerous tissues, suggesting that LAP2α could contribute to the development of different cancers, also provide some insights of novel therapeutic targets. We further determined the association between LAP2α level and clinical outcomes in GBM patients by LAP2α microarray contained 80 samples of GBM tissues \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. And the survival curves showed that the low expression of LAP2α in GBM patients indicating a better OS and FPS, suggesting that LAP2α could be a valuable indicator in patients with GBM \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-C\u003cb\u003e)\u003c/b\u003e. Additionally, the expression of LAP2α was positively associated with MGMT status of GBM patients \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between LAP2α expression and clinicopathologic features in GBM samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh(N\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow(N\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22(27.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12(15.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26(32.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20(25.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.63\u0026thinsp;\u0026plusmn;\u0026thinsp;10.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.63\u0026thinsp;\u0026plusmn;\u0026thinsp;13.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.56\u0026thinsp;\u0026plusmn;\u0026thinsp;15.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.14\u0026thinsp;\u0026plusmn;\u0026thinsp;20.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20E-43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3(4.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(3.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWildtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37(56.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24(36.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMGMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethylated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26(36.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(9.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUn-methylated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20(27.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19(26.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTERT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19(29.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11(16.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWildtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20(30.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15(23.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.8 The Effects of LAP2α Knockdown on Proliferation, Migration, Cell Cycle and Apoptosis of GBM Cells\u003c/h2\u003e \u003cp\u003eIn order to confirm the molecular roles of LAP2α in glioma cells, we silenced the LAP2α expression in the U251 and U87 cell lines, individually \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Subsequently, the MTT assay findings revealed that the suppression of LAP2α greatly impeded the capacity of cell proliferation in both GBM cell lines \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Furthermore, the wound healing experiment demonstrated that the suppression of LAP2α could decelerate the migration speed of GBM cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Meanwhile, we conducted apoptosis and cell cycle assays to confirm that the decrease in LAP2α caused apoptosis and cell cycle arrest in GBM cells during the G2/M phase \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD-E\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLamina-associated polypeptide 2 (LAP2), also known as thymopoietin (TMPO), can generate six splice isoforms (α, β, γ, δ, ε, ζ) by means of alternative splicing. According to position of transmembrane domain, these isoforms were reported to be divided into two parts: localization in inner nuclear membrane and in nucleoplasm through unique C-terminus, which can bind to pRb protein \u003csup\u003e10,27\u003c/sup\u003e. Nevertheless, LAP2α have been considered as the latter category, for the unique C-terminus of LAP2α lacking a transmembrane domain, which is replaced by a massive four-stranded antiparallel coiled coil dimer \u003csup\u003e28\u003c/sup\u003e, specifically binding to A-type lamins in nucleoplasm \u003csup\u003e29\u003c/sup\u003e. Recent observations indicated that LAP2α\u0026rsquo;s C-terminus could play roles in the process of cell cycle and mediate the tumor suppressor via interacting with pRb \u003csup\u003e30\u003c/sup\u003e. Previous studies also reported that LAP2α was overexpressed in proliferating cells and diverse human tumor samples with variational transcript and protein levels \u003csup\u003e31,32\u003c/sup\u003e. It is still uncertain from these studies whether it specifically controls the onset and progression of tumors. Hence, the potential roles of LAP2α in initiation and progression of different kinds of tumors are worthy of determination.\u003c/p\u003e \u003cp\u003eThis study investigated the oncogenic function and prognostic significance of LAP2α in various types of tumors, including glioma. Specifically, we discovered that LAP2α expression was elevated in different types of cancers compared to their corresponding noncancerous tissues, suggesting tumorigenic potential of LAP2α and broad foreground in the aspects of cancer research. Consistent with this view, a previous investigation showed the overexpression of LAP2α in cervical carcinoma, and further revealed the critical regulation mechanism of LAP2α \u003csup\u003e20\u003c/sup\u003e. Our research discovered a correlation between the presence of LAP2α in different types of cancers and the tumor stage, which may provide vital implications for diagnosis of patients in different stages. The COX regression and Kaplan-Meier analyses suggested that upregulation of LAP2α had high risks of worse OS and DSS in GBM/LGG, MESO, KIRP, KICH, LUAD and ACC. In the TCGA dataset, a strong correlation was observed between elevated LAP2α levels and unfavorable overall survival (OS) and disease-free survival (DFS) in GBM, MESO, LIHC, SARC, ACC, and UVM. Obviously, we can hypothesize that LAP2α is able to serve as a prognostic indicator of GBM patients.\u003c/p\u003e \u003cp\u003eAs a kind of somatic mutation, TMB is an available predictive biomarker with the potential to lead to flourishing development of tumor precision medicine \u003csup\u003e33\u003c/sup\u003e. Previous research revealed that most individuals with non-small-cell lung cancer and a high burden of somatic nonsynonymous mutations experienced improved objective response rate (ORR) and progression-free survival (PFS) following treatment with pembrolizumab \u003csup\u003e34\u003c/sup\u003e. Therefore, the level of TMB was seen as genomic biomarker that can forecast the response to immune checkpoint inhibitors (ICIs). The loss activity of DNA mismatch repair typically leads to the generation of MSI, which is also acknowledged as a beneficial biomarker for ICIs. It has been shown that the patients with high frequency of MSI were generally accompanied by hypersensibility to immunotherapy and chemotherapeutics in colorectal tumors \u003csup\u003e35\u003c/sup\u003e. MATH is deemed to serve as a clinically practical indicator to identify the correlations between intratumor genetic heterogeneity and outcome of patients. A study found that head and neck squamous cell carcinoma (HNSCC) tumors with a higher MATH score might have more various populations of cells lead to metastasis and low target therapeutic efficiency than their less MATH group \u003csup\u003e36\u003c/sup\u003e. Our study found increase LAP2α expression significantly correlated with TMB, MSI and MATH in GBM/LGG, LUAD, KICH, STES, STAD, and BRCA, with the fact that genome stability strongly associates with progression of cancer, and we further affirm the oncogenic function of LAP2α and prediction ability of the patient's immunotherapy efficacy.\u003c/p\u003e \u003cp\u003eFurthermore, we examined that LAP2α played a significant role in the immune response against tumors. According to public databases, our analysis revealed a clear positive correlation between the expression of the LAP2α gene and CD4\u0026thinsp;+\u0026thinsp;T cells, B cells, and M1-like macrophages in GBM/LGG. As is known to us all, the discovering of immune biomarkers grows in importance in this era of stratified medicine. Additionally, we conducted Kaplan-Meier analysis to find the relationship between LAP2α levels, infiltrated immune cells and clinical outcomes. Several research studies have shown that tumor-associated macrophages (TAMs) and tumor-infiltrating neutrophils (TINs) significantly impact the prognosis and effectiveness of cancer treatments, such as chemotherapy and immunotherapy \u003csup\u003e37,38\u003c/sup\u003e. In this study, it was observed that GBM/LGG patients with both elevated LAP2α expression and increased macrophage levels had a significantly poorer overall survival compared to those with high LAP2α expression and low macrophage levels. The negative correlation between tumor-associated macrophages and clinical prognosis is widely recognized. Based on the findings in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, there is already evidence of a direct link between LAP2α and macrophages. Additionally, a strong association exists between high LAP2α expression and poorer overall survival in GBM/LGG. Consequently, it is apparent that the level of LAP2α expression and immune infiltration can potentially serve as accurate predictors for the prognosis of GBM/LGG patients. Moreover, immune checkpoints, as a kind of molecules able to regulate functions of T cells, can directly affect the inhibition and stimulation pathways exerted by immune cells \u003csup\u003e39\u003c/sup\u003e. The immune checkpoint signal represented the pathway to immune escape, resulting in tumor aggressiveness. Currently, there is a growing emphasis on investigating the efficacy of ICI, a type of monoclonal antibody that attaches to PD-1 or PD-L1, resulting in enhanced immunogenicity of the tumors \u003csup\u003e40\u003c/sup\u003e. Nevertheless, most tumor patients receiving ICI targeting PD-L1 therapy do not improve their survival time, probably on account of insufficient immune activation to identify tumor-associated antigen \u003csup\u003e41\u003c/sup\u003e. As a result, it is extremely urgent to discover novel checkpoint inhibitors and targeted molecules to enhance the effectiveness of current treatments for immune checkpoint modulation \u003csup\u003e42\u003c/sup\u003e. This study involved analyzing the correlation between LAP2α and immune checkpoint genes across cancers. In diverse types of cancer, the expression of LAP2α was found to be associated with the suppression of various immune checkpoint genes, including CD276, VEGFA, CD274, IL10, and EDNRB, and activation of HMGB1, ICOS, CD28, ICAM1, CD40 and TNF in diverse cancer types. Considering the positive correlation between LAP2α expression and two immune checkpoint genes, CD276 and VEGFA, especially in GBM/LGG, and previous studies proved that CD276 was involved in T-cell receptor signaling pathway and indicated a worse prognosis for glioma patients \u003csup\u003e43\u003c/sup\u003e, our research can provide helpful thoughts for exploration, validation and development of immunotherapies in glioma.\u003c/p\u003e \u003cp\u003eAs a vital element in cell regulatory network in biological processes, deregulated cell signaling has generally been found in a number of human diseases, including cancer. Functional enrichment analysis of LAP2α was performed in this study using the TCGA dataset. The findings indicated a strong association between LAP2α and processes angiogenesis, cellular response to hypoxia, apoptosis, tumor progression, and diverse amino acid metabolism. Moreover, our analysis of the CancerSEA database revealed a significant association between LAP2α and various processes in glioma, such as cell cycle regulation, DNA damage response, DNA repair mechanisms, and cellular proliferation. In keeping with our analysis, previous study reported that LAP2α played an important role in chromatin reorganization during cell apoptosis \u003csup\u003e44\u003c/sup\u003e. A recent investigation revealed that LAP2α could promote the combination between replication protein and ssDNA produced by damaged chromatin, consequently protect the replication forks and initiate repair of damaged DNA \u003csup\u003e45\u003c/sup\u003e. Another study suggested that DNA damage-related chromosomal instability induced by LAP2α could lead to metastasis of breast cancer \u003csup\u003e46\u003c/sup\u003e. However, it has not yet research about the oncogenetic mechanism of LAP2α in glioma, thus the roles of LAP2α-related regulation mechanisms in tumorigenesis of glioma need to be further validated.\u003c/p\u003e \u003cp\u003eAs the most common intracranial primary malignancy in adults, GBM brings great disservice and loss to society. Despite present standard treatment, the median patient survival is still less than 14 months \u003csup\u003e47\u003c/sup\u003e. Therefore, there is urgent need to explore more available prognostic indicators as well as credible prognostic models of GBM. The Lasso Cox regression analysis indicated that the risk signature in GBM could enhance sensitivity and precision of prognosis, so as to better provide guidance for anti-tumor therapy.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eCombined with the above results, our pan-cancer analysis revealed distinct expression patterns of LAP2α in tumor and neighboring healthy tissue. Furthermore, LAP2α exhibited a significant correlation with the clinical outcome in the majority of malignancies, suggesting that LAP2α is able to become a promising indicator for the diagnosis and survival of individuals with cancer. Additionally, our research uncovered the infiltration of immune cells that influenced by LAP2α in various types of cancers, indicating its potential as a valuable tool for personalized immunotherapy in the future. Furthermore, the suppression of LAP2α hindered the malignant advancement of GBM cells, leading to decreased cell growth, arrest of the cell cycle in the G2/M stage, heightened rate of cell death, and impeded cell migration. In conclusion, the comprehensive analysis of various types of cancer revealed the significance of LAP2α, particularly in glioma, and also provided novel insights into therapeutic strategy for tumor patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; D.W. conceived the idea, analyzed the data, and drafted the work. D.W. and D.Y. analyzed the data and performed the visualization. D.Y., F.T., Z.P., and Z.W. collected the data and participated in the revision. C.M. and Z.L. supervised the study and provided funding support. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the project of Hubei province and the Translational Medicine Research Fund of Zhongnan Hospital of Wuhan University (YYXKNL2023011, ZNJC202206).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., and Bray, F. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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This research was aimed to investigate the involvement of LAP2α in cancer development, especially in glioma. Based on public datasets, we found LAP2α was upregulated in most cancers, and the survival analysis indicated a distinct correlation between elevated LAP2α expression and unfavorable prognosis among glioma patients with elevated levels of macrophage and neutrophil. LAP2α level evidently associated with the immune checkpoint therapy related genes in cancers. Specifically, we made the tissue microarray covered 80 glioma patients with prognostic analysis, and verified that reducing LAP2α hindered the growth and movement capacity of the glioma cells. Our data suggests that LAP2α may be an important tool for indication of immunotherapy and medical prognosis in pan-cancer, and is expected to have a critical role in the oncogenesis of glioma.\u003c/p\u003e","manuscriptTitle":"Pan‑cancer Analysis of LAP2α as a Potential Prognostic and Immunological Biomarker for Multiple Cancer Types Including Glioma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-02 20:13:34","doi":"10.21203/rs.3.rs-3806677/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"745b9244-10bd-480b-b524-99ea92322ab1","owner":[],"postedDate":"January 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-09T00:44:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-02 20:13:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3806677","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3806677","identity":"rs-3806677","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}