Prognostic value and immune status of AIM2 in skin cutaneous melanoma

Prognostic value and immune status of AIM2 in skin cutaneous melanoma | 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 Prognostic value and immune status of AIM2 in skin cutaneous melanoma Yong Sheng Long, Jing Xu, Yu Mao Wang, Wan Qian Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3899213/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 Background Absent in melanoma 2 (AIM2) is an important developmental regulator for innate immune responses, and recent studies on AIM2 have reported its vital role in cancer development and progression. However, AIM2 in skin cutaneous melanoma (SKCM) tumor immune microenvironment has not been extensively studied. Methods We explored the expression and prognostic value of AIM2 at the pan-cancer level based on multiple public databases. We analyzed the SKCM transcriptome sequencing data and clinical information, available on various public databases, using differential analysis, prognostic analysis, machine learning, and various immune infiltration algorithms. We used online visualization databases to explore AIM2 expression in SKCM to determine its prognostic impact. Furthermore, we constructed a risk signature based on AIM2-related genes. Results Based on the pan-cancer analysis, AIM2 was found to be an independent prognostic factor for SKCM. AIM2 expression notably differed in SKCM and was associated with an improved survival rate among patients. Increased AIM2 expression promoted the immune response of patients with SKCM, inducing pyroptosis, apoptosis, and necroptosis. In vitro transwell assay and scratch test showed that the knockdown of AIM2 expression increased its invasiveness and metastasis of the SKCM cell line, A875. Knockdown of AIM2 expression revealed decreased expression of ZBP1 and MEFV , the important genes in the PANoptosis complex. Simultaneously, the expression of pyroptosis, apoptosis, and CD8 + T cell marker genes ( GSDMD , CASP-8 , and CD8A ) also decreased. The infiltration levels of various antitumor immune cells positively correlated with AIM2 expression, and the infiltration levels notably differed between patients with high and low levels of AIM2 expression. The Tumor Immune Dysfunction and Exclusion framework analysis revealed that AIM2 expression accurately facilitated the prediction of the efficacy of SKCM immunotherapy. Mechanistic analysis revealed an association between AIM2 overexpression and PANoptosis signaling upregulation, thereby affecting the patterns of chemokines and cytokines in TIME. Furthermore, the prediction and prediction performance of the prognostic model was found to be accurate. Conclusion AIM2 is associated with an increased abundance of effector CD8 + T cells, positive responses to immune checkpoint blockade treatment, and improved SKCM prognoses. Therefore, it could be used as a putative enhancer and prognostic biomarker for SKCM treatment. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Skin cutaneous melanoma (SKCM) is the most malignant and deadly among all skin tumors[ 1 ], and its incidence and mortality rates are increasing[ 2 , 3 ]. Although the prognoses of patients with advanced SKCM have improved in recent years owing to the developments in immunotherapy targeting immune checkpoints[ 4 , 5 ], only a few patients have benefitted from immunotherapy[ 6 , 7 ]. The lack of clinically validated predictive biomarkers is among the primary reasons for the inconsistent efficacy of current immunotherapy. Programmed death-ligand 1 (PD-L1) expression, tumor mutational burden (TMB), neoantigen load, mismatch repair, microsatellite instability, and various gene-specific biomarkers (such as mutations and tumor-infiltrating lymphocytes) have been successfully used to predict the effects of immune checkpoint inhibitor (ICI) therapy[ 8 ]. However, these potential markers have certain limitations, such as spatial heterogeneity and temporal variation in the detection of PD-L1[ 9 , 10 ]. The TMB of Asian melanoma is very low and not yet sufficiently reliable to predict overall survival (OS)[ 11 – 13 ]. Therefore, effective biomarkers are urgently required to guide clinical immunotherapy. AIM2 consists an N-terminal pyrin domain (PYD) and a C-terminal hematopoietic interferon-inducible nucleoprotein domain with a 200-amino acid repeat sequence (HIN200). HIN200 has a double-stranded deoxyribonucleotide (dsDNA) recognition ability[ 4 ] for binding to single-stranded DNA[ 5 ]. PYD is a folded structure composed of six α-helices, which recruits and binds to the protein apoptosis-associated speck-like protein via highly specific PYD–PYD interactions to complete the initial steps of inflammasome assembly[ 6 ]. PYD is an important receptor of DNA in cells and an important pattern recognition receptor of inflammasomes. It plays an important role in processes such as pyroptosis, PANoptosis, and innate immune response[ 3 ]. AIM2 plays a pleiotropic role in the tumor microenvironment (TME) as an important cellular regulator of innate immune responses[ 14 ]. AIM2 is a pro-oncogenic factor in non-small-cell lung cancer, in breast cancer caused by human epidermal growth factor receptor 2+[ 15 – 17 ], in colon cancer, and in SKCM[ 18 – 22 ]. A recent study on the dual role of AIM2 in TME reported that it inhibits the cyclic guanosine monophosphate–adenosine monophosphate synthase–stimulator of interferon pathway, thereby evading the adaptive immune responses[ 15 ]. The underlying regulatory roles of AIM2 in TME and its impact on immune checkpoint blockade (ICB) therapy, however, remain unknown. A previous study reported the inhibitory effect of AIM2 overexpression on the progression of melanoma and the proliferation of mouse fibroblasts[ 23 ]. There is no reported consensus on the mechanism underlying the function of AIM2 in SKCM; thus, its specific mechanism of action and TME regulation are unknown. Moreover, it is unclear whether AIM2 can be used as a biomarker for the prediction of prognoses and ICB treatment responses in patients with SKCM. Based on the existing knowledge on the dual role of AIM2 in tumors, we aimed to determine the prognostic roles of AIM2 in SKCM along with its effects on the TME and ICB. The findings are expected to shed light on the multifaceted roles of AIM2 in tumors and may provide a theoretical basis for cytokine-based immunotherapy for patients with SKCM. Materials and Methods SKCM patients and ethics approval This study included 13 patients diagnosed with SKCM from August 2020 to August 2021. Patients with SKCM were not treated with tumor-related treatment before resection and did not have other serious diseases during the same period. The procedures involved in this study were conducted with the understanding and knowledge of the participants, and the relevant written informed consent was obtained. This project strictly followed the Declaration of Helsinki (№ 2022A-066) and was approved by the Second Research Ethics Committee of Lanzhou University Hospital. and carried out according to the provisions of the Helsinki Declaration. Data Collection The RNA-seq data of The Cancer Genome Atlas (TCGA) database and the corresponding clinical information of 33 patients with cancer were obtained from the Genomic Data Commons (GDC) data portal ( https://portal.gdc.cancer.gov/ ). The normal tissue specimen data were obtained from the Genotype-Tissue Expression v8 database ( https://gtexportal.org/home/datasets ). A total of 15 SKCM datasets (GSE19293, GSE22153, GSE22154, GSE59455, GSE54467, GSE53118, GSE133713, GSE100797, GSE65904, GSE98394, GSE99898, GSE78220, GSE190113, GSE53118, and GSE19234) cohorts were obtained from Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ) were used to further validate AIM2 expression and its survival analysis. The characteristics of the 15 datasets are detailed in supple table 1. Immunocorrelational analysis Based on six algorithms in the immunedeconv package[ 24 ] ( CIBERSORT[ 25 ], TIMER[ 26 ], MCPCOUNTER[ 27 ], QUANTISEQ[ 28 ], EPIC[ 29 ], xCell[ 30 ]), ssGSEA[ 31 ], and ESTIMATE[ 32 ], differences in the infiltration abundance and immune scores of various immune cells between patients with high and low levels of AIM2 expression in the TCGA-SKCM dataset were analyzed. Subsequently, based on eight immune algorithms (CIBERSORT, TIMER, EPIC, xCell, MCPCOUNTER, QUANTISEQ, ESTIMATE, and CIBERSORT_ABS), the TCGA-SKCM dataset and 15 SKCM datasets in the GEO database were analyzed to examine the abundance of immune cell infiltration and immune scores in TME and SKCM correlation between AIM2 expression in patients. We also explored the correlation between AIM2 expression and CD8 + T cell infiltration levels using the TIMER2.0 online database( http://timer.cistrome.org/ )[ 33 ] and TIMER algorithm. The effect of AIM2 expression on the prognosis of the ICB-treated group was assessed using the TIDE online database༈Tumor Immune Dysfunction and Exclusion (TIDE) http://tide.dfci.harvard.edu/login/༉ [ 34 ]. Finally, we also explored the correlation between AIM2 expression and immune markers, immune checkpoints, major histocompatibility complex (MHC) molecules, chemokines, chemokine receptors, immune-activators, and T cell signature genes in different types of immune cells. Acquisition of differentially expressed genes (DEGs) and analysis of functional enrichment In this study, The limma R software package and the Student t test were used to monitor DEGs,The threshold values of “| Log 2 (FC) | >1 and adj p < 0.05” were used for DEG screening,The P value of the t test was adjusted by the Benjamini-Hochberg method[ 35 ]. To elucidate the putative biological role of AIM2 in SKCM, the ClusterProfiler package in R was used for gene ontology (GO) annotation, GSEA, and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis; the online visualization database, WebGestalt ( http://www.webgestalt.org/ ) [ 36 ], was used to perform functional enrichment analysis. Comprehensive analysis of AIM2 in SKCM First, the mRNA and protein expression levels of AIM2 in SKCM were analyzed using data from TCGA, GTEx, Human Protein Atlas (HPA), GSE46517, GSE15605, and cohorts from GEO databases. We performed correlational analyses between mRNA expression levels of AIM2 in SKCM and various clinical characteristics based on TCGA data. Subsequently, we performed correlational analyses between AIM2 expression levels and the GSE65904 and TCGA–SKCM datasets with. Using disease-free survival (DSS) and OS rates, we predicted the clinical prognoses of patients with SKCM. Based on the TCGA–SKCM data, multivariate COX regression and Kaplan–Meier analyses were used to evaluate clinical factors that significantly affected survival. A prognostic alignment diagram-based model was constructed and its prognostic value along with the immune correlations and risk scores were analyzed. Finally, functional enrichment and PANoptosis analyses were performed. Identification of AIM2-related genes for the construction of risk signature To further study the expression characteristics of AIM2 in SKCM, based on the TCGA–SKCM and GSE65904 data sets, AIM2-related genes (correlation coefficient > 0.6) were screened, and R language was used based on their expression levels. Finally, least absolute shrinkage and selection operator (LASSO) and multivariate COX regression analyses were performed on OS prognosis-related genes to construct a risk signature of AIM2-related genes. Risk signature was calculated as follows: risk signature = Σ(Expi × coefi), where Coefi and Expi represent the risk factor and expression of each gene, respectively. Survival analysis, evaluation of immune scores, analysis of immune checkpoint-related gene expression, and functional enrichment analysis were performed for evaluating the molecular subtypes of high and low AIM2 expression groups. Immunohistochemistry (IHC) Thirteen patients pathologically diagnosed with SKCM in the Second Affiliated Hospital of Lanzhou University were included. The paraffin-embedded formalin-fixed SKCM tissues and adjacent normal skin tissues of these patients with SKCM were subjected to IHC staining to verify the expression levels of AIM2 in SKCM tissues. Standard staining procedures were followed for IHC staining. The antibody against AIM2 used in this study was obtained from Abcam (No. ab204995). Cell culture and transfection The human skin melanoma cell line (A875) was purchased from Beina Bio (Shanghai, China). A875 was incubated at 37°C under 5% CO 2 conditions in a cell incubator and maintained in Dulbecco’s modified eagle medium (DMEM; Solarbio, 31600) with 10% fetal bovine serum (FBS; Bioind, Kibbutz Beit Haemek, Israel). Small interfering RNA (siRNA) targeting AIM2 were purchased from Jikai Gene (Shanghai, China). Lipofectamine™ 3000 Transfection Reagent (Invitrogen, Carlsbad, USA) was used to transfect a negative control, and A875 was transfected by introducing siRNA targeting AIM2 according to the manufacturer’s instructions. Transwell assay Add 100 µl of siRNA-transfected A875 cell suspension with a density of 5×105/ml to the upper chamber of a Transwell chamber lined with Matrigel (BD, 356234), add 600 µl of serum-containing medium to the lower chamber, and culture for 48 hours. After the cells were fixed with 4% paraformaldehyde for 30 min, the cells in the lower layer were stained with Giemsa staining solution (Solarbio, G1015) for 30 min, and the unmigrated cells in the upper layer were gently wiped off with a cotton swab, and washed 3 times with PBS. Finally, the number of invaded cells was counted under a light microscope (Olympus, Japan). Scratch test Seed a suspension of siRNA-transfected A875 cells at a density of 5 × 105/ml in a 6-well plate for 24 hours and incubate at 37°C. At 90% confluency, use a 200 µl pipette tip to create a Thin scratches were made, and non-adherent cells were rinsed three times with PBS, and images were collected using an inverted microscope (Zeiss, Germany) to determine the scratch distance at 0 h. Subsequently, cells were starved in serum-free medium. Photos were retaken at 12 h, 24 h, and 48 h using an inverted microscope (Zeiss, Germany). Use the Image-Pro Plus software measurement tool to measure the cell scratch boundary distance before and after treatment, and subtract the post-treatment distance from the pre-treatment distance, which is the migration distance of cells at 12 h, 24 h, and 48 h. Real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) siRNA-transfected A875 cells were analyzed using RT-qPCR. Cell samples were collected after the cells reached approximately 70% confluency. Total RNA was extracted according to the instructions of EZNA Total RNA Kit Ⅰ (Omega, product number: R6834-01, batch number: R6834010000D20V099). cDNA was synthesized according to the instructions of Evo M-MLV Reverse Transcription Premix Kit (ACCURATE BIOLOGY, Cat. No. AG11728, Lot No. A4A0765). Amplification was performed using cDNA as a template according to the instructions provided in the SYBR Green Pro Taq HS Premixed qPCR Kit (ACCURATE BIOLOGY, Cat. No.: AG11701, Lot No.: A4A0503). After amplification, the relative expression level of the target gene was calculated as F = 2−△△Ct. See Supplementary Table 2 for primer sequences. Statistical analysis All RNAseq data in the transcripts per million (TPM) reads format were normalized after log 2 transformation. The Wilcoxon signed-rank test was used to compare normal and tumor tissue. Survival analysis was performed for the two CM groups using log-sequence analysis, and the predictive performance and risk scores of AIM2 were analyzed using timeROC. Univariate and multivariate Cox regression analyses were implemented via the R language “forestplot” package. According to the results of the Cox multivariate regression analysis, the R language “rms” package was used to construct a prognostic nomogram model. Spearman’s or Pearson’s test was used to analyze the relationship between the two variables. The above results were visualized using the R (v4.0.3) language “ggplot2” and “pheatmap” packages. The results of Transwell assay, Scratch test and RT-qPCR were statistically analyzed using SPSS22.0, the independent samples between groups were analyzed by T test, and the statistical methods of multiple groups were analyzed by single factor analysis.P < 0.05 was considered significant. *p < 0.05, **p < 0.01,***p < 0.001. Results Pan-cancer analysis of AIM2 as an independent prognostic factor for SKCM AIM2 has been reported to be abnormally expressed in various tumors; therefore, to fully understand the role of AIM2 in the occurrence of pan-cancer, we analyzed its expression and prognosis. Based on the GTEx dataset, we found that the expression of AIM2 was high in blood, pituitary glands, small intestine, and spleen tissues, and the remaining normal tissues showed low expression levels (Fig. 1 (A)). Subsequently, we explored the mRNA expression of AIM2 in pan-cancer occurrence. Based on the CCLE database analysis, AIM2 showed high expression levels in 18 tumor types, of which DLBC showed high expression levels, and expression levels in BLCA, MM, SKCM, LUAD, NSC, HNSC, and LCML were higher than 2 (Fig. 1 (B)). TCGA combined with GTEx database analysis found abnormal expression in 24 tumors, including BLCA, BRCA, CESC, COAD, ESCA, HNSC, KIRC, LAML, LGG, LUAD, LUSC, OV, PAAD, SKCM, STAD, THCA and UCEC. The expression levels of AIM2 mRNA were found to be upregulated in GBM and KIRP was downregulated in THYM, ACC, DLBC, LIHC, and PRAD (Fig. 1 (C)). Univariate COX regression analyses identified AIM2 as a risk factor for the OS among patients with KIRC, ACC, BLCA, COAD, LGG, MESO, and UVM, whereas AIM2 was identified as a protective factor for OS among patients with SKCM (Fig. 1 (I)). Multivariate COX regression analyses (OS) of patients with ACC, BLCA, COAD and UVM revealed that AIM2 was an independent prognostic factor of SKCM (Fig. 1 (E-J)). Using pan-cancer analysis, AIM2 was revealed to be significantly overexpressed in patients with SKCM and was an independent prognostic factor for SKCM, implicating its crucial role in predicting clinical outcomes of patients with SKCM. Therefore, we subsequently focused on investigating the function of AIM2 in SKCM. High expression levels of AIM2 in SKCM are associated with improved prognoses To gain a comprehensive understanding of AIM2 functions underlying SKCM development, we analyzed the GTEx data, along data from TCGA and the GSE15605 and GSE46517 datasets of the GEO database. The results revealed that AIM2 mRNA and protein levels (HPA database) in patients with SKCM were found to be significantly high (p < 0.05) (Fig. 2 (A-B)). Further IHC analysis suggested that the protein levels of AIM2 in SKCM tissues were markedly higher than the normal skin tissues (Fig. 2 (C)). AIM2 expression levels were significantly correlated with radiation therapy, DSS, Breslow depth, T stage,and OS in patients with SKCM (Fig. 2 (E), Supplementary Table 3). Considering the clinical relevance of AIM2 , the Kaplan–Meier analysis suggested that high expression levels of AIM2 , according to the GSE65904 and TCGA-SKCM datasets, may indicate protective effects and facilitate prognostic improvement in patients with SKCM (Fig. 2 (D)). The abovementioned results strongly suggest that AIM2 is a tumor suppressor gene closely related to the progression and metastasis of SKCM. To confirm that AIM2 is a tumor suppressor gene of SKCM, siRNA-based analysis was performed. The proliferation and migration abilities of A875 cells were significantly increased after the expression levels of AIM2 in the A875 cells were decreased ((Fig. 2 (F)); transwell assay) (Fig. 2 (G)); scratch test), verifying that AIM2 is an important tumor suppressor gene of SKCM. Functional enrichment analysis for AIM2 Subsequently, the biological functions associated with AIM2 were analyzed based on RNA-seq data from the TCGA-SKCM cohort. First, we conducted the GO annotation and KEGG enrichment analysis of 300 genes that were positively associated with AIM2 . Among the DEGs in groups with low and high levels of AIM2 expression, all biological functions and transduction cascades were found to be related to the immune system (Supplementary Fig. 1 (A-B and F)). Thus, AIM2 was involved in the TME. To further investigate the functional roles of AIM2 in SKCM, GSEA was performed using the RNA-seq data for SKCM in the TCGA database. A total of 39 molecular pathways significantly differed between the groups with low and high levels of AIM2 expression as evidenced by the online visualization database, WebGestaltR. Of these pathways, 37 were positively correlated with the high expression group and two were correlated with the low expression group. Immune responses and inflammatory signaling pathways were found to be significantly enriched in the high expression group, whereas pathways related to oxidative phosphorylation and ribosome formation were enriched in the low expression group (Supplementary Fig. 1 (E)). GSEA based on the ClusterProfiler package in R showed that the high level of AIM2 expression in SKCM was significantly enriched the immune and inflammatory responses; it also enriched the cell-related signaling pathways for immune responses, apoptosis, pyroptosis, and necrosis (Supplementary Fig. 1 (C-D)). Furthermore, the abovementioned findings[ 37 ] along with those reported in our recent study led us to hypothesize that AIM2 promotes cellular apoptosis, pyroptosis, and necrosis by binding to ZBP1, thereby initiating PANoptosis[ 38 ]. We evaluated correlation between AIM2 expression and the marker genes of cell pyroptosis, apoptosis, and necrosis in SKCM-TCGA and GSE65904 datasets to further validate our hypothesis. TThe correlation between AIM2 and SKCM immune infiltration We further evaluated the correlation between AIM2 expression and TME based on the results of functional enrichment and annotation. First, the correlation and differential expression of AIM2 with type I antitumor response genes (interferon gamma (IFNG) and granzyme B (GZMB)), type II protumor response gene interleukin 5 (IL-5) associated with antitumor immunity, and immune scores were analyzed. The results suggested that AIM2 expression was significantly correlated with IFNG , GZMB , and immune score; the association was higher in the high expression group than in the low expression group. Although IL-5 and AIM2 showed no correlation, the low expression group showed a high association, suggesting that AIM2 exerted antitumor immune effects (Fig. 3 (G-H), Fig. 4 (A-C), Supplementary Table 4). To fully elucidate the effects of AIM2 expression in immune cells, the CIBERSORT, TIMER, MCPCOUNTER, QUANTISEQ, xCell, and ssGSEA algorithms were used to analyze the differences in the abundance of various immune cell infiltrates between patients with low or high levels of AIM2 . The number of several immune cells was significantly elevated in patients with high AIM2 expression (Fig. 3 (A-F)), indicating that it promotes the initiation of immune responses. To further confirm the association of antitumor immunity and AIM2 expression, we analyzed the correlation between AIM2 expression and immune cell infiltration in 15 SKCM datasets in the GEO database and the TCGA-SKCM dataset was analyzed based on 8 algorithms. AIM2 expression was found to increase and the infiltration levels of antitumor immunity cells (natural killer (NK) cells, T cells, gamma delta T cells, dendritic cells (DCs), activated CD4 + T cells, B cells, M1 macrophages, effector memory CD8 + T cells, and activated CD8 + T cells) increased. In particular, the infiltration levels of B and CD8 + T cells significantly increased, whereas those of tumor-promoting immune cells (regulatory cells, cancer-associated fibroblasts, and M2 macrophages) did not change or partially decreased (Fig. 3 (F)). To further elucidate the AIM2 distribution in SKCM tissue samples, the expression data of six SKCM single cells in the TISCH database [ 39 ] were used to explore the AIM2 expression in different SKCM cell types (including cancer and noncancer cells). The results showed that among the six SKCM single-cell datasets analyzed, proliferating T and B cells showed the highest expression of AIM2 (Fig. 4 (E)). These results are consistent with previous findings that AIM2 is closely related to T cell activity. The analysis further confirmed the antitumor functions of AIM2 in TME. Subsequently, based on the TCGA-SKCM and GSE65904 datasets, we analyzed the correlation and differences between immune cell marker genes. Simultaneously, based on the 15 SKCM datasets in the GEO database and TCGA-SKCM datasets, AIM2 expression and the expression of chemokines, chemokine receptors, and MHC molecules were discussed in the context of the correlation between molecular and immune activator expression levels. AIM2 was found to be positively correlated with most antitumor immune cell marker genes, and was significantly expressed in the group with high levels of AIM2 expression (Fig. 4 (A-C), Supplementary Table 4). In contrast, as shown in 15 SKCM and TCGA-SKCM datasets in the GEO database, AIM2 expression was positively correlated with MHC molecules, suggesting the upregulation of antigen presentation and processing. The key chemokines inducing CD8 + T cells ( CXCL9 and CXCL10 ) were positively correlated with AIM2 expression in 15 SKCM and TCGA-SKCM datasets. Other chemokines ( CXCL13,CCL8, CCL20, CCL2,CCL5,CCL4,CCL3,CCL25,CCL19,CCL21, CCL22, XCL1, XCL2,CXCL11,CXCL1,CXCL8,CCL7,CXCL16,CXCL2 ,and CXCL5 ) and receptors ( CXCR6, CCR2, CXCR3, CCR7,CCR1,CCR5,CXCR5,XCR1,CCR4 , and CCR6 ) showed positive correlations with AIM2 (Fig. 4 (D)). These chemokines and receptors promote the recruitment of effector tumor infiltrating immune cells, such as CD8 + T, B, and antigen-presenting cells. AIM2 stimulates CD8 + T-cell infiltration by activating the PANoptosis pathway The recruitment of activated CD8 + T cells is the focus of tumor immunotherapy. The TCGA-SKCM dataset was analyzed based on the TIMER 2.0 online database, the GASElite online database[ 40 ], and ssGSEA. The findings suggested that AIM2 expression was positively correlated with CD8 + T-cell infiltration (Fig. 5 (C-E)). Subsequently, the same was confirmed on analysis of 15 SKCM datasets in the GEO database based on the TIMER algorithm (Fig. 5 (I)). Patients with high AIM2 expression levels showed significantly higher expression levels of T cell signature genes than those exhibiting low expression levels (Fig. 5 (H)). We also found that AIM2 expression was positively correlated with T cell signature genes (Fig. 5 (F-G)). GSEA indicated that AIM2 expression was positively correlated (FDR = 0.01, NES = 1.949; FDR = 0.01, NES = 2.232; Fig. 5 (A-B)) with T-cell infiltration. We performed GSEA to evaluate AIM2 -related molecular mechanisms that affected T cell infiltration. AIM2 expression was associated with signaling transduction in cell pyroptosis, apoptosis, and necrosis. Recent studies revealed that AIM2 regulates Z-DNA binding protein 1 (ZBP1), an intrinsic immune response sensor, resulting in the formation of ZBP1 PANoptosome and thereby activating the PANoptosis pathway. This initiates PANoptosis inflammatory cell necrosis, promotes downstream apoptotic pyroptosis, and leads to the activation of necrotic effector molecules, causing inflammatory cell necrosis and cytokine release, eventually promoting T-cell infiltration in TME[ 37 , 41 ]. This study revealed that patients with high AIM2 expression showed a significantly enriched number of genes related to apoptosis, pyroptosis, and cell death signaling pathways downstream of the PANoptosis pathway (Fig. 6 (A)). Thus, AIM2 was positively correlated with apoptosis, pyroptosis, and cell death signaling pathways. Consistent with recent studies and functional enrichment analysis, we speculate that AIM2 initiates PANoptosis by binding to MEFV (encoding pyrin protein) and ZBP1 to promote apoptosis, pyroptosis, and necrosis. To evaluate our hypothesis, we used SKCM data in the TCGA and GSE65904 datasets that explored the relationship between AIM2 , the core genes ( MEFV and ZBP1 ) and their related genes ( RIPK3 , RIPK1 , caspase-6 , Caspase-8 , NLRP3 , and Caspase-1 ) in the ZBP1 PANoptosome. The expression levels of all genes, except RIPK1 , were positively correlated with AIM2 expression in both the TCGA-SKCM and GSE65904 datasets, showing high expression levels in patients with high AIM2 expression (Fig. 6 (B-E). Among these, the correlation between the core genes ( MEFV and ZBP1 ) and AIM2 was found to be stronger. Under conditions of low AIM2 expression, we subsequently investigated the expression of six important genes including core genes ( MEFV and ZBP 1), pyroptosis, apoptosis, necrosis, and CD8 + T cell marker genes ( GSDMD , MLKL , CASP-8 , CD8A ) in the AIM2 PANoptosis complex based on the siRNA interference method. The expression of the core genes ( MEFV and ZBP1 ), pyroptosis, apoptosis, necrosis, and CD8 + T cell marker genes ( GSDMD , CASP-8 , and CD8A ) in the AIM2 PANoptosis complex decreased under conditions of low AIM2 expression (Fig. 6 (F)), indicating that the PANoptosome is activated by abnormally high expression levels of AIM2 . To further understand the effects of the PANoptosome on TME, the correlation between 13 PANoptosome signature genes and levels of immune cell infiltration in TME were investigated using TIMER and MCPCOUNTER algorithms and CD4 + T cells, B cells, and NK cells (Fig. 6 (G-H)). The results suggested that PANoptosome remodels the immune activation of the TME and inhibits tumor immune escape. To verify the abovementioned conclusions, we analyzed the correlation between PANoptosome signature genes, T cell signature genes, and MHC molecules, and found that PANoptosome signature genes were positively correlated with T cell signature genes and MHC molecules (Fig. 6 (I-J)). AIM2 improves response to ICBs AIM2 was found to be related to antitumor immunity and could also be related to immunotherapeutic responses. Thus, we evaluated the effects of AIM2 expression on immunotherapeutic responses. Solid tumors have been reported to be classified into two types based on immunotherapy sensitivity; hot and cold tumors, with hot tumors being responsive to ICBs. Therefore, we used hot tumor signature genes ( CCL5, CD8A, PDCD1, CD8B, CXCR3, CXCL9, CXCL10, CD4, CD3E, CXCL11, CD274 and CXCR4 ) in this study[ 42 ]. An unsupervised clustering method was used to classify patients with SKCM in the TCGA database as having immuno-cold or immuno-thermal tumors. Subsequently, the GSE65904 dataset was divided into two clusters (Supplementary Fig. 2 (A-C)). The differences in AIM2 expression and prognoses between the two clusters suggested that AIM2 was significantly overexpressed in hot tumors and was significantly associated with improved prognoses (Supplementary Fig. 2(D)), indicating its correlation with ICB responses. To further investigate the therapeutic value of AIM2 , we analyzed the correlational and differential expressions of AIM2 with eight predictors of immunotherapy response[ 43 ], including SIGLEC15 [ 44 ], IDO1, CD274 , HAVCR2 , PDCD1 , CTLA4 , LAG3 , and PDCD1LG2 . AIM2 expression was strongly correlated with these molecules and the correlation coefficients ranged between 0.52 and 0.60 (P < 0.01; (Fig. 7 (A)). These molecules were found to be highly expressed in patients with high AIM2 expression levels (Fig. 7 (B)). Therefore, these results strongly suggested that AIM2 promotes tumor therapeutic effects. To validate the efficacy of AIM2 in SKCM, the melanoma immunotherapy Dizier cohort 2013 and Wolf cohort 2021 analyses were performed. AIM2 expression in ICB treatment response patients was significantly higher than that in ICB treatment nonresponsive patients (Fig. 7 (G)). Thus, AIM2 can enhance the therapeutic effects of SKCM ICBs. Based on the TIDE online database analysis, the survival of patients with high AIM2 expression levels was significantly prolonged after ICI treatment, and AIM2 expression was positively correlated with T cell infiltration (Fig. 7 (C)). AIM2 as a predictive marker for ICB in SKCM TMB can predict immunotherapy efficacy. AIM2 was found to be positively correlated with TMB (Fig. 7 (D)), suggesting that AIM2 was involved in immunotherapeutic responses and could predict immunotherapeutic efficacy. Based on 15 melanoma tumor cohorts, the area under the curve (AUC) of receiver operating characteristic (ROC) for AIM2 expression with 13 treatment cohorts except VanAllen cohort 2015 (Anti-CTLA-4) (0.496) and Van cohort 2021 (Anti-PD-L1) (0.495) greater than 0.5, among which the AUC value in the Gao cohort 2018 (Anti − PD − 1/CTLA − 4) cohort was greater than 0.931 (Fig. 7(E)), strongly suggesting that AIM2 is a predictor of the effect of SKCM immunotherapy.Based on TIDE, we compared the area under the curve (AUC) of receiver operating characteristic (ROC) for AIM2 expression with existing biomarkers (TMB, CD274, and PD-L1), and MSI. The score was used as a tool to predict immunotherapy responses. In the seven SKCM cohorts, AIM2 showed comparable predictive performances with CD274, much higher relative to TMB and MSI. AUC > 0.7 was obtained in all three SKCM cohorts, suggesting the efficiency in the prediction of a strong positive immunotherapeutic response. Notably, AIM2 was the only biomarker showing a positive performance for predicting ICB responses in the Riaz_2017 cohort (AUC = 0.82)(Supple Table 5). We also found that the tumor stemness index of patients with SKCM and high AIM2 expression levels was lower than that of patients with low AIM2 expression levels (Fig. 7 (F)). To further study the effects of AIM2 on SKCM treatment response, we performed a correlational analysis between AIM2 expression level and drug sensitivity in GEO15 SKCM and TCGA-SKCM datasets based on the GDSC database. The sensitivity of most drugs was negatively correlated. These data suggested that AIM2 can act as a biomarker for predicting drug treatment (Fig. 7 (H)). Construction and testing of AIM2-related gene risk signature Based on TCGA-SKCM and GSE65904 datasets, analyses revealed that 15 genes ( GBP2 ) were strongly positively correlated with AIM2 (correlation coefficients > 0.6) (Fig. 8(B-D)). Thus, they may have some common features or functions. Univariate Cox regression analyses showed that AIM2 and 15 genes were strongly correlated and included in LASSO regression analyses (Fig. 8 (A)). Lasso regression analyses were performed on these 16 genes based on the TCGA-SKCM dataset, and six genes were identified for constructing the risk signature (lambda.min = 0.0395). Risk score = (− 0.0013)*BIRC3 + (− 0.0048)*SAMSN1 + (− 0.0982)*APOBEC3G + (− 0.1376)*GBP2 + (− 0.0316)*GBP5 + (− 0.0044)*AIM2. As shown in the above equation, the risk score was calculated by multiplying the sum of the expression levels of each risk signature gene with the coefficient of each risk signature gene. Patients with SKCM in the TCGA cohort were divided into high- and low-risk groups according to the median risk score. Scatterplots and heatmaps indicate the survival of patients with risk scores and the expression of six risk signature genes in high- and low-risk groups. It suggests that the death rate of samples with high risk scores is significantly higher than that of samples with low risk scores, and the prognosis of samples with high risk scores is poor. The AUC value of the ROC curve was used to evaluate the ability of the prognostic prediction of the model, and the results found that the AUC values ​​at 2, 4, 6, and 8 years were 0.703, 0.686, 0.696, and 0.735, respectively. Based on the risk score, the Kaplan–Meier analysis revealed a significant difference between the groups (p < 0.0001), and the high-risk group showed a poorer prognosis (Fig. 8 (E)). These results were verified using the GSE65904 dataset (Fig. 8(G)). In conclusion, our prognostic risk signature showed robustness and can play a stable role in prognostic prediction for different cohorts. To explore the different prognostic differences between the high- and low-risk groups, the correlation between the risk score and immune cell infiltration level was analyzed based on the TMER and EPIC algorithms. The results showed that the risk score was negatively correlated with the infiltration levels of antitumor B cells (Supplementary Fig. 5 (A-B)). Compared with the high-risk group, the low-risk group had an increased number of various antitumor immune cells with significantly improved prognosis. To better understand whether risk score is a clinically independent prognostic factor in patients with SKCM, univariate and multivariate COX regression analyses were performed integrating risk score and patient clinical characteristics. Univariate cox regression analysis results found that risk score, age, T stage, N stage, M stage, and stage were associated with OS prognosis, whereas multivariate cox regression analyses showed that risk score, age, T stage, N stage, and M stage were correlated with OS prognosis. Therefore, risk score, age, T stage, N stage, and M stage may be independent prognostic factors. Multivariate analyses revealed that low-risk scores were significantly associated with favorable OS rates even after adjusting for other clinical characteristics. In conclusion, AIM2 and its five related gene signatures can be used as independent risk factors for predicting the prognosis of patients with SKCM. Subsequently, we constructed a nomogram model employing clinical factors to predict mortality in patients with SKCM by incorporating independent prognostic factors (Fig. 8 (F)). By calculating the scores of the abovementioned variables for each patient, we could predict the 2-, 4-, 6-, and 8-year OS rates for the patients. The higher the total score, the worse the patient prognosis. The C-index of the nomogram was 0.716 (95% confidence interval: 0.676–0.755), indicating good discriminative ability. Furthermore, the calibration curve lithogram confirmed that the constructed nomogram model could accurately predict performance. Discussion Cancer therapy is mainly based on the induction of cell death, and the two major modes of cell death (pyroptosis and PANoptosis) greatly affect the TME. Therefore, we investigated the relationship between AIM2, an important inducer of pyroptosis and PANoptosis, and tumor immune features to determine the prognostic value of AIM2 in SKCM. We found that AIM2 is considered an important tumor suppressor. Further analysis showed that AIM2 is may correlated with immune infiltration and PANoptosis. Furthermore, AIM2 expression is associated with increased cytotoxic T-cell infiltration and responses to ICB treatment in patients with SKCM. Mechanistically, AIM2 may increase the efficacy of immunotherapy by activating the PANoptosis pathway, which initiates PANoptosis inflammatory cell apoptosis and promotes CD8 + T-cell infiltration. Furthermore, a prognostic risk model with a 6-gene signature was constructed on the basis of AIM2 and its 15 strongly correlated genes, and its predictive ability was determined. Patients with low-risk and high-risk signatures exhibited significantly different prognoses and TME. According to the results of multivariate COX regression analysis of risk factors, we established a prognostic nomogram model, which further improved the prediction performance. This prognostic model can be used for the prognostic stratification of patients with SKCM, which can help us to better understand the molecular mechanism underlying SKCM and provide new strategies for targeted therapy. In this study, AIM2 was mainly involved in several tumor-immune-related and inflammatory responses, along with cellular signaling pathways in apoptosis and necrosis, including B-cell receptor, toll-like receptor, nod-like receptor, T-cell receptor transduction cascades, complement activation, chemokines, cytokines, inflammatory responses, and antigen processing and presentation. Furthermore, the association of AIM2 with inflammatory cell necrosis in PANoptosome was verified. The interferon regulatory factor 1-dependent activation of PANoptosis prevents azoxymethane /dextran sodium sulfate-induced colorectal tumorigenesis in a mouse model of colorectal cancer, indicating a wider scope of PANoptosis in cancer[ 45 ]. In this study, the relationship between AIM2 and PANoptosis was confirmed, which provided mechanistic evidence for the functions of AIM2 in cancer. AIM2 expression increases the levels of immune infiltration of anti-tumor immune cells (such as DC) in the TME. This is consistent with the results of previous studies[ 46 , 47 ]. The underlying mechanism is possibly by increasing the infiltration levels of CD8 + T cells by activating the PANoptosis signaling pathway. Recently, cell pyroptosis has been shown to promote the infiltration of T-cells in the TME[ 48 ]. We found a significant positive correlation between AIM2 expression and the downstream PANoptosis effectors, cellular pyroptosis, apoptosis, and necrosis pathways, indicating the mechanism underlying AIM2 -mediated CD8 + T cell infiltration. Furthermore, the expression of eight immune checkpoint genes also exhibited an upregulated trend with high AIM2 expression compared with that in the low-expression cohort and showed a positive association with AIM2 expression. Together, these results indicated that the administration of ICB to patients with high AIM2 expression decreased the immunosuppressive state induced by PD-L1 expression, making the outcomes of immunotherapy more favorable. Based on this hypothesis, we detected AIM2 expression in two SKCM immunotherapy cohorts and used the TIDE algorithm to predict the response and prognosis of ICB in patients with different AIM2 expression levels. The results showed that AIM2 expression was higher in ICB treatment-responsive patients than in ICB-treatment non-responsive patients. Furthermore, patients with high expression of AIM2 had prolonged survival after ICB treatment, which confirmed our hypothesis. Folic acid-transplanted PEI600-CyD (h1) nanoparticle-mediated DNA vaccine can act as a vaccine adjuvant to increase the activity of tumor-specific CD8 + T cells by triggering AIM2 expression, thereby promoting antitumor therapeutic efficacy[ 49 ]. Based on the findings of this study, it was reasonably hypothesized that the response to ICB treatment due to AIM2 in SKCM is facilitated by the following two factors: AIM2 promotes the activation and infiltration of effector T cells. It also may induces tumor cell pyroptosis, apoptosis, and necrosis by activating PANoptosis. As predictive biomarkers of immunotherapeutic responses, AIM2 expression is positively associated with TMB. The analysis of two SKCM ICB treatment cohorts found that the AUC values of AIM2 ​​in the two SKCM ICB treatment cohorts were > 0.7, indicating its putative utility as a marker for predicting immunotherapeutic responses. Through the TIDE analysis, it was found that the AUC values of AIM2 in three of the seven SKCM-ICB treatment cohorts were > 0.7, which strongly indicated that AIM2 can be used as a marker to predict responses to ICB in SKCM. Cancer stem cells (CSCs) are self-renewing cells that promote tumorigenesis, progression, and metastasis[ 50 ]. Previous studies have shown that there is a significant association between cancer stemness and cancer immune evasion and drug resistance[ 51 ]. Recent studies have shown that ICB therapy is less effective in patients with high stemness[ 52 ]. Analyzing the differences in tumor stemness between patients with high and low expression of AIM2 showed that the tumor stemness index of patients with high AIM2 expression was lower than that of patients with low AIM2 expression. These results revealed that patients with low AIM2 expression have cancers with increased invasive ability, poor prognosis, and promoted tumor immune escape. Furthermore, the ICB treatment effect is poor due to the risk of drug resistance. Based on this hypothesis, we performed in vitro experiments to investigate the invasive ability of the A875 cell line with different AIM2 expression levels and analyzed the correlation between AIM2 expression levels and drug sensitivity using the GDSC database. The invasive ability of the A875 cell line was significantly increased, and the low expression of AIM2 in SKCM was positively correlated with the sensitivity of most drugs. The analysis of immune infiltration and ICB treatment response in patients with high and low AIM2 expression showed that patients with high AIM2 expression had increased levels of anti-tumor immune cell infiltration, inhibited tumor immune escape, and significantly higher responsiveness to ICB treatment compared with those in patients with low AIM2 expression. Patient survival was prolonged after treatment, which confirmed our hypothesis. On the basis of the key role of AIM2 in copper cell apoptosis, we constructed a risk prognostic model by selecting genes that are strongly associated with AIM2 by performing LASSO regression and univariate Cox analyses. The prognosis and TME of patients with high and low-risk scores were significantly different. Univariate and multivariate Cox regression analyses showed that the risk score was an independent prognostic factor in patients with SKCM. Age, TNM stages, and risk score were independent prognostic factors in patients with SKCM. To further investigate the efficacy of the prognostic risk model in predicting prognosis, we constructed a prognostic nomogram model based on the age and TNM stages of the patients and risk score to further evaluate the predictive efficacy of each patient with SKCM. The C value of the nomogram model is 0.716 (95% confidence interval [CI]: 0.676–0.755), which reveals that the prognostic nomogram model has a good predictive effect. Therefore, the risk score can be used as a predictor of the clinical treatment and prognosis of SKCM, which is of immense importance for understanding the molecular mechanism underlying SKCM and can provide new strategies for targeted therapy. The present study has some limitations. This study was primarily based on the analysis of multiple bioinformatic tools; therefore, subsequent experiments should validate these findings. However, we used two or more methods and relevant literature data for validating the results. For instance, to verify the correlation between AIM2 and the TME, various methods, including GO annotation, KEGG functional enrichment analysis, GSEA, CIBERSORT, xCell, ssGSEA algorithms, TIMER2.0, GASElite, TIDE online database, and correlation-based investigation of cold and hot tumors, were utilized. This improved the reliability of the results. Furthermore, although it was found that the combined application of AIM2 -based expression and clinical alignment diagrams improved the prognostic prediction of patients with SKCM, the findings could not be validated using other datasets owing to the paucity of cohorts with complete clinical data. However, the analysis of the ICB-treated melanoma cohort on the basis of the TIDE online database confirmed the ability of AIM2 expression to function as an independent predictor of survival. To summarize, AIM2 is an important protective factor of SKCM. Furthermore, the prognostic model for SKCM had favorable predictive efficacy. AIM2 can increase immunotherapeutic efficacy by may activating PANoptosis signaling and promoting CD8 + T-cell infiltration. These findings are expected to provide valuable insights into the development of cytokine-based tumor immunotherapy. Declarations Ethics Statement This study was approved by the Ethics Committee of the Second Affiliated Hospital of Lanzhou University.Patients/participants provided their written informed consent to participate in this study. Conflicts of Interest The authors declare that they do not have any conflicts of interest. Funding declaration No funding Author contributions This replaces any statement written within the manuscript and is the one that we will publish. Sheng Yong Long designed this study，Jing Xu oversaw it. Sheng Yong Long was analyzed data, Yu Mao Wang and Wan Qian Chen examined statistical methods.The manuscript was written by Sheng Yong Long. All authors in this study contributed to the article and approved the submitted version, all authors reviewed the manuscript. 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Correlation analysis of AIM2 differential expression between immune cold and hot tumors, and prognosis analysis.(A-D) Unsupervised clustering method based on characteristic genes of hot tumors and differential expression and prognosis of AIM2. Supplefigure2..tif Supple Figure 2. Correlation of risk factors with immune infiltrating cells. (A) Bar graph showing the correlation of risk factors with the level of immune cell infiltration. (B) Correlation of risk factors with immune score, matrix score, and Estimate score. (C) Differences in immune score, matrix score, and Estimate score between high and low risk factor groups. (D) Differences in the infiltration of 24 immune cells in the high and low risk factor groups. (E) Association of risk factors with immune cell marker genes. (F) Association of risk factors with 8 immune checkpoints. SuppleFigure3.tif SuppleFigure4.tif SuppileFigure5.tif SuppleFigure6.tif suppletable1.docx SuppleTable2.docx suppletable3.docx SuppleTable4.docx suppleTable5.docx RTqPCR.xlsx RTqPCROperationReport.docx flowchart..pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3899213","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280586320,"identity":"644de4de-a719-4836-bae7-718e9d2adafd","order_by":0,"name":"Yong Sheng Long","email":"","orcid":"","institution":"Tongren People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"Sheng","lastName":"Long","suffix":""},{"id":280586321,"identity":"62cfeadc-60b9-4ad8-ae2d-e712d4258982","order_by":1,"name":"Jing Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACfobjhx//qZBgtj/eQKQWycYzaQY8ZyzYGc4cIFKLQfMBAwnetgp+hhsJxGphO5BgIMEmIc048/HGGww1NtEEtZjzHDzwwIBHwphZOq3YguFYWm4DIS2WM4C2JEhIJLNJ55hJMDYcJqzF4P4DAwmgd+p7JM8Qq+XAAQPJhgQJZgkJHiK1SDacSTNmOCDBbMAD9EsCMX4BRyXjvzpmA/bDG298qLEhrAXFkRIJpCiHaCFVxygYBaNgFIwMAADoUT7BbniJmgAAAABJRU5ErkJggg==","orcid":"","institution":"Tongren People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Xu","suffix":""},{"id":280586322,"identity":"2565f48c-62a3-41ea-8ac8-aea81cced2ac","order_by":2,"name":"Yu Mao Wang","email":"","orcid":"","institution":"Tongren People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"Mao","lastName":"Wang","suffix":""},{"id":280586323,"identity":"6652d18a-1364-44d4-9b6a-b3d70d75be7c","order_by":3,"name":"Wan Qian Chen","email":"","orcid":"","institution":"Tongren People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wan","middleName":"Qian","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-01-26 06:44:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3899213/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3899213/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53033198,"identity":"69328560-cfa9-4d5f-8aab-3a34df943064","added_by":"auto","created_at":"2024-03-19 20:21:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":328282,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression level of AIM2 and its relationship with clinicopathology. (A) AIM2 mRNA expression in normal tissues. (B-C) AIM2 AIM2 mRNA expression in pan-cancer. (D) Differential expression of AIM2 mRNA between normal tissue and skin melanoma in GSE46517 and GSE15605 datasets. (E) Differential expression of AIM2 mRNA between normal tissues and skin melanomas in the TCGA database. (F) AIM2 protein expression in normal tissue and skin melanoma from HPA database. (G) Immunohistochemical staining of AIM2 in a patient with cutaneous melanoma, representative images are show. (H) Dot plots depict the mean and standard deviation of AIM2 protein expression in cutaneous melanoma patient tissues and in adjacent normal skin tissues. (I) Correlation between AIM2 mRNA expression levels and meaningful various clinical case characteristics in cutaneous melanoma.\u003c/p\u003e","description":"","filename":"OnlineFigure1..png","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/2efb3e2ca7a9d737966276dd.png"},{"id":53033201,"identity":"c64f48ce-b850-46c6-85b6-d2b8302b248d","added_by":"auto","created_at":"2024-03-19 20:21:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1335735,"visible":true,"origin":"","legend":"\u003cp\u003eAIM2 prognosis analysis and functional analysis in SKCM. (A-B) Kaplan-Meier survival analysis Survival curves of OS and DSS of AIM2 in the SKCM dataset in the TCGA database. (C) Kaplan-Meier survival analysis of the OS survival curves of AIM2 in the GSE65904 dataset. (D) Transwell detected the changes of AIM2 knockdown on the invasive ability of A875 cells.\u003c/p\u003e","description":"","filename":"OnlineFigure2..png","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/21d7228308441d563cf7fd8f.png"},{"id":53033200,"identity":"f98e10d6-2fd8-4814-b539-d7bf79004608","added_by":"auto","created_at":"2024-03-19 20:21:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":456567,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional annotation of AIM2 in skin melanoma (A-B) GO and KEGG analysis of the top 300 genes positively correlated with AIM2; (C-E) GSEA enrichment analysis; (F) GO and KEGG analysis of differential genes between AIM2 high and low expression groups KEGG analysis.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/16c74301295b37f0721fd2aa.png"},{"id":53033211,"identity":"2f56e2f4-6247-4e82-8482-e6399c58692c","added_by":"auto","created_at":"2024-03-19 20:21:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":359128,"visible":true,"origin":"","legend":"\u003cp\u003eImmune correlation analysis of AIM2 in cutaneous melanoma. (A-F) Histograms show the differential infiltration levels of immune cells between high and low AIM2 expression groups analyzed by CIBERSORT, TIMER, MCPCOUNTER, QUANTISEQ , ssGSEA and xCell algorithms. (G) ESTIMATE algorithm was used to analyze the difference of matrix score, immune score and ESTIMATE score with AIM2 expression and between high and low AIM2 expression groups. (H) heat map showing the correlation between AIM2 expression and various immune cell infiltration levels in 8 algorithms\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/d565554a8b83e571d0500a1f.png"},{"id":53033543,"identity":"3e1744a6-ae93-4e44-b37d-93a8af171c7f","added_by":"auto","created_at":"2024-03-19 20:29:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":509187,"visible":true,"origin":"","legend":"\u003cp\u003eImmune correlation analysis of AIM2 in cutaneous melanoma. (A-B) Heat map showing the correlation of AIM2 expression with immune cell marker gene expression in GSE65904 and TCGA SKCM datasets. (C) Differential expression of immune marker genes between high and low AIM2 expression groups in TCGA SKCM datasets.（D）The heat map shows the correlation between the expression of Antigen presentation Immunoinhibitor Immunostimulator Chemokine Receptor and AIM2 in 15 SKCM datasets in the GEO database and in the TCGA SKCM dataset.（E AIM2 expression in 6 SKCM single-cell expression datasets in the TISCH database.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/bba29f2487235e036806c94b.png"},{"id":53033219,"identity":"d01dc0fa-c925-4346-8c9a-4c096b6c22e1","added_by":"auto","created_at":"2024-03-19 20:21:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":348487,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of AIM2 expression with CD8+ T cell infiltration level and T cell signature genes. (A-B) AIM2 signaling pathway with T cell infiltration (C) Correlation of AIM2 expression and CD8+ T cell infiltration level by CASElite database analysis.(D)ssGSEA algorithm to analyze the correlation between AIM2 expression and CD8+ T cell infiltration level. (E) TIMER2.0 database analysis of the correlation between AIM2 expression and CD8+ T cell infiltration level. (F-G) Heat map showing the correlation between AIM2 expression and T cell signature gene expression in GSE65904 and TCGA SKCM datasets; (H) The expression difference of T cell signature genes between high and low AIM2 expression groups. (I) The correlation between AIM2 expression and CD8+ T cell infiltration was analyzed for 15 SKCM datasets in the GEO database based on the TMER algorithm.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/a8a9008a54528e8f043b4f94.png"},{"id":53033213,"identity":"7985aab6-1263-4a9a-84ca-4f7f468a700e","added_by":"auto","created_at":"2024-03-19 20:21:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":403716,"visible":true,"origin":"","legend":"\u003cp\u003eAIM2 is involved in the regulation of PANoptosis process and the relationship between PANoptosis characteristic genes and tumor microenvironment. (A) GSEA analysis showed that AIM2 expression was positively correlated with pyroptosis, apoptosis, and necrosis signaling pathways. (B-D) Correlation of AIM2 with 13 PANoptosis signature genes. (E) Differential expression of 13 PANoptosis signature genes between AIM2 high and low expression groups. (F-G) TMER and MCPCOUNTER algorithms were used to analyze the correlation between 13 PANoptosis signature genes and immune cell infiltration levels. (H-J) Correlation of 13 PANoptosis signature genes with T cell signature genes, antigen presentation and processing signature genes, and MHC molecules.\u003c/p\u003e","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/c8d86fe6f807a979ed7b504d.png"},{"id":53033202,"identity":"862b3e1b-d060-455c-9602-309e6f2efef1","added_by":"auto","created_at":"2024-03-19 20:21:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":373987,"visible":true,"origin":"","legend":"\u003cp\u003eAIM2 prognosis and response in a cutaneous melanoma immunotherapy cohort. (A-B) Correlation and differential expression of AIM2 with 8 predictors of immunotherapy response. (C) Impact of AIM2 expression on prognosis and CD8+ T cell infiltration in the cutaneous melanoma ICB cohort. (D) Correlation of AIM2 expression with TMB and MSI. (E) AIM2 AUC values ​​in 15 melanoma immunotherapy cohorts. (F) Differential expression of tumor stemness index in high and low AIM2 expression groups and normal tissues. (G) Bar graph showing the difference in AIM2 expression between immune responders and non-responders in Melanoma Immunotherapy Cohort Dizier cohort 2013 and Wolf cohort 2021. \u0026nbsp;(H) Analysis of the correlation between AIM2 expression level and drug sensitivity based on GEO 15 SKCM datasets and TCGA SKCM datasets based on GDSC database.\u003c/p\u003e","description":"","filename":"OnlineFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/78e5058dd821e5d054791c96.png"},{"id":62880114,"identity":"1f7543ad-52c0-4af1-b0f1-9a4807c5b4cd","added_by":"auto","created_at":"2024-08-20 14:37:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6653095,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/fdb21dfe-4198-4cd9-8b5b-808d0798636b.pdf"},{"id":53033205,"identity":"77614a40-e5a2-4daa-a474-d59b348982bf","added_by":"auto","created_at":"2024-03-19 20:21:15","extension":"tiff","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1759490,"visible":true,"origin":"","legend":"\u003cp\u003eSupple Figure 1. Correlation analysis of AIM2 differential expression between immune cold and hot tumors, and prognosis analysis.(A-D) Unsupervised clustering method based on characteristic genes of hot tumors and differential expression and prognosis of AIM2.\u003c/p\u003e","description":"","filename":"SuppleFigure1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/4810178e2502296ba9caffee.tiff"},{"id":53033540,"identity":"5aacf2ec-6d3e-4135-81f3-1e701e46d20a","added_by":"auto","created_at":"2024-03-19 20:29:15","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3392912,"visible":true,"origin":"","legend":"\u003cp\u003eSupple Figure 2. Correlation of risk factors with immune infiltrating cells. (A) Bar graph showing the correlation of risk factors with the level of immune cell infiltration. (B) Correlation of risk factors with immune score, matrix score, and Estimate score. (C) Differences in immune score, matrix score, and Estimate score between high and low risk factor groups. (D) Differences in the infiltration of 24 immune cells in the high and low risk factor groups. (E) Association of risk factors with immune cell marker genes. (F) Association of risk factors with 8 immune checkpoints.\u003c/p\u003e","description":"","filename":"Supplefigure2..tif","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/f7dc396f6a2e05071f0a632c.tif"},{"id":53033542,"identity":"d8287554-c866-4868-a947-614301b4c8b1","added_by":"auto","created_at":"2024-03-19 20:29:15","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":5264444,"visible":true,"origin":"","legend":"","description":"","filename":"SuppleFigure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/4303bb591ba3f1a49d19a118.tif"},{"id":53033203,"identity":"30741520-5abc-4ef4-b0c7-9aa8b2930a3d","added_by":"auto","created_at":"2024-03-19 20:21:15","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":11317064,"visible":true,"origin":"","legend":"","description":"","filename":"SuppleFigure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/57fbaf85a7ef53321ae3ea97.tif"},{"id":53033215,"identity":"d50ef220-10e7-408b-b13c-73b633fb20e2","added_by":"auto","created_at":"2024-03-19 20:21:16","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":6320780,"visible":true,"origin":"","legend":"","description":"","filename":"SuppileFigure5.tif","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/7fb5a6d8005b941c63f119de.tif"},{"id":53033546,"identity":"3a417729-876a-46d6-8fd5-5207ce236b6d","added_by":"auto","created_at":"2024-03-19 20:29:16","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":6287296,"visible":true,"origin":"","legend":"","description":"","filename":"SuppleFigure6.tif","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/592973a48bc29caf5a1e0f17.tif"},{"id":53033207,"identity":"ee4fe381-5e01-4e05-a4c5-cd90c1b4d4af","added_by":"auto","created_at":"2024-03-19 20:21:15","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":13091,"visible":true,"origin":"","legend":"","description":"","filename":"suppletable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/e26d4603ea3956348f461fb0.docx"},{"id":53033216,"identity":"2e968e0d-c259-4d8e-81c0-f49453c1483a","added_by":"auto","created_at":"2024-03-19 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20:21:16","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":16448,"visible":true,"origin":"","legend":"","description":"","filename":"SuppleTable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/2ffd409cb53ae979dc078e9d.docx"},{"id":53033206,"identity":"baf0c4bb-b3bd-4c2e-b6db-a9b57dfc8b20","added_by":"auto","created_at":"2024-03-19 20:21:15","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":16579,"visible":true,"origin":"","legend":"","description":"","filename":"suppleTable5.docx","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/5717d782bee107d947371def.docx"},{"id":53033220,"identity":"114c6bd6-e168-4f65-b5c5-472b633dd76a","added_by":"auto","created_at":"2024-03-19 20:21:16","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":3543331,"visible":true,"origin":"","legend":"","description":"","filename":"RTqPCR.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/d8f6e250f2e5ed37bb143b59.xlsx"},{"id":53033212,"identity":"e6b5d404-7743-424a-8eae-9eae3f0222ed","added_by":"auto","created_at":"2024-03-19 20:21:15","extension":"docx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":21555,"visible":true,"origin":"","legend":"","description":"","filename":"RTqPCROperationReport.docx","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/b0e1c341488e8c616d6fd085.docx"},{"id":53033209,"identity":"9e992415-99f7-491a-9140-efb95333a2a9","added_by":"auto","created_at":"2024-03-19 20:21:15","extension":"pdf","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":200434,"visible":true,"origin":"","legend":"","description":"","filename":"flowchart..pdf","url":"https://assets-eu.researchsquare.com/files/rs-3899213/v1/7131fd8b71ce4a4f363af343.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic value and immune status of AIM2 in skin cutaneous melanoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSkin cutaneous melanoma (SKCM) is the most malignant and deadly among all skin tumors[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and its incidence and mortality rates are increasing[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although the prognoses of patients with advanced SKCM have improved in recent years owing to the developments in immunotherapy targeting immune checkpoints[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], only a few patients have benefitted from immunotherapy[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The lack of clinically validated predictive biomarkers is among the primary reasons for the inconsistent efficacy of current immunotherapy. Programmed death-ligand 1 (PD-L1) expression, tumor mutational burden (TMB), neoantigen load, mismatch repair, microsatellite instability, and various gene-specific biomarkers (such as mutations and tumor-infiltrating lymphocytes) have been successfully used to predict the effects of immune checkpoint inhibitor (ICI) therapy[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, these potential markers have certain limitations, such as spatial heterogeneity and temporal variation in the detection of PD-L1[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The TMB of Asian melanoma is very low and not yet sufficiently reliable to predict overall survival (OS)[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, effective biomarkers are urgently required to guide clinical immunotherapy.\u003c/p\u003e \u003cp\u003eAIM2 consists an N-terminal pyrin domain (PYD) and a C-terminal hematopoietic interferon-inducible nucleoprotein domain with a 200-amino acid repeat sequence (HIN200). HIN200 has a double-stranded deoxyribonucleotide (dsDNA) recognition ability[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] for binding to single-stranded DNA[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. PYD is a folded structure composed of six α-helices, which recruits and binds to the protein apoptosis-associated speck-like protein via highly specific PYD\u0026ndash;PYD interactions to complete the initial steps of inflammasome assembly[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. PYD is an important receptor of DNA in cells and an important pattern recognition receptor of inflammasomes. It plays an important role in processes such as pyroptosis, PANoptosis, and innate immune response[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. \u003cem\u003eAIM2\u003c/em\u003e plays a pleiotropic role in the tumor microenvironment (TME) as an important cellular regulator of innate immune responses[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. \u003cem\u003eAIM2\u003c/em\u003e is a pro-oncogenic factor in non-small-cell lung cancer, in breast cancer caused by human epidermal growth factor receptor 2+[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], in colon cancer, and in SKCM[\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A recent study on the dual role of \u003cem\u003eAIM2\u003c/em\u003e in TME reported that it inhibits the cyclic guanosine monophosphate\u0026ndash;adenosine monophosphate synthase\u0026ndash;stimulator of interferon pathway, thereby evading the adaptive immune responses[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The underlying regulatory roles of \u003cem\u003eAIM2\u003c/em\u003e in TME and its impact on immune checkpoint blockade (ICB) therapy, however, remain unknown.\u003c/p\u003e \u003cp\u003eA previous study reported the inhibitory effect of AIM2 overexpression on the progression of melanoma and the proliferation of mouse fibroblasts[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. There is no reported consensus on the mechanism underlying the function of \u003cem\u003eAIM2\u003c/em\u003e in SKCM; thus, its specific mechanism of action and TME regulation are unknown. Moreover, it is unclear whether \u003cem\u003eAIM2\u003c/em\u003e can be used as a biomarker for the prediction of prognoses and ICB treatment responses in patients with SKCM.\u003c/p\u003e \u003cp\u003eBased on the existing knowledge on the dual role of \u003cem\u003eAIM2\u003c/em\u003e in tumors, we aimed to determine the prognostic roles of \u003cem\u003eAIM2\u003c/em\u003e in SKCM along with its effects on the TME and ICB. The findings are expected to shed light on the multifaceted roles of \u003cem\u003eAIM2\u003c/em\u003e in tumors and may provide a theoretical basis for cytokine-based immunotherapy for patients with SKCM.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSKCM patients and ethics approval\u003c/h2\u003e \u003cp\u003eThis study included 13 patients diagnosed with SKCM from August 2020 to August 2021. Patients with SKCM were not treated with tumor-related treatment before resection and did not have other serious diseases during the same period. The procedures involved in this study were conducted with the understanding and knowledge of the participants, and the relevant written informed consent was obtained. This project strictly followed the Declaration of Helsinki (№ 2022A-066) and was approved by the Second Research Ethics Committee of Lanzhou University Hospital. and carried out according to the provisions of the Helsinki Declaration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eThe RNA-seq data of The Cancer Genome Atlas (TCGA) database and the corresponding clinical information of 33 patients with cancer were obtained from the Genomic Data Commons (GDC) data portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The normal tissue specimen data were obtained from the Genotype-Tissue Expression v8 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gtexportal.org/home/datasets\u003c/span\u003e\u003cspan address=\"https://gtexportal.org/home/datasets\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e A total of 15 SKCM datasets (GSE19293, GSE22153, GSE22154, GSE59455, GSE54467, GSE53118, GSE133713, GSE100797, GSE65904, GSE98394, GSE99898, GSE78220, GSE190113, GSE53118, and GSE19234) cohorts were obtained from Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to further validate \u003cem\u003eAIM2\u003c/em\u003eexpression and its survival analysis. The characteristics of the 15 datasets are detailed in supple table 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eImmunocorrelational analysis\u003c/h2\u003e \u003cp\u003eBased on six algorithms in the immunedeconv package[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] \u003cb\u003e(\u003c/b\u003eCIBERSORT[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], TIMER[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], MCPCOUNTER[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], QUANTISEQ[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], EPIC[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], xCell[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]), ssGSEA[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and ESTIMATE[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], differences in the infiltration abundance and immune scores of various immune cells between patients with high and low levels of \u003cem\u003eAIM2\u003c/em\u003e expression in the TCGA-SKCM dataset were analyzed. Subsequently, based on eight immune algorithms (CIBERSORT, TIMER, EPIC, xCell, MCPCOUNTER, QUANTISEQ, ESTIMATE, and CIBERSORT_ABS), the TCGA-SKCM dataset and 15 SKCM datasets in the GEO database were analyzed to examine the abundance of immune cell infiltration and immune scores in TME and SKCM correlation between \u003cem\u003eAIM2\u003c/em\u003e expression in patients.\u003c/p\u003e \u003cp\u003eWe also explored the correlation between \u003cem\u003eAIM2\u003c/em\u003e expression and CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration levels using the TIMER2.0 online database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and TIMER algorithm. The effect of \u003cem\u003eAIM2\u003c/em\u003e expression on the prognosis of the ICB-treated group was assessed using the TIDE online database༈Tumor Immune Dysfunction and Exclusion (TIDE) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/login/༉\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/login/༉\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Finally, we also explored the correlation between \u003cem\u003eAIM2\u003c/em\u003e expression and immune markers, immune checkpoints, major histocompatibility complex (MHC) molecules, chemokines, chemokine receptors, immune-activators, and T cell signature genes in different types of immune cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition of differentially expressed genes (DEGs) and analysis of functional enrichment\u003c/h2\u003e \u003cp\u003eIn this study, The limma R software package and the Student t test were used to monitor DEGs,The threshold values of \u0026ldquo;| Log\u003csub\u003e2\u003c/sub\u003e(FC) | \u0026gt;1 and adj p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026rdquo; were used for DEG screening,The P value of the t test was adjusted by the Benjamini-Hochberg method[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. To elucidate the putative biological role of \u003cem\u003eAIM2\u003c/em\u003e in SKCM, the ClusterProfiler package in R was used for gene ontology (GO) annotation, GSEA, and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis; the online visualization database, WebGestalt (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.webgestalt.org/\u003c/span\u003e\u003cspan address=\"http://www.webgestalt.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], was used to perform functional enrichment analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eComprehensive analysis of AIM2 in SKCM\u003c/h2\u003e \u003cp\u003eFirst, the mRNA and protein expression levels of \u003cem\u003eAIM2\u003c/em\u003e in SKCM were analyzed using data from TCGA, GTEx, Human Protein Atlas (HPA), GSE46517, GSE15605, and cohorts from GEO databases. We performed correlational analyses between mRNA expression levels of \u003cem\u003eAIM2\u003c/em\u003e in SKCM and various clinical characteristics based on TCGA data. Subsequently, we performed correlational analyses between \u003cem\u003eAIM2\u003c/em\u003e expression levels and the GSE65904 and TCGA\u0026ndash;SKCM datasets with. Using disease-free survival (DSS) and OS rates, we predicted the clinical prognoses of patients with SKCM. Based on the TCGA\u0026ndash;SKCM data, multivariate COX regression and Kaplan\u0026ndash;Meier analyses were used to evaluate clinical factors that significantly affected survival. A prognostic alignment diagram-based model was constructed and its prognostic value along with the immune correlations and risk scores were analyzed. Finally, functional enrichment and PANoptosis analyses were performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of AIM2-related genes for the construction of risk signature\u003c/h2\u003e \u003cp\u003eTo further study the expression characteristics of AIM2 in SKCM, based on the TCGA\u0026ndash;SKCM and GSE65904 data sets, AIM2-related genes (correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.6) were screened, and R language was used based on their expression levels. Finally, least absolute shrinkage and selection operator (LASSO) and multivariate COX regression analyses were performed on OS prognosis-related genes to construct a risk signature of AIM2-related genes. Risk signature was calculated as follows: risk signature\u0026thinsp;=\u0026thinsp;Σ(Expi \u0026times; coefi), where Coefi and Expi represent the risk factor and expression of each gene, respectively. Survival analysis, evaluation of immune scores, analysis of immune checkpoint-related gene expression, and functional enrichment analysis were performed for evaluating the molecular subtypes of high and low \u003cem\u003eAIM2\u003c/em\u003e expression groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry (IHC)\u003c/h2\u003e \u003cp\u003eThirteen patients pathologically diagnosed with SKCM in the Second Affiliated Hospital of Lanzhou University were included. The paraffin-embedded formalin-fixed SKCM tissues and adjacent normal skin tissues of these patients with SKCM were subjected to IHC staining to verify the expression levels of AIM2 in SKCM tissues. Standard staining procedures were followed for IHC staining. The antibody against AIM2 used in this study was obtained from Abcam (No. ab204995).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and transfection\u003c/h2\u003e \u003cp\u003eThe human skin melanoma cell line (A875) was purchased from Beina Bio (Shanghai, China). A875 was incubated at 37\u0026deg;C under 5% CO\u003csub\u003e2\u003c/sub\u003e conditions in a cell incubator and maintained in Dulbecco\u0026rsquo;s modified eagle medium (DMEM; Solarbio, 31600) with 10% fetal bovine serum (FBS; Bioind, Kibbutz Beit Haemek, Israel). Small interfering RNA (siRNA) targeting \u003cem\u003eAIM2\u003c/em\u003e were purchased from Jikai Gene (Shanghai, China). Lipofectamine\u0026trade; 3000 Transfection Reagent (Invitrogen, Carlsbad, USA) was used to transfect a negative control, and A875 was transfected by introducing siRNA targeting \u003cem\u003eAIM2\u003c/em\u003e according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTranswell assay\u003c/h2\u003e \u003cp\u003eAdd 100 \u0026micro;l of siRNA-transfected A875 cell suspension with a density of 5\u0026times;105/ml to the upper chamber of a Transwell chamber lined with Matrigel (BD, 356234), add 600 \u0026micro;l of serum-containing medium to the lower chamber, and culture for 48 hours. After the cells were fixed with 4% paraformaldehyde for 30 min, the cells in the lower layer were stained with Giemsa staining solution (Solarbio, G1015) for 30 min, and the unmigrated cells in the upper layer were gently wiped off with a cotton swab, and washed 3 times with PBS. Finally, the number of invaded cells was counted under a light microscope (Olympus, Japan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eScratch test\u003c/h2\u003e \u003cp\u003eSeed a suspension of siRNA-transfected A875 cells at a density of 5 \u0026times; 105/ml in a 6-well plate for 24 hours and incubate at 37\u0026deg;C. At 90% confluency, use a 200 \u0026micro;l pipette tip to create a Thin scratches were made, and non-adherent cells were rinsed three times with PBS, and images were collected using an inverted microscope (Zeiss, Germany) to determine the scratch distance at 0 h. Subsequently, cells were starved in serum-free medium. Photos were retaken at 12 h, 24 h, and 48 h using an inverted microscope (Zeiss, Germany). Use the Image-Pro Plus software measurement tool to measure the cell scratch boundary distance before and after treatment, and subtract the post-treatment distance from the pre-treatment distance, which is the migration distance of cells at 12 h, 24 h, and 48 h.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReal-time quantitative reverse transcription polymerase chain reaction (RT-qPCR)\u003c/h2\u003e \u003cp\u003esiRNA-transfected A875 cells were analyzed using RT-qPCR. Cell samples were collected after the cells reached approximately 70% confluency. Total RNA was extracted according to the instructions of EZNA Total RNA Kit Ⅰ (Omega, product number: R6834-01, batch number: R6834010000D20V099). cDNA was synthesized according to the instructions of Evo M-MLV Reverse Transcription Premix Kit (ACCURATE BIOLOGY, Cat. No. AG11728, Lot No. A4A0765). Amplification was performed using cDNA as a template according to the instructions provided in the SYBR Green Pro Taq HS Premixed qPCR Kit (ACCURATE BIOLOGY, Cat. No.: AG11701, Lot No.: A4A0503). After amplification, the relative expression level of the target gene was calculated as F\u0026thinsp;=\u0026thinsp;2\u0026minus;△△Ct. See Supplementary Table\u0026nbsp;2 for primer sequences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll RNAseq data in the transcripts per million (TPM) reads format were normalized after log\u003csub\u003e2\u003c/sub\u003e transformation. The Wilcoxon signed-rank test was used to compare normal and tumor tissue. Survival analysis was performed for the two CM groups using log-sequence analysis, and the predictive performance and risk scores of \u003cem\u003eAIM2\u003c/em\u003e were analyzed using timeROC. Univariate and multivariate Cox regression analyses were implemented via the R language \u0026ldquo;forestplot\u0026rdquo; package. According to the results of the Cox multivariate regression analysis, the R language \u0026ldquo;rms\u0026rdquo; package was used to construct a prognostic nomogram model. Spearman\u0026rsquo;s or Pearson\u0026rsquo;s test was used to analyze the relationship between the two variables. The above results were visualized using the R (v4.0.3) language \u0026ldquo;ggplot2\u0026rdquo; and \u0026ldquo;pheatmap\u0026rdquo; packages. The results of Transwell assay, Scratch test and RT-qPCR were statistically analyzed using SPSS22.0, the independent samples between groups were analyzed by T test, and the statistical methods of multiple groups were analyzed by single factor analysis.P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01,***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePan-cancer analysis of AIM2 as an independent prognostic factor for SKCM\u003c/h2\u003e \u003cp\u003eAIM2 has been reported to be abnormally expressed in various tumors; therefore, to fully understand the role of AIM2 in the occurrence of pan-cancer, we analyzed its expression and prognosis. Based on the GTEx dataset, we found that the expression of AIM2 was high in blood, pituitary glands, small intestine, and spleen tissues, and the remaining normal tissues showed low expression levels (Fig.\u0026nbsp;1 (A)). Subsequently, we explored the mRNA expression of \u003cem\u003eAIM2\u003c/em\u003e in pan-cancer occurrence. Based on the CCLE database analysis, \u003cem\u003eAIM2\u003c/em\u003e showed high expression levels in 18 tumor types, of which DLBC showed high expression levels, and expression levels in BLCA, MM, SKCM, LUAD, NSC, HNSC, and LCML were higher than 2 (Fig.\u0026nbsp;1 (B)). TCGA combined with GTEx database analysis found abnormal expression in 24 tumors, including BLCA, BRCA, CESC, COAD, ESCA, HNSC, KIRC, LAML, LGG, LUAD, LUSC, OV, PAAD, SKCM, STAD, THCA and UCEC. The expression levels of AIM2 mRNA were found to be upregulated in GBM and KIRP was downregulated in THYM, ACC, DLBC, LIHC, and PRAD (Fig.\u0026nbsp;1 (C)). Univariate COX regression analyses identified AIM2 as a risk factor for the OS among patients with KIRC, ACC, BLCA, COAD, LGG, MESO, and UVM, whereas AIM2 was identified as a protective factor for OS among patients with SKCM (Fig.\u0026nbsp;1 (I)). Multivariate COX regression analyses (OS) of patients with ACC, BLCA, COAD and UVM revealed that AIM2 was an independent prognostic factor of SKCM (Fig.\u0026nbsp;1 (E-J)). Using pan-cancer analysis, AIM2 was revealed to be significantly overexpressed in patients with SKCM and was an independent prognostic factor for SKCM, implicating its crucial role in predicting clinical outcomes of patients with SKCM. Therefore, we subsequently focused on investigating the function of AIM2 in SKCM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eHigh expression levels of AIM2 in SKCM are associated with improved prognoses\u003c/h2\u003e \u003cp\u003eTo gain a comprehensive understanding of \u003cem\u003eAIM2\u003c/em\u003e functions underlying SKCM development, we analyzed the GTEx data, along data from TCGA and the GSE15605 and GSE46517 datasets of the GEO database. The results revealed that AIM2 mRNA and protein levels (HPA database) in patients with SKCM were found to be significantly high (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;2 (A-B)). Further IHC analysis suggested that the protein levels of \u003cem\u003eAIM2\u003c/em\u003e in SKCM tissues were markedly higher than the normal skin tissues (Fig.\u0026nbsp;2 (C)). \u003cem\u003eAIM2\u003c/em\u003e expression levels were significantly correlated with radiation therapy, DSS, Breslow depth, T stage,and OS in patients with SKCM (Fig.\u0026nbsp;2 (E), Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eConsidering the clinical relevance of \u003cem\u003eAIM2\u003c/em\u003e, the Kaplan\u0026ndash;Meier analysis suggested that high expression levels of \u003cem\u003eAIM2\u003c/em\u003e, according to the GSE65904 and TCGA-SKCM datasets, may indicate protective effects and facilitate prognostic improvement in patients with SKCM (Fig.\u0026nbsp;2 (D)). The abovementioned results strongly suggest that \u003cem\u003eAIM2\u003c/em\u003e is a tumor suppressor gene closely related to the progression and metastasis of SKCM. To confirm that AIM2 is a tumor suppressor gene of SKCM, siRNA-based analysis was performed. The proliferation and migration abilities of A875 cells were significantly increased after the expression levels of AIM2 in the A875 cells were decreased ((Fig.\u0026nbsp;2 (F)); transwell assay) (Fig.\u0026nbsp;2 (G)); scratch test), verifying that \u003cem\u003eAIM2\u003c/em\u003e is an important tumor suppressor gene of SKCM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis for AIM2\u003c/h2\u003e \u003cp\u003eSubsequently, the biological functions associated with \u003cem\u003eAIM2\u003c/em\u003e were analyzed based on RNA-seq data from the TCGA-SKCM cohort. First, we conducted the GO annotation and KEGG enrichment analysis of 300 genes that were positively associated with \u003cem\u003eAIM2\u003c/em\u003e. Among the DEGs in groups with low and high levels of \u003cem\u003eAIM2\u003c/em\u003e expression, all biological functions and transduction cascades were found to be related to the immune system (Supplementary Fig.\u0026nbsp;1 (A-B and F)). Thus, \u003cem\u003eAIM2\u003c/em\u003e was involved in the TME.\u003c/p\u003e \u003cp\u003eTo further investigate the functional roles of \u003cem\u003eAIM2\u003c/em\u003e in SKCM, GSEA was performed using the RNA-seq data for SKCM in the TCGA database. A total of 39 molecular pathways significantly differed between the groups with low and high levels of \u003cem\u003eAIM2\u003c/em\u003e expression as evidenced by the online visualization database, WebGestaltR. Of these pathways, 37 were positively correlated with the high expression group and two were correlated with the low expression group. Immune responses and inflammatory signaling pathways were found to be significantly enriched in the high expression group, whereas pathways related to oxidative phosphorylation and ribosome formation were enriched in the low expression group (Supplementary Fig.\u0026nbsp;1 (E)). GSEA based on the ClusterProfiler package in R showed that the high level of \u003cem\u003eAIM2\u003c/em\u003e expression in SKCM was significantly enriched the immune and inflammatory responses; it also enriched the cell-related signaling pathways for immune responses, apoptosis, pyroptosis, and necrosis (Supplementary Fig.\u0026nbsp;1 (C-D)). Furthermore, the abovementioned findings[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] along with those reported in our recent study led us to hypothesize that \u003cem\u003eAIM2\u003c/em\u003e promotes cellular apoptosis, pyroptosis, and necrosis by binding to ZBP1, thereby initiating PANoptosis[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. We evaluated correlation between \u003cem\u003eAIM2\u003c/em\u003e expression and the marker genes of cell pyroptosis, apoptosis, and necrosis in SKCM-TCGA and GSE65904 datasets to further validate our hypothesis.\u003c/p\u003e \u003cp\u003e TThe correlation between AIM2 and SKCM immune infiltration\u003c/p\u003e \u003cp\u003eWe further evaluated the correlation between \u003cem\u003eAIM2\u003c/em\u003e expression and TME based on the results of functional enrichment and annotation. First, the correlation and differential expression of \u003cem\u003eAIM2\u003c/em\u003e with type I antitumor response genes (interferon gamma (IFNG) and granzyme B (GZMB)), type II protumor response gene interleukin 5 (IL-5) associated with antitumor immunity, and immune scores were analyzed. The results suggested that \u003cem\u003eAIM2\u003c/em\u003e expression was significantly correlated with \u003cem\u003eIFNG\u003c/em\u003e, \u003cem\u003eGZMB\u003c/em\u003e, and immune score; the association was higher in the high expression group than in the low expression group. Although IL-5 and \u003cem\u003eAIM2\u003c/em\u003e showed no correlation, the low expression group showed a high association, suggesting that \u003cem\u003eAIM2\u003c/em\u003e exerted antitumor immune effects (Fig.\u0026nbsp;3 (G-H), Fig.\u0026nbsp;4 (A-C), Supplementary Table\u0026nbsp;4). To fully elucidate the effects of \u003cem\u003eAIM2\u003c/em\u003e expression in immune cells, the CIBERSORT, TIMER, MCPCOUNTER, QUANTISEQ, xCell, and ssGSEA algorithms were used to analyze the differences in the abundance of various immune cell infiltrates between patients with low or high levels of \u003cem\u003eAIM2\u003c/em\u003e. The number of several immune cells was significantly elevated in patients with high \u003cem\u003eAIM2\u003c/em\u003e expression (Fig.\u0026nbsp;3 (A-F)), indicating that it promotes the initiation of immune responses. To further confirm the association of antitumor immunity and \u003cem\u003eAIM2\u003c/em\u003e expression, we analyzed the correlation between \u003cem\u003eAIM2\u003c/em\u003e expression and immune cell infiltration in 15 SKCM datasets in the GEO database and the TCGA-SKCM dataset was analyzed based on 8 algorithms. \u003cem\u003eAIM2\u003c/em\u003e expression was found to increase and the infiltration levels of antitumor immunity cells (natural killer (NK) cells, T cells, gamma delta T cells, dendritic cells (DCs), activated CD4\u0026thinsp;+\u0026thinsp;T cells, B cells, M1 macrophages, effector memory CD8\u0026thinsp;+\u0026thinsp;T cells, and activated CD8\u0026thinsp;+\u0026thinsp;T cells) increased. In particular, the infiltration levels of B and CD8\u0026thinsp;+\u0026thinsp;T cells significantly increased, whereas those of tumor-promoting immune cells (regulatory cells, cancer-associated fibroblasts, and M2 macrophages) did not change or partially decreased (Fig.\u0026nbsp;3 (F)). To further elucidate the \u003cem\u003eAIM2\u003c/em\u003e distribution in SKCM tissue samples, the expression data of six SKCM single cells in the TISCH database [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] were used to explore the \u003cem\u003eAIM2\u003c/em\u003e expression in different SKCM cell types (including cancer and noncancer cells). The results showed that among the six SKCM single-cell datasets analyzed, proliferating T and B cells showed the highest expression of \u003cem\u003eAIM2\u003c/em\u003e (Fig.\u0026nbsp;4 (E)). These results are consistent with previous findings that \u003cem\u003eAIM2\u003c/em\u003e is closely related to T cell activity. The analysis further confirmed the antitumor functions of \u003cem\u003eAIM2\u003c/em\u003e in TME. Subsequently, based on the TCGA-SKCM and GSE65904 datasets, we analyzed the correlation and differences between immune cell marker genes. Simultaneously, based on the 15 SKCM datasets in the GEO database and TCGA-SKCM datasets, \u003cem\u003eAIM2\u003c/em\u003e expression and the expression of chemokines, chemokine receptors, and MHC molecules were discussed in the context of the correlation between molecular and immune activator expression levels. \u003cem\u003eAIM2\u003c/em\u003e was found to be positively correlated with most antitumor immune cell marker genes, and was significantly expressed in the group with high levels of \u003cem\u003eAIM2\u003c/em\u003e expression (Fig.\u0026nbsp;4 (A-C), Supplementary Table\u0026nbsp;4). In contrast, as shown in 15 SKCM and TCGA-SKCM datasets in the GEO database, \u003cem\u003eAIM2\u003c/em\u003e expression was positively correlated with MHC molecules, suggesting the upregulation of antigen presentation and processing. The key chemokines inducing CD8\u0026thinsp;+\u0026thinsp;T cells (\u003cem\u003eCXCL9\u003c/em\u003e and \u003cem\u003eCXCL10\u003c/em\u003e) were positively correlated with \u003cem\u003eAIM2\u003c/em\u003e expression in 15 SKCM and TCGA-SKCM datasets. Other chemokines (\u003cem\u003eCXCL13,CCL8, CCL20, CCL2,CCL5,CCL4,CCL3,CCL25,CCL19,CCL21, CCL22, XCL1, XCL2,CXCL11,CXCL1,CXCL8,CCL7,CXCL16,CXCL2\u003c/em\u003e ,and \u003cem\u003eCXCL5\u003c/em\u003e) and receptors (\u003cem\u003eCXCR6, CCR2, CXCR3, CCR7,CCR1,CCR5,CXCR5,XCR1,CCR4\u003c/em\u003e, and \u003cem\u003eCCR6\u003c/em\u003e) showed positive correlations with \u003cem\u003eAIM2\u003c/em\u003e (Fig.\u0026nbsp;4 (D)). These chemokines and receptors promote the recruitment of effector tumor infiltrating immune cells, such as CD8\u0026thinsp;+\u0026thinsp;T, B, and antigen-presenting cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAIM2 stimulates CD8\u0026thinsp;+\u0026thinsp;T-cell infiltration by activating the PANoptosis pathway\u003c/h2\u003e \u003cp\u003eThe recruitment of activated CD8\u0026thinsp;+\u0026thinsp;T cells is the focus of tumor immunotherapy. The TCGA-SKCM dataset was analyzed based on the TIMER 2.0 online database, the GASElite online database[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and ssGSEA. The findings suggested that \u003cem\u003eAIM2\u003c/em\u003e expression was positively correlated with CD8\u0026thinsp;+\u0026thinsp;T-cell infiltration (Fig.\u0026nbsp;5 (C-E)). Subsequently, the same was confirmed on analysis of 15 SKCM datasets in the GEO database based on the TIMER algorithm (Fig.\u0026nbsp;5 (I)). Patients with high \u003cem\u003eAIM2\u003c/em\u003e expression levels showed significantly higher expression levels of T cell signature genes than those exhibiting low expression levels (Fig.\u0026nbsp;5 (H)). We also found that \u003cem\u003eAIM2\u003c/em\u003e expression was positively correlated with T cell signature genes (Fig.\u0026nbsp;5 (F-G)). GSEA indicated that \u003cem\u003eAIM2\u003c/em\u003e expression was positively correlated (FDR\u0026thinsp;=\u0026thinsp;0.01, NES\u0026thinsp;=\u0026thinsp;1.949; FDR\u0026thinsp;=\u0026thinsp;0.01, NES\u0026thinsp;=\u0026thinsp;2.232; Fig.\u0026nbsp;5 (A-B)) with T-cell infiltration.\u003c/p\u003e \u003cp\u003eWe performed GSEA to evaluate \u003cem\u003eAIM2\u003c/em\u003e-related molecular mechanisms that affected T cell infiltration. \u003cem\u003eAIM2\u003c/em\u003e expression was associated with signaling transduction in cell pyroptosis, apoptosis, and necrosis. Recent studies revealed that \u003cem\u003eAIM2\u003c/em\u003e regulates Z-DNA binding protein 1 (ZBP1), an intrinsic immune response sensor, resulting in the formation of ZBP1 PANoptosome and thereby activating the PANoptosis pathway. This initiates PANoptosis inflammatory cell necrosis, promotes downstream apoptotic pyroptosis, and leads to the activation of necrotic effector molecules, causing inflammatory cell necrosis and cytokine release, eventually promoting T-cell infiltration in TME[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This study revealed that patients with high \u003cem\u003eAIM2\u003c/em\u003e expression showed a significantly enriched number of genes related to apoptosis, pyroptosis, and cell death signaling pathways downstream of the PANoptosis pathway (Fig.\u0026nbsp;6 (A)). Thus, \u003cem\u003eAIM2\u003c/em\u003e was positively correlated with apoptosis, pyroptosis, and cell death signaling pathways.\u003c/p\u003e \u003cp\u003eConsistent with recent studies and functional enrichment analysis, we speculate that \u003cem\u003eAIM2\u003c/em\u003e initiates PANoptosis by binding to \u003cem\u003eMEFV\u003c/em\u003e (encoding pyrin protein) and ZBP1 to promote apoptosis, pyroptosis, and necrosis. To evaluate our hypothesis, we used SKCM data in the TCGA and GSE65904 datasets that explored the relationship between \u003cem\u003eAIM2\u003c/em\u003e, the core genes (\u003cem\u003eMEFV\u003c/em\u003e and \u003cem\u003eZBP1\u003c/em\u003e) and their related genes (\u003cem\u003eRIPK3\u003c/em\u003e, \u003cem\u003eRIPK1\u003c/em\u003e, \u003cem\u003ecaspase-6\u003c/em\u003e, \u003cem\u003eCaspase-8\u003c/em\u003e, \u003cem\u003eNLRP3\u003c/em\u003e, \u003cem\u003eand Caspase-1\u003c/em\u003e) in the ZBP1 PANoptosome. The expression levels of all genes, except \u003cem\u003eRIPK1\u003c/em\u003e, were positively correlated with \u003cem\u003eAIM2\u003c/em\u003e expression in both the TCGA-SKCM and GSE65904 datasets, showing high expression levels in patients with high \u003cem\u003eAIM2\u003c/em\u003e expression (Fig.\u0026nbsp;6 (B-E). Among these, the correlation between the core genes (\u003cem\u003eMEFV\u003c/em\u003e and \u003cem\u003eZBP1\u003c/em\u003e) and \u003cem\u003eAIM2\u003c/em\u003e was found to be stronger. Under conditions of low AIM2 expression, we subsequently investigated the expression of six important genes including core genes (\u003cem\u003eMEFV\u003c/em\u003e and \u003cem\u003eZBP\u003c/em\u003e1), pyroptosis, apoptosis, necrosis, and CD8\u0026thinsp;+\u0026thinsp;T cell marker genes (\u003cem\u003eGSDMD\u003c/em\u003e, \u003cem\u003eMLKL\u003c/em\u003e, \u003cem\u003eCASP-8\u003c/em\u003e, \u003cem\u003eCD8A\u003c/em\u003e) in the AIM2 PANoptosis complex based on the siRNA interference method. The expression of the core genes (\u003cem\u003eMEFV\u003c/em\u003e and \u003cem\u003eZBP1\u003c/em\u003e), pyroptosis, apoptosis, necrosis, and CD8\u0026thinsp;+\u0026thinsp;T cell marker genes (\u003cem\u003eGSDMD\u003c/em\u003e, \u003cem\u003eCASP-8\u003c/em\u003e, and \u003cem\u003eCD8A\u003c/em\u003e) in the AIM2 PANoptosis complex decreased under conditions of low AIM2 expression (Fig.\u0026nbsp;6 (F)), indicating that the PANoptosome is activated by abnormally high expression levels of \u003cem\u003eAIM2\u003c/em\u003e. To further understand the effects of the PANoptosome on TME, the correlation between 13 PANoptosome signature genes and levels of immune cell infiltration in TME were investigated using TIMER and MCPCOUNTER algorithms and CD4\u0026thinsp;+\u0026thinsp;T cells, B cells, and NK cells (Fig.\u0026nbsp;6 (G-H)). The results suggested that PANoptosome remodels the immune activation of the TME and inhibits tumor immune escape. To verify the abovementioned conclusions, we analyzed the correlation between PANoptosome signature genes, T cell signature genes, and MHC molecules, and found that PANoptosome signature genes were positively correlated with T cell signature genes and MHC molecules (Fig.\u0026nbsp;6 (I-J)).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eAIM2 improves response to ICBs\u003c/h2\u003e \u003cp\u003e \u003cem\u003eAIM2\u003c/em\u003e was found to be related to antitumor immunity and could also be related to immunotherapeutic responses. Thus, we evaluated the effects of \u003cem\u003eAIM2\u003c/em\u003e expression on immunotherapeutic responses. Solid tumors have been reported to be classified into two types based on immunotherapy sensitivity; hot and cold tumors, with hot tumors being responsive to ICBs. Therefore, we used hot tumor signature genes (\u003cem\u003eCCL5, CD8A, PDCD1, CD8B, CXCR3, CXCL9, CXCL10, CD4, CD3E, CXCL11, CD274\u003c/em\u003e and \u003cem\u003eCXCR4\u003c/em\u003e) in this study[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. An unsupervised clustering method was used to classify patients with SKCM in the TCGA database as having immuno-cold or immuno-thermal tumors. Subsequently, the GSE65904 dataset was divided into two clusters (Supplementary Fig.\u0026nbsp;2 (A-C)). The differences in \u003cem\u003eAIM2\u003c/em\u003e expression and prognoses between the two clusters suggested that \u003cem\u003eAIM2\u003c/em\u003e was significantly overexpressed in hot tumors and was significantly associated with improved prognoses (Supplementary Fig.\u0026nbsp;2(D)), indicating its correlation with ICB responses. To further investigate the therapeutic value of \u003cem\u003eAIM2\u003c/em\u003e, we analyzed the correlational and differential expressions of \u003cem\u003eAIM2\u003c/em\u003e with eight predictors of immunotherapy response[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], including \u003cem\u003eSIGLEC15\u003c/em\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], \u003cem\u003eIDO1, CD274\u003c/em\u003e, \u003cem\u003eHAVCR2\u003c/em\u003e, \u003cem\u003ePDCD1\u003c/em\u003e, \u003cem\u003eCTLA4\u003c/em\u003e, \u003cem\u003eLAG3\u003c/em\u003e, and \u003cem\u003ePDCD1LG2\u003c/em\u003e. \u003cem\u003eAIM2\u003c/em\u003e expression was strongly correlated with these molecules and the correlation coefficients ranged between 0.52 and 0.60 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; (Fig.\u0026nbsp;7 (A)). These molecules were found to be highly expressed in patients with high \u003cem\u003eAIM2\u003c/em\u003e expression levels (Fig.\u0026nbsp;7 (B)). Therefore, these results strongly suggested that \u003cem\u003eAIM2\u003c/em\u003e promotes tumor therapeutic effects. To validate the efficacy of AIM2 in SKCM, the melanoma immunotherapy Dizier cohort 2013 and Wolf cohort 2021 analyses were performed. \u003cem\u003eAIM2\u003c/em\u003e expression in ICB treatment response patients was significantly higher than that in ICB treatment nonresponsive patients (Fig.\u0026nbsp;7 (G)). Thus, \u003cem\u003eAIM2\u003c/em\u003e can enhance the therapeutic effects of SKCM ICBs. Based on the TIDE online database analysis, the survival of patients with high \u003cem\u003eAIM2\u003c/em\u003e expression levels was significantly prolonged after ICI treatment, and \u003cem\u003eAIM2\u003c/em\u003e expression was positively correlated with T cell infiltration (Fig.\u0026nbsp;7 (C)).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eAIM2 as a predictive marker for ICB in SKCM\u003c/h2\u003e \u003cp\u003eTMB can predict immunotherapy efficacy. \u003cem\u003eAIM2\u003c/em\u003e was found to be positively correlated with TMB (Fig.\u0026nbsp;7 (D)), suggesting that \u003cem\u003eAIM2\u003c/em\u003e was involved in immunotherapeutic responses and could predict immunotherapeutic efficacy. Based on 15 melanoma tumor cohorts, the area under the curve (AUC) of receiver operating characteristic (ROC) for \u003cem\u003eAIM2\u003c/em\u003e expression with 13 treatment cohorts except VanAllen cohort 2015 (Anti-CTLA-4) (0.496) and Van cohort 2021 (Anti-PD-L1) (0.495) greater than 0.5, among which the AUC value in the Gao cohort 2018 (Anti\u0026thinsp;\u0026minus;\u0026thinsp;PD\u0026thinsp;\u0026minus;\u0026thinsp;1/CTLA\u0026thinsp;\u0026minus;\u0026thinsp;4) cohort was greater than 0.931 (Fig.\u0026nbsp;7(E)), strongly suggesting that \u003cem\u003eAIM2\u003c/em\u003e is a predictor of the effect of SKCM immunotherapy.Based on TIDE, we compared the area under the curve (AUC) of receiver operating characteristic (ROC) for \u003cem\u003eAIM2\u003c/em\u003e expression with existing biomarkers (TMB, CD274, and PD-L1), and MSI. The score was used as a tool to predict immunotherapy responses. In the seven SKCM cohorts, \u003cem\u003eAIM2\u003c/em\u003e showed comparable predictive performances with CD274, much higher relative to TMB and MSI. AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 was obtained in all three SKCM cohorts, suggesting the efficiency in the prediction of a strong positive immunotherapeutic response. Notably, \u003cem\u003eAIM2\u003c/em\u003e was the only biomarker showing a positive performance for predicting ICB responses in the Riaz_2017 cohort (AUC\u0026thinsp;=\u0026thinsp;0.82)(Supple Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eWe also found that the tumor stemness index of patients with SKCM and high \u003cem\u003eAIM2\u003c/em\u003e expression levels was lower than that of patients with low \u003cem\u003eAIM2\u003c/em\u003e expression levels (Fig.\u0026nbsp;7 (F)).\u003c/p\u003e \u003cp\u003eTo further study the effects of \u003cem\u003eAIM2\u003c/em\u003e on SKCM treatment response, we performed a correlational analysis between \u003cem\u003eAIM2\u003c/em\u003e expression level and drug sensitivity in GEO15 SKCM and TCGA-SKCM datasets based on the GDSC database. The sensitivity of most drugs was negatively correlated. These data suggested that \u003cem\u003eAIM2\u003c/em\u003e can act as a biomarker for predicting drug treatment (Fig.\u0026nbsp;7 (H)).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and testing of AIM2-related gene risk signature\u003c/h2\u003e \u003cp\u003eBased on TCGA-SKCM and GSE65904 datasets, analyses revealed that 15 genes (\u003cem\u003eGBP2\u003c/em\u003e) were strongly positively correlated with AIM2 (correlation coefficients\u0026thinsp;\u0026gt;\u0026thinsp;0.6) (Fig.\u0026nbsp;8(B-D)). Thus, they may have some common features or functions. Univariate Cox regression analyses showed that AIM2 and 15 genes were strongly correlated and included in LASSO regression analyses (Fig.\u0026nbsp;8 (A)). Lasso regression analyses were performed on these 16 genes based on the TCGA-SKCM dataset, and six genes were identified for constructing the risk signature (lambda.min\u0026thinsp;=\u0026thinsp;0.0395). Risk score = (\u0026minus;\u0026thinsp;0.0013)*BIRC3 + (\u0026minus;\u0026thinsp;0.0048)*SAMSN1 + (\u0026minus;\u0026thinsp;0.0982)*APOBEC3G + (\u0026minus;\u0026thinsp;0.1376)*GBP2 + (\u0026minus;\u0026thinsp;0.0316)*GBP5 + (\u0026minus;\u0026thinsp;0.0044)*AIM2. As shown in the above equation, the risk score was calculated by multiplying the sum of the expression levels of each risk signature gene with the coefficient of each risk signature gene. Patients with SKCM in the TCGA cohort were divided into high- and low-risk groups according to the median risk score. Scatterplots and heatmaps indicate the survival of patients with risk scores and the expression of six risk signature genes in high- and low-risk groups. It suggests that the death rate of samples with high risk scores is significantly higher than that of samples with low risk scores, and the prognosis of samples with high risk scores is poor. The AUC value of the ROC curve was used to evaluate the ability of the prognostic prediction of the model, and the results found that the AUC values ​​at 2, 4, 6, and 8 years were 0.703, 0.686, 0.696, and 0.735, respectively. Based on the risk score, the Kaplan\u0026ndash;Meier analysis revealed a significant difference between the groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and the high-risk group showed a poorer prognosis (Fig.\u0026nbsp;8 (E)). These results were verified using the GSE65904 dataset (Fig.\u0026nbsp;8(G)). In conclusion, our prognostic risk signature showed robustness and can play a stable role in prognostic prediction for different cohorts.\u003c/p\u003e \u003cp\u003eTo explore the different prognostic differences between the high- and low-risk groups, the correlation between the risk score and immune cell infiltration level was analyzed based on the TMER and EPIC algorithms. The results showed that the risk score was negatively correlated with the infiltration levels of antitumor B cells (Supplementary Fig.\u0026nbsp;5 (A-B)). Compared with the high-risk group, the low-risk group had an increased number of various antitumor immune cells with significantly improved prognosis.\u003c/p\u003e \u003cp\u003eTo better understand whether risk score is a clinically independent prognostic factor in patients with SKCM, univariate and multivariate COX regression analyses were performed integrating risk score and patient clinical characteristics. Univariate cox regression analysis results found that risk score, age, T stage, N stage, M stage, and stage were associated with OS prognosis, whereas multivariate cox regression analyses showed that risk score, age, T stage, N stage, and M stage were correlated with OS prognosis. Therefore, risk score, age, T stage, N stage, and M stage may be independent prognostic factors. Multivariate analyses revealed that low-risk scores were significantly associated with favorable OS rates even after adjusting for other clinical characteristics. In conclusion, AIM2 and its five related gene signatures can be used as independent risk factors for predicting the prognosis of patients with SKCM. Subsequently, we constructed a nomogram model employing clinical factors to predict mortality in patients with SKCM by incorporating independent prognostic factors (Fig.\u0026nbsp;8 (F)). By calculating the scores of the abovementioned variables for each patient, we could predict the 2-, 4-, 6-, and 8-year OS rates for the patients. The higher the total score, the worse the patient prognosis. The C-index of the nomogram was 0.716 (95% confidence interval: 0.676\u0026ndash;0.755), indicating good discriminative ability. Furthermore, the calibration curve lithogram confirmed that the constructed nomogram model could accurately predict performance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCancer therapy is mainly based on the induction of cell death, and the two major modes of cell death (pyroptosis and PANoptosis) greatly affect the TME. Therefore, we investigated the relationship between AIM2, an important inducer of pyroptosis and PANoptosis, and tumor immune features to determine the prognostic value of AIM2 in SKCM. We found that AIM2 is considered an important tumor suppressor. Further analysis showed that \u003cem\u003eAIM2\u003c/em\u003e is may correlated with immune infiltration and PANoptosis. Furthermore, \u003cem\u003eAIM2\u003c/em\u003e expression is associated with increased cytotoxic T-cell infiltration and responses to ICB treatment in patients with SKCM. Mechanistically, \u003cem\u003eAIM2\u003c/em\u003e may increase the efficacy of immunotherapy by activating the PANoptosis pathway, which initiates PANoptosis inflammatory cell apoptosis and promotes CD8\u0026thinsp;+\u0026thinsp;T-cell infiltration. Furthermore, a prognostic risk model with a 6-gene signature was constructed on the basis of AIM2 and its 15 strongly correlated genes, and its predictive ability was determined. Patients with low-risk and high-risk signatures exhibited significantly different prognoses and TME. According to the results of multivariate COX regression analysis of risk factors, we established a prognostic nomogram model, which further improved the prediction performance. This prognostic model can be used for the prognostic stratification of patients with SKCM, which can help us to better understand the molecular mechanism underlying SKCM and provide new strategies for targeted therapy.\u003c/p\u003e \u003cp\u003eIn this study, \u003cem\u003eAIM2\u003c/em\u003e was mainly involved in several tumor-immune-related and inflammatory responses, along with cellular signaling pathways in apoptosis and necrosis, including B-cell receptor, toll-like receptor, nod-like receptor, T-cell receptor transduction cascades, complement activation, chemokines, cytokines, inflammatory responses, and antigen processing and presentation. Furthermore, the association of \u003cem\u003eAIM2\u003c/em\u003e with inflammatory cell necrosis in PANoptosome was verified. The interferon regulatory factor 1-dependent activation of PANoptosis prevents azoxymethane /dextran sodium sulfate-induced colorectal tumorigenesis in a mouse model of colorectal cancer, indicating a wider scope of PANoptosis in cancer[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In this study, the relationship between \u003cem\u003eAIM2\u003c/em\u003e and PANoptosis was confirmed, which provided mechanistic evidence for the functions of \u003cem\u003eAIM2\u003c/em\u003e in cancer.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAIM2\u003c/em\u003e expression increases the levels of immune infiltration of anti-tumor immune cells (such as DC) in the TME. This is consistent with the results of previous studies[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The underlying mechanism is possibly by increasing the infiltration levels of CD8\u0026thinsp;+\u0026thinsp;T cells by activating the PANoptosis signaling pathway. Recently, cell pyroptosis has been shown to promote the infiltration of T-cells in the TME[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. We found a significant positive correlation between \u003cem\u003eAIM2\u003c/em\u003e expression and the downstream PANoptosis effectors, cellular pyroptosis, apoptosis, and necrosis pathways, indicating the mechanism underlying \u003cem\u003eAIM2\u003c/em\u003e-mediated CD8\u0026thinsp;+\u0026thinsp;T cell infiltration.\u003c/p\u003e \u003cp\u003eFurthermore, the expression of eight immune checkpoint genes also exhibited an upregulated trend with high \u003cem\u003eAIM2\u003c/em\u003e expression compared with that in the low-expression cohort and showed a positive association with \u003cem\u003eAIM2\u003c/em\u003e expression. Together, these results indicated that the administration of ICB to patients with high \u003cem\u003eAIM2\u003c/em\u003e expression decreased the immunosuppressive state induced by PD-L1 expression, making the outcomes of immunotherapy more favorable. Based on this hypothesis, we detected \u003cem\u003eAIM2\u003c/em\u003e expression in two SKCM immunotherapy cohorts and used the TIDE algorithm to predict the response and prognosis of ICB in patients with different \u003cem\u003eAIM2\u003c/em\u003e expression levels. The results showed that \u003cem\u003eAIM2\u003c/em\u003e expression was higher in ICB treatment-responsive patients than in ICB-treatment non-responsive patients. Furthermore, patients with high expression of \u003cem\u003eAIM2\u003c/em\u003e had prolonged survival after ICB treatment, which confirmed our hypothesis. Folic acid-transplanted PEI600-CyD (h1) nanoparticle-mediated DNA vaccine can act as a vaccine adjuvant to increase the activity of tumor-specific CD8\u0026thinsp;+\u0026thinsp;T cells by triggering \u003cem\u003eAIM2\u003c/em\u003e expression, thereby promoting antitumor therapeutic efficacy[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Based on the findings of this study, it was reasonably hypothesized that the response to ICB treatment due to \u003cem\u003eAIM2\u003c/em\u003e in SKCM is facilitated by the following two factors: \u003cem\u003eAIM2\u003c/em\u003e promotes the activation and infiltration of effector T cells. It also may induces tumor cell pyroptosis, apoptosis, and necrosis by activating PANoptosis.\u003c/p\u003e \u003cp\u003eAs predictive biomarkers of immunotherapeutic responses, \u003cem\u003eAIM2\u003c/em\u003e expression is positively associated with TMB. The analysis of two SKCM ICB treatment cohorts found that the AUC values of \u003cem\u003eAIM2\u003c/em\u003e ​​in the two SKCM ICB treatment cohorts were \u0026gt;\u0026thinsp;0.7, indicating its putative utility as a marker for predicting immunotherapeutic responses. Through the TIDE analysis, it was found that the AUC values of \u003cem\u003eAIM2\u003c/em\u003e in three of the seven SKCM-ICB treatment cohorts were \u0026gt;\u0026thinsp;0.7, which strongly indicated that \u003cem\u003eAIM2\u003c/em\u003e can be used as a marker to predict responses to ICB in SKCM.\u003c/p\u003e \u003cp\u003eCancer stem cells (CSCs) are self-renewing cells that promote tumorigenesis, progression, and metastasis[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Previous studies have shown that there is a significant association between cancer stemness and cancer immune evasion and drug resistance[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Recent studies have shown that ICB therapy is less effective in patients with high stemness[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Analyzing the differences in tumor stemness between patients with high and low expression of \u003cem\u003eAIM2\u003c/em\u003e showed that the tumor stemness index of patients with high \u003cem\u003eAIM2\u003c/em\u003e expression was lower than that of patients with low \u003cem\u003eAIM2\u003c/em\u003e expression. These results revealed that patients with low \u003cem\u003eAIM2\u003c/em\u003e expression have cancers with increased invasive ability, poor prognosis, and promoted tumor immune escape. Furthermore, the ICB treatment effect is poor due to the risk of drug resistance.\u003c/p\u003e \u003cp\u003eBased on this hypothesis, we performed \u003cem\u003ein vitro\u003c/em\u003e experiments to investigate the invasive ability of the A875 cell line with different \u003cem\u003eAIM2\u003c/em\u003e expression levels and analyzed the correlation between AIM2 expression levels and drug sensitivity using the GDSC database. The invasive ability of the A875 cell line was significantly increased, and the low expression of \u003cem\u003eAIM2\u003c/em\u003e in SKCM was positively correlated with the sensitivity of most drugs. The analysis of immune infiltration and ICB treatment response in patients with high and low \u003cem\u003eAIM2\u003c/em\u003e expression showed that patients with high \u003cem\u003eAIM2\u003c/em\u003e expression had increased levels of anti-tumor immune cell infiltration, inhibited tumor immune escape, and significantly higher responsiveness to ICB treatment compared with those in patients with low \u003cem\u003eAIM2\u003c/em\u003e expression. Patient survival was prolonged after treatment, which confirmed our hypothesis.\u003c/p\u003e \u003cp\u003eOn the basis of the key role of AIM2 in copper cell apoptosis, we constructed a risk prognostic model by selecting genes that are strongly associated with AIM2 by performing LASSO regression and univariate Cox analyses. The prognosis and TME of patients with high and low-risk scores were significantly different. Univariate and multivariate Cox regression analyses showed that the risk score was an independent prognostic factor in patients with SKCM. Age, TNM stages, and risk score were independent prognostic factors in patients with SKCM. To further investigate the efficacy of the prognostic risk model in predicting prognosis, we constructed a prognostic nomogram model based on the age and TNM stages of the patients and risk score to further evaluate the predictive efficacy of each patient with SKCM. The C value of the nomogram model is 0.716 (95% confidence interval [CI]: 0.676\u0026ndash;0.755), which reveals that the prognostic nomogram model has a good predictive effect. Therefore, the risk score can be used as a predictor of the clinical treatment and prognosis of SKCM, which is of immense importance for understanding the molecular mechanism underlying SKCM and can provide new strategies for targeted therapy.\u003c/p\u003e \u003cp\u003eThe present study has some limitations. This study was primarily based on the analysis of multiple bioinformatic tools; therefore, subsequent experiments should validate these findings. However, we used two or more methods and relevant literature data for validating the results. For instance, to verify the correlation between \u003cem\u003eAIM2\u003c/em\u003e and the TME, various methods, including GO annotation, KEGG functional enrichment analysis, GSEA, CIBERSORT, xCell, ssGSEA algorithms, TIMER2.0, GASElite, TIDE online database, and correlation-based investigation of cold and hot tumors, were utilized. This improved the reliability of the results. Furthermore, although it was found that the combined application of \u003cem\u003eAIM2\u003c/em\u003e-based expression and clinical alignment diagrams improved the prognostic prediction of patients with SKCM, the findings could not be validated using other datasets owing to the paucity of cohorts with complete clinical data. However, the analysis of the ICB-treated melanoma cohort on the basis of the TIDE online database confirmed the ability of \u003cem\u003eAIM2\u003c/em\u003e expression to function as an independent predictor of survival.\u003c/p\u003e \u003cp\u003eTo summarize, \u003cem\u003eAIM2\u003c/em\u003e is an important protective factor of SKCM. Furthermore, the prognostic model for SKCM had favorable predictive efficacy. \u003cem\u003eAIM2\u003c/em\u003e can increase immunotherapeutic efficacy by may activating PANoptosis signaling and promoting CD8\u003csup\u003e+\u003c/sup\u003e T-cell infiltration. These findings are expected to provide valuable insights into the development of cytokine-based tumor immunotherapy.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Second Affiliated Hospital of Lanzhou University.Patients/participants provided their written informed consent to participate in this study. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they do not have any conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis replaces any statement written within the manuscript and is the one that we will publish. Sheng Yong Long designed this study，Jing Xu oversaw it. \u0026nbsp;Sheng Yong Long was analyzed data, Yu Mao Wang and Wan Qian Chen examined statistical methods.The manuscript was written by Sheng Yong Long. All authors in this study contributed to the article and approved the submitted version, all authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the TCGA database and the GEO database, We gratefully acknowledge contributions from the CASElite and TMER2.0 platforms\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchadendorf D, van Akkooi ACJ, Berking C, Griewank KG, Gutzmer R, Hauschild A, Stang A, Roesch A, Ugurel S, Melanoma. Lancet (London England). 2018;392(10151):971\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreydanus DE, Pratt HD, Patel DR. Preface. 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Genome Med. 2022;14(1):45.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3899213/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3899213/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAbsent in melanoma 2 (AIM2) is an important developmental regulator for innate immune responses, and recent studies on AIM2 have reported its vital role in cancer development and progression. However, AIM2 in skin cutaneous melanoma (SKCM) tumor immune microenvironment has not been extensively studied.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe explored the expression and prognostic value of AIM2 at the pan-cancer level based on multiple public databases. We analyzed the SKCM transcriptome sequencing data and clinical information, available on various public databases, using differential analysis, prognostic analysis, machine learning, and various immune infiltration algorithms. We used online visualization databases to explore AIM2 expression in SKCM to determine its prognostic impact. Furthermore, we constructed a risk signature based on AIM2-related genes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBased on the pan-cancer analysis, AIM2 was found to be an independent prognostic factor for SKCM. AIM2 expression notably differed in SKCM and was associated with an improved survival rate among patients. Increased AIM2 expression promoted the immune response of patients with SKCM, inducing pyroptosis, apoptosis, and necroptosis. \u003cem\u003eIn vitro\u003c/em\u003e transwell assay and scratch test showed that the knockdown of AIM2 expression increased its invasiveness and metastasis of the SKCM cell line, A875. Knockdown of AIM2 expression revealed decreased expression of \u003cem\u003eZBP1\u003c/em\u003e and \u003cem\u003eMEFV\u003c/em\u003e, the important genes in the PANoptosis complex. Simultaneously, the expression of pyroptosis, apoptosis, and CD8\u003csup\u003e+\u003c/sup\u003e T cell marker genes (\u003cem\u003eGSDMD\u003c/em\u003e, \u003cem\u003eCASP-8\u003c/em\u003e, and \u003cem\u003eCD8A\u003c/em\u003e) also decreased. The infiltration levels of various antitumor immune cells positively correlated with AIM2 expression, and the infiltration levels notably differed between patients with high and low levels of AIM2 expression. The Tumor Immune Dysfunction and Exclusion framework analysis revealed that AIM2 expression accurately facilitated the prediction of the efficacy of SKCM immunotherapy. Mechanistic analysis revealed an association between AIM2 overexpression and PANoptosis signaling upregulation, thereby affecting the patterns of chemokines and cytokines in TIME. Furthermore, the prediction and prediction performance of the prognostic model was found to be accurate.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003e \u003cem\u003eAIM2\u003c/em\u003e is associated with an increased abundance of effector CD8\u003csup\u003e+\u003c/sup\u003e T cells, positive responses to immune checkpoint blockade treatment, and improved SKCM prognoses. Therefore, it could be used as a putative enhancer and prognostic biomarker for SKCM treatment.\u003c/p\u003e","manuscriptTitle":"Prognostic value and immune status of AIM2 in skin cutaneous melanoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-19 20:21:09","doi":"10.21203/rs.3.rs-3899213/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":"67509461-2115-465a-874f-31e60d018351","owner":[],"postedDate":"March 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-20T14:29:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-19 20:21:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3899213","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3899213","identity":"rs-3899213","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}