ALG13 as a prognostic biomarker of prostate cancer associated with tumor immune infiltration and mediated by upstream ncRNA

ALG13 as a prognostic biomarker of prostate cancer associated with tumor immune infiltration and mediated by upstream ncRNA | 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 Article ALG13 as a prognostic biomarker of prostate cancer associated with tumor immune infiltration and mediated by upstream ncRNA Maolin Xiao, Yunfeng Xiao, Wanlan Liu, Xiao Xiao, Zongke Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2680822/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 Asparagine-linked glycosylation 13 (ALG13) is a highly conserved protein in most eukaryotes, which belongs to the OTU family. It plays a role in neuroblastoma and non-small cell lung cancer. However, the role of ALG13 in prostate cancer (Pca) and its correlation with tumor-infiltrating immune cells remain unclear. Thus, in this study, we extracted and analyzed The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Tumor Immune Estimation Resource (TIMER), and Human Protein Atlas (HPA) data sets to study the potential carcinogenic mechanism of ALG13, including ALG13 expression, prognosis and the correlation of ALG13 expression in immune cell infiltration in Pca. Furthermore, the potential biological signaling pathway of ALG13 in Pca was studied by using Gene set enrichment analysis (GSEA). Upstream microRNA and lncRNA related to ALG13 were found through the prediction of miRWalk and starBase. Results showed that ALG13 was highly expressed in Pca tissues and associated with poor overall survival (OS) and disease-specific survival (DSS). ALG13 expression was correlated with immune cell infiltration. In addition, ALG13 was co-expressed with most immune-related genes, and the high-expression of ALG13 was effective for immune-checkpoint blockade treatment. ALG13 may regulate the pathogenesis of Pca through tumor and immune-related pathways. Finally, AL390728.6/hsa-miR-381-3p axis is considered as a potential upstream ncRNA-related pathway of ALG13 in Pca. Our results demonstrate that the ncRNA-mediated upregulation of ALG13 is associated with poor OS in Prostate adenocarcinoma (PRAD) and tumor immune infiltration. ALG13 may be a new potential prognostic biomarker. Health sciences/Biomarkers Health sciences/Oncology Health sciences/Urology ALG13 prostate cancer prognosis biomarker immune infiltration ncRNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Prostate cancer (Pca) has the largest number of new cases among male patients worldwide every year, and it is also the second largest cause of tumor-related mortality 1 . Prostate adenocarcinoma (PRAD) is a common type of Pca. In China, approximately 125,646 new cases of Pca are reported, while about 56,239 patients die because of Pca 2 . With the improvement of diagnostic technology and early screening, an increasing number of patients with Pca are diagnosed, but many patients are initially diagnosed with metastatic cancer; thus, they lose the opportunity for surgery. In addition, castrate-resistant prostate cancer appeared after treatment. Pca is a “cold” tumor, with little success in dealing with various immune-related treatments 3 . Therefore, biomarkers are urgently needed for the diagnosis of Pca, and exploring immune-related molecular markers is an important focus of Pca research. Asparagine-linked glycosylation 13 (ALG13) is a putative bifunctional uridine diphospho-N-acetylglucosamine(UDP-GlcNAc) transferase and deubiquitinase, a highly conserved protein in most eukaryotes. ALG13 belongs to the ovarian tumor(OUT) domain-containing protein (OTUDs) family. OTUD is also a subfamily of ovarian tumor-associated proteases (OTUs), and it a subtype of the deubiquitinating enzyme system 4 , 5 . ALG13 is located on the X chromosome, and it encodes a protein heterodimerized with ALG14, which forms a functional UDP-GlcNAc glycosyltransferase on the endoplasmic reticulum and catalyzes the second step of protein N-glycosylation 6 , 7 . ALG13 plays an important role in human malignant and nonmalignant diseases. Previous reports implicated that ALG13 gene mutations caused X-linked congenital disorder of glycosylation type I (CDG-I), which is also known as ALG13-CDG 8 , 9 . Similarly, a missense mutation of the X-linked gene ALG13 was found in a child with seizures and early death caused by CDG-1 10 . In tumor diseases, some studies have shown that the expression level of ALG13 mRNA decreased in lung adenocarcinoma and squamous cell carcinoma 11 , 12 . In a recent study, the mRNA expression level of ALG13 decreased in lung cancer tissues compared with normal tissues, including patients with adenocarcinoma and squamous cell carcinoma, and the high-expression level of ALG13mRNA was not significantly correlated with the prognosis of patients with squamous cell carcinoma, but it could predict the overall survival (OS) of patients with adenocarcinoma 13 . ALG13 has also been identified as an early target of microRNA-34a, which was associated with the development of neuroblastoma 14 . However, comprehensive studies on the expression, prognosis, and mechanism of ALG13 in Pca are still lacking, and the relationship between ALG13 and tumor immune infiltration in Pca remains unknown. In this study, we analyzed the expression and survival of ALG13 in PRAD. The data sets were extracted and analyzed from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Tumor Immune Estimation Resource (TIMER), and the Human Protein Atlas (HPA) to explore the potential carcinogenic mechanism of ALG13, including the relationship between ALG13 and the prognosis of PRAD, immune cell infiltration, immune cell biomarker, or immune-checkpoint. Gene set enrichment analysis (GSEA) were applied to investigate the potential function of ALG13. Finally, we explored ALG13-related noncoding RNA (ncRNA) regulation in PRAD, including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs). Result Expression of ALG13 in in different tumors and in PRAD patients. The pan-cancer analysis showed that ALG13 was differentially expressed in 13 cancers and was highly expressed in 11 cancers, including bile duct cancer (CHOL), colon cancer (COAD), head and neck cancer (HNSC), kidney clear cell carcinoma (KIRC), Stomach Cancer (STAD)( Fig. 1 a). In evaluating the ALG13 expression in PRAD and normal tissues, we found that ALG13 was overexpressed in PRAD compared with paired and unpaired normal tissues (Fig. 1 b, c). We further verified the expression of ALG13 in PRAD tumor tissues using GEO database, and the result was consistent with our findings. ALG13 was also highly expressed in PRAD tumor tissues (Fig. 1 d). Finally, in evaluating the ALG13 expression at the protein level, we obtained and analyzed the IHC results from the HPA database. The result showed that the staining intensity of ALG13-IHC was high or median in PRAD tumor tissues and low in normal prostate tissues, which indicated that ALG13 was highly expressed in PRAD tumor tissues (Fig. 2 ). Relationship between ALG13 Expression and Clinicopathologic Variable. Through the analysis of TCGA clinical data, we found that the high expression of ALG13 was only significantly correlated with pathological T stage( Supplementary Table 1). The expression of ALG13 was related to Clinical T stage, N stage, disease-specific survival (DSS) event and progression-free interval (PFI) event, the rest were not correlated with ALG13 expression (Fig. 3 a-i). Prognostic values of ALG13 in PRAD. Next, the survival of ALG13 in PRAD was analyzed, including OS, DFS and PFI. High ALG13 expression indicates poor OS and DSS (Fig. 4 a, b), whereas PFI showed no statistical significance for the prognosis of patients (Fig. 4 c). Furthermore, we found that high expression of ALG13 significantly affected OS and DSS in PRAD patients with Gleason score 6, 7, 8, 9 and 10, Pathologic T2, T3, and T4, M0 and M1(Fig. 4 d-j). Relationship between ALG13 and PRAD immune infiltration level. We used CIBERSORT for immune assessment to explore the role of immune cell infiltration in PRAD. ALG13 was divided into high- and low-expression groups. The results showed that B cell memory, regulatory T cells (Tregs), NK cell activation, macrophage M1, macrophage M2, myeloid dendritic cell resting, and myeloid dendritic cell activation were significantly correlated with ALG13 expression in PRAD tissues (Fig. 5 a, b). In investigating the relationship between ALG13 and immune infiltration, ALG13 with different copy numbers is associated with CD8 T + cells, macrophages, and neutrophil invasion level in PRAD (Fig. 5 c). The ESTIMATE algorithm showed that the high-ALG13 group had lower stromal score, immune score and ESTIMATE score than the low-ALG13 group(Fig. 5 d). We further revealed the correlation of ALG13 with infiltrating levels of six immune cell subtypes, including dendritic cells, neutrophils, B cells, CD8 + T cells, macrophages, and CD4 + T cells. The result showed that the ALG13 expression was significantly correlated with the infiltration of B cells, CD4 + T cells, CD8 + T cells, macrophages, neutrophils, and dendritic cells (Fig. 5 e). Relationship between ALG13 and immune-checkpoint and key immune-related genes. We divided ALG13 into high- and low-expression groups and observed the expression of immune-checkpoints, including SIGLEC15, TIGIT, CTLA4, CD274, HAVCR2, LAG3, PDCD1, and PDCD1LG2. The results showed that CD274, PDCD1LG2, and SIGLEC15 were upregulated in PRAD patients with high ALG13 expression, whereas LAG3 was downregulated in PRAD patients with low ALG13 expression. In addition, no difference in other immune-checkpoints was observed (Fig. 6 a). Then, we used the TIDE algorithm to predict the potential immunotherapy response of different ALG13 expression levels. The results showed that the TIDE score of the low-expression group was higher than that of the high-expression group, and the difference was statistically significant (Fig. 6 b). Therefore, tumor patients with high ALG13 expression may have a better effect on ICB and have a longer survival time than those with low-expression. Next, we performed ALG13 co-expression analysis to explore the relationship between ALG13 expression and tumor immune-related genes, including MHC, chemokines, chemokine receptors, immuno-inhibitors, and immunostimulators. The results showed that ALG13 was positively co-expressed with B2M, HLA − C, HLA − DMB, HLA − G, TAP1, TAP2, and TAPBP among the listed MHC genes Fig. 6 c). ALG13 was significantly associated with nearly half of chemokines (Fig. 6 d). It is also significantly related to most chemokine receptors (Fig. 6 E). Furthermore, most immunostimulators, immuno-inhibitors, and ALG13 were co-expressed (Fig. 6 f-g). Gene set enrichment analysis of ALG13. In studying the potential molecular mechanism of ALG13 in Pca, we applied GSEA pathway analyses. Correlated Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analyses revealed that 3 pathways are connected with the high-ALG13-expression group: “glycosylphosphatidylinositol (GPI)-anchor biosynthesis”, “basal transcription factors”, “ubiquitin-mediated proteolysis”( Fig. 7 a). A total of 40 pathways are related to the low ALG13 expression group, several signaling pathways that are acknowledged to influence the pathological behavior of malignant tumors and are related to immunity were enriched, such as: “Cell adhesion molecules (CAMs) ”, “ribosome”, “extracellular matrix-receptor (ECM-receptor) interaction”, “chemokine signaling pathway”, “oxidative phosphorylation (OXPHOS) ”(Fig. 7 b). These GSEA analyses reveal ALG13-related molecular changes, including phospholipid metabolism and oxidative stress, and they may be involved in immune-related pathways of PRAD TME. Prediction and analysis of upstream miRNAs of ALG13. Salmena et al. 15 first proposed the competitive endogenous RNA (ceRNA) hypothesis, which described that ceRNAs could alter the function of target miRNAs by competing for the common binding site of mRNAs on target miRNAs. In determining whether ALG13 is regulated by some upstream miRNAs, we used the miRWalk database to predict the upstream miRNAs that might bind ALG13. Finally, 34 miRNAs were found. Through R and Spearman’s correlation analysis, eight miRNAs were associated with ALG13 in Pca (Fig. 8 a). From the perspective of the mechanism of miRNA regulating the expression of target genes, a negative correlation should be observed between miRNA and ALG13, and miRNA should have a low-expression level in Pca tissues. Based on this analysis, six miRNAs were negatively correlated with ALG13. We continuously used starBase to verify the 34 miRNAs and found that three miRNAs were negatively related to ALG13, which overlapped with the results analyzed by R (Fig. 8 b). In addition, only has-miR-27a-3p and has-miR-381-3p showed low-expression in PRAD (Fig. 8 c). These results indicated that has-miR-27a-3p and has-miR-381-3p may be potential upstream miRNAs of ALG13 in PRAD. Prediction and analysis of upstream lncRNAs of has-miR-27a-3p and has-miR-381-3p. Subsequently, starBase was used to predict the upstream lncRNAs of has-miR-27a-3p and has-miR-381-3p. A total of 64 lncRNAs were related to has-miR-381-3p. We constructed the lncRNA-has-miR-381-3p regulatory network using cytoscape for visualization (Fig. 9 a). Then, UALCAN was used to detect the expression level and prognosis of these lncRNAs in PRAD, and the results showed that only AL390728.6, KCNQ1OT1, and AC012645.1 were significantly upregulated compared with normal tissues, and patients with PRAD had poor OS (Fig. 9 b). Based on the ceRNA hypothesis, lncRNA should be negatively correlated with miRNA and positively correlated with mRNA. We detected the expression correlation between three lncRNAs and has-miR-381-3p or ALG13 in PRAD and found that only AL390728.6 was negatively related to has-miR-381-3p and positively correlated with ALG13 by using starBase (Table 1 ). Next, we used the same method to predict the upstream lncRNA of has-miR-27a-3p and found a total of 154 lncRNAs. Similarly, we established the lncRNA-has-miR-27a-3p regulatory network for visualization (Fig. 9 c). Based on the ULACAN database, six lncRNAs with poor OS were upregulated in PRAD, including LINC01355, PSMD6-AS2, PVT1, KCNQ1OT1, AL162171.3, and Z83843.1 (Fig. 9 d). They were all positively correlated with ALG13 but not negatively correlated with has-miR-27a-3p as detected by starBase (Table 2 ). Therefore, ALG13 is likely regulated by the AL390728.6/has-miR-381-3p axis to play a role in Pca PRAD. Table 1 Correlation analysis between lncRNA and has-miR-381-3p or lncRNA and ALG13 in PRAD determined by starBase. lncRNA miRNA R value P value AL390728.6 has-miR-381-3p −0.124 5.76E-03 KCNQ1OT1 has-miR-381-3p 0.033 4.60E-01 AC012645.1 has-miR-381-3p −0.115 1.06E-02 lncRNA mRNA R value P value AL390728.6 ALG13 0.433 3.51E-24 KCNQ1OT1 ALG13 0.39 1.40E-19 AC012645.1 ALG13 −0.033 4.65E-01 Table 2 Correlation analysis between lncRNA and has-miR-27a-3p or lncRNA and ALG13 in PRAD determined by starBase. lncRNA miRNA R value P value LINC01355 hsa-miR–27a–3p −0.051 2.59E-01 PSMD6-AS2 hsa-miR–27a–3p 0.087 5.25E-02 PVT1 hsa-miR–27a–3p −0.048 2.84E-01 KCNQ1OT1 hsa-miR–27a–3p 0.086 5.54E-02 AL162171.3 hsa-miR–27a–3p 0.021 6.34E-01 Z83843.1 hsa-miR–27a–3p 0.066 1.43E-01 lncRNA mRNA R value P value LINC01355 ALG13 0.497 1.53E-32 PSMD6-AS2 ALG13 0.367 2.23E-17 PVT1 ALG13 0.148 9.10E-04 KCNQ1OT1 ALG13 0.39 1.40E-19 AL162171.3 ALG13 0.418 1.53E-22 Z83843.1 ALG13 0.55 7.30E-41 Discussion Prostate cancer is a common tumor of the male genitourinary system worldwide. The popularity of prostate-specific antigen (PSA) screening has led to an increase in the number of men undergoing prostate biopsies and diagnosed with early-stage Pca 16 , 17 . Given the lack of specificity in the PSA test, a large number of prostate biopsies were negative 18 , 19 . In addition, unnecessary biopsies may lead to overdiagnosis of clinically unimportant tumors 20 . Although most patients with Pca are diagnosed with inertia or slow progression, about 20% of patients are at risk of developing potentially high-risk and fatal diseases during the course of the disease 21 . Therefore, finding promising prognostic biomarkers or effective treatment targets is necessary for the diagnosis and treatment of Pca. ALG13 is highly conserved in most eukaryotes, and it belongs to the OTU family 5 . Evidence suggests that ALG13 plays an important role in human diseases, including malignant tumors. However, the role of ALG13 in PRAD and its related biological functions in Pca remains unknown. In this study, we initially used TCGA-PRAD data to analyze the expression of ALG13 in PRAD and then the GEO database to verify the expression of ALG13. We found that ALG13 was highly expressed in PRAD tumor tissues, and the protein expression of ALG13 was significantly higher than that in normal tissues based on the HPA database. Furthermore, we found that high ALG13 expression was related to poor OS and DSS in patients with PRAD. Different types of immune cells play a key regulatory role in TME. An increasing number of evidence shows that the interaction between cancer cells and various components of TME promotes tumor immune escape, thereby leading to tumor proliferation, recurrence, and metastasis. Although immunotherapy has made some breakthroughs in cancer treatment, its successful application still faces many challenges 22 , 23 . In particular, “cold” tumors such as Pca has little success in responding to various immune-related treatments 3 , 24 , 25 . Sipuleucel-T is the only immunotherapeutic drug approved by FDA for Pca 26 . Therefore, the identification of new targets and biomarkers is critical to the development of immunotherapy for Pca. However, the mechanism by which ALG13 participates in TME and affects TIICs in Pca remains to be explored. we divided ALG13 into high- and low-expression groups for CIBERSORT analysis to evaluate the proportion of TIICs in PRAD. The expression of ALG13 in PRAD is related to B cell memory, Tregs, NK cell activation, macrophage M1, macrophage M2, myeloid dendritic cell resting, and myeloid dendritic cell activation. Next, we used the TIMER database to study whether ALG13 expression in PRAD was related to TIICs. The results showed that ALG13 was positively correlated with B cells, CD8 + T cells, macrophages, neutrophils, and dendritic cells and negatively correlated with CD4 + T cells. These results suggested that ALG13 may be involved in immune infiltration. In addition, we found that PRAD patients with high ALG13 expression had higher expression at CD274, PDCD1LG2, and SIGLEC15, whereas those with low ALG13 expression had higher expression at LAG3. Combined with ICB analysis, patients with high ALG13 expression had better response to ICB. Finally, we confirmed that ALG13 is co-expressed with several key immune regulatory genes, such as MHC genes, immune-stimulators, immuno-inhibitors, chemokines, and chemokine receptors, indicating that ALG13 may be a potential target for immunotherapy. Furthermore, applied GESA analysis to identify the potential molecular mechanisms and biological functions of ALG13 in promoting PRAD. Enrichment analysis showed that ALG13 affected tumor development through a variety of cancer and immune-related pathways. In the high ALG13 expression group, we enriched the “ubiquitin mediated proteolysis” signaling pathway, the ubiquitin proteasome pathway is the most significant intracellular proteolytic pathway 27 , which involved in almost all cellular processes, such as DNA repair, transcription, autophagy, cell cycle progression, the unfolded protein response, apoptosis, endocytosis, protein-protein interaction, intracellular trafficking, inflammatory signaling 27 – 30 . Another pathway related to tumor and immunity is the chemokine signaling pathway. Chemokine is a small, secretory, and structurally related family of cytokines that play an important role in inflammation and immunity 31 , which could directly affect the proliferation and metastasis of cancer cells 32 , 33 . We have also enriched ECM-receptor interaction, and specific interactions between ECM and cells are mediated by transmembrane molecules, including integrins, proteoglycans, and other cell surface-related components. These interactions can lead to malignant biological behaviors of tumor cells, such as adhesion, migration, proliferation, and apoptosis 34 , 35 . OXPHOS, and ribosome pathways were significantly enriched in the low ALG13 expression group. The increase of OXPHOS is associated with drug resistance to chemotherapy. In Pca, OXPHOS inhibition can address resistance to docetaxel 36 while responding to the immune response 37 . Oncogenes or the loss of tumor suppressor genes leads to the hyperactivation of ribosome biogenesis, which plays a key role in cancer initiation and progression 38 , 39 . In addition, the relationship between ribosome abundance and cell intrinsic immunity has been confirmed 40 . These pathways are widely involved in the occurrence and development of tumors, indicating that changes in ALG13 expression level play an important role in the occurrence and development of PRAD, and that ALG13 was involved in immune-related pathways in the TME of PRAD. However, the mechanism of these signal pathways involved in the regulation of PRAD by ALG13 remains to be further studied and verified. At present, numerous studies have indicated that lncRNA can be used as ceRNA to regulate the expression of target mRNA by sponge-adsorbing miRNA and jointly regulate tumor progression 41 – 44 . In exploring the upstream miRNA that regulates ALG13, we used the miRWalk database to predict the potential upstream miRNA of ALG13. After correlation, expression, and survival analysis, we screened out hsa-miR-27a-3p and hsa-miR-381-3p as ALG13 upstream miRNA to inhibit tumor progression. A previous study has also shown that hsa-miR-27a-3p has an inhibitory effect on the proliferation and migration of esophageal cancer 45 . hsa-miR-27a-3p over-expression can also inhibit the progression of glioblastoma 46 , 47 . Moreover, has-miR-381-3p was upregulated in the serum exosomes of patients with parathyroid tumor 48 . However, the expression was low in breast cancer, endometrial cancer, and ovarian cancer 49 . Next, we predicted the upstream lncRNA of the has-miR-27a-3p and has-miR-381-3p/ALG13 axes. Based on expression, survival, and correlation analyses, lncRNA was associated with hsa-miR-27a-3p and positively correlated with ALG13 in PRAD but not or negatively related to hsa-miR-27a-3p. Among the lncRNAs associated with hsa-miR-381-3p, only AL390728.6 in PRAD was negatively correlated with hsa-miR-381-3p and positively correlated with ALG13. At present, AL390728.6 was highly expressed in hepatocellular carcinoma, and it may be used as a potential biomarker 50 . Therefore, the AL390728.6/hsa-miR-381-3p/ALG13 axis was considered to be a potential pathway for regulating PRAD progression. However, this study still has some limitations because it is based on public data and assumptions to generate forecasts. First, more clinical samples are needed to verify the high-expression of ALG13 in PRAD. Second, basic experiments are needed to verify the exact role of ALG13-related pathways and AL390728.6/hsa-miR-381-3p/ALG13 axes in PRAD. Finally, the mechanism of ALG13 regulating immune cell infiltration remains to be further studied. We clarified that the increased expression of ALG13 in PRAD tumor tissues was positively correlated with poor OS of patients with PRAD. ALG13 may affect tumor development by regulating TIICs in TME, and it may be a potential target for immunotherapy. In addition, we found the upstream regulation mechanism of ALG13 in PRAD, namely, AL390728.6/hsa-miR-381-3p/ALG13 axis. Methods Data source and analysis of differential expressions. We downloaded cancer-related RNA sequences as well as clinicopathological and survival data of PRAD from the Cancer Genome Atlas (TCGA) database( https://portal.gdc.cancer.gov/ ). We also downloaded 33 tumors RNA-sequencing expression (level 3) profiles from the TCGA dataset( https://portal.gdc.com ) for pan-caner analysis. R v4.0.3 was used for statistical analysis. The Wilcoxon test was used to evaluate the differential mRNA expression of ALG13 in PRAD, and P value < 0.05 was considered as statistically significant. We downloaded GSE32571 gene expression profiling data from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ) to verify the expression of ALG13 in PRAD, which containing 59 Pca and 39 benign tissue samples 51 . The raw data were downloaded as MINiML files. Box plots are drawn by boxplot. Immunohistochemistry (IHC) staining. We performed immunohistochemical image analysis using the HPA ( http://www.proteinatlas.org/ ) to assess the differential expression of ALG13 in PRAD and normal tissues at the protein level. The HPA043684 antibody was used for IHC. Prognostic analysis of ALG13. We extracted the survival information of each sample in TCGA. Then, we selected several indexes, including OS, DSS, and PFI, to clarify the relationship between the expression of ALG13 and the prognosis of patients with PRAD. Survival analysis of PRAD was performed by using the Kaplan–Meier method and evaluated by R package “survival” and “survminer” packages. Then, Cox analysis was performed using R packet “survival” to determine the correlation between ALG13 expression and survival. Relationship between ALG13 and immune cell infiltration in the tumor microenvironment (TME) of PRAD. TIMER ( https://cistrome.shinyapps.io/timer/ ) is a web server for comprehensive analysis of the abundance of tumor-infiltrating immune cells (TIICs) across diverse cancer types 52 . The TIICs in TIMER included B cells, CD4 + T cells, CD8 + T cells, macrophages, neutrophils, and dendritic cells. In assessing the reliable results of immune score evaluation, we used “immuneeconv.” It is an R software package that integrates six latest algorithms, including TIMER, xCell, MCP-counter, CIBERSORT, EPIC, and quanTIseq. These algorithms had been benchmarked, and each algorithm had a unique advantage. Subsequently, we applied the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression (ESTIMATE) algorithm 53 to calculate and compare stromal scores, immune scores, and ESTIMATE scores between high and low ALG13 expression groups of PRAD samples. In this study, we used the R software packages “ggplot” and “ggpubr” to visualize the analysis results. Immune-checkpoint and immuno-regulatory factor analysis. We divided ALG13 into the high-expression and low-expression groups and then extracted the expression data of SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2 genes associated with immune-checkpoint. The R packages “ggplot2” and “immuneeconv” were used to evaluate the expression of ALG13 at different levels in PRAD. Subsequently, we predicted the potential immune-checkpoint blockade (ICB) response based on the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. TIDE used a set of gene expression markers to evaluate two different mechanisms of tumor immune escape, including dysfunction of tumor-infiltrating cytotoxic T lymphocytes (CTL) and rejection of CTL caused by immunosuppressive factors, high TIDE score, poor efficacy of ICB, and short survival after ICB treatment 54 . In addition, we analyzed the relationship between ALG13 and immunomodulators, including chemokines, chemokine receptors, immune-inhibitors, immune-stimulators, and MHC molecules, and used the R packages “pheatmap” and “immuneeconv” to evaluate the expression of immunomodulators and co-expression of ALG13 and immune genes. Gene set enrichment analysis (GSEA). In exploring the biological signaling pathway, GSEA was performed in the high- and low-expression groups compared with the median level of ALG13 expression. GSEA 4.2.3 was also used to perform this analysis. “c2.cp.kegg.v2022.1.Hs.symbols.gmt” from the MSigDB gene set were selected as the reference gene set. Gene sets with |NES|>1, NOM P < 0.05, and FDR q < 0.25 were considered significant 55 . ALG13 upstream miRNA and lncRNA prediction. Upstream binding miRNAs of ALG13 were predicted by the miRWalk database (mirwalk.umm.uni-heidelberg.de). We selected the first 100 miRNAs whose miRBD program value is “0”. These predicted miRNAs were regarded as candidate miRNAs of ALG13. The correlation between filtered miRNAs and ALG13 was demonstrated by R package “pheatmap,” and their correlation with ALG13 was verified by starBase. Then, starBase, which is a database for exploring miRNA-related studies 56 , was used to predict upstream lncRNAs associated with candidate miRNAs. In verifying the differential expression and prognosis of ncRNAs associated with ALG13 in PRAD and normal tissues, we further verified our results using the University of ALabama at Birmingham Caner Data Analysis Portal (UALCAN, http://ualcan.path.uab.edu ) database 57 , 58 and constructed boxplot and survival curves. Statistical analysis. Gene expression data were normalized by log2-transformation. Normal and tumor tissues were compared using a two-group wilcox-test. The relationship between prognosis and ALG13 expression was presented using the Kaplan–Meier survival curve and compared using a log-rank test. Continuous variables were described as mean ± SD. The Cox proportional hazards regression model was used for univariate and multivariate analyses. The correlation between ALG13 expression and abundance scores of immune cells was evaluated by Spearman or Pearson correlation analysis. Furthermore, P value < 0.05 or log-rank P value < 0.05 was considered statistically significant. R (version 4.0.3) was used for statistical analysis. Declarations Acknowledgements We are sincerely grateful to all the authors in the study. At the same time, we would like to express our sincere respect to the founders and data collectors of TCGA, GEO, HPA, TIMER, GSEA, UALCAN and other online databases. It is precisely because of such convenient biological information platform that clinical researchers have such a broad platform to learn and complete corresponding scientific research like us. Author contributions M.X. and Z.Y. designed and supervised the study, M.X. analyzed data and wrote the manuscript, Y.X. collected the data, W.L. and X.X. processed the figures and tables. All authors read and approved the final manuscript. Data availability statement All data generated or analysed for this study are included in this article. Further details are available from the corresponding author upon request. Patient data that supported the findings of this study are available in The Cancer Genome Atlas (TCGA) datasets at https://portal.gdc.cancer.gov/repository.The datasets presented in this study were obtained from the GEO (https://www.ncbi.nlm.nih.gov/geo/)(GSE32571, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi/acc.cgi) Competing interests This paper relates to no conflict of interest. References Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer statistics, 2022. CA Cancer J Clin 72, 7–33, doi: 10.3322/caac.21708 (2022). Xia, C. et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J (Engl) 135, 584–590, doi: 10.1097/CM9.0000000000002108 (2022). Laccetti, A. L. & Subudhi, S. K. Immunotherapy for metastatic prostate cancer: immuno-cold or the tip of the iceberg? Curr Opin Urol 27, 566–571, doi: 10.1097/MOU.0000000000000433 (2017). Mevissen, T. E. et al. OTU deubiquitinases reveal mechanisms of linkage specificity and enable ubiquitin chain restriction analysis. Cell 154, 169–184, doi: 10.1016/j.cell.2013.05.046 (2013). Wang, X., Weldeghiorghis, T., Zhang, G., Imperiali, B. & Prestegard, J. H. Solution structure of Alg13: the sugar donor subunit of a yeast N-acetylglucosamine transferase. Structure 16, 965–975, doi: 10.1016/j.str.2008.03.010 (2008). Averbeck, N., Gao, X. D., Nishimura, S. & Dean, N. Alg13p, the catalytic subunit of the endoplasmic reticulum UDP-GlcNAc glycosyltransferase, is a target for proteasomal degradation. Mol Biol Cell 19, 2169–2178, doi: 10.1091/mbc.e07-10-1077 (2008). Gao, X. D., Moriyama, S., Miura, N., Dean, N. & Nishimura, S. Interaction between the C termini of Alg13 and Alg14 mediates formation of the active UDP-N-acetylglucosamine transferase complex. J Biol Chem 283, 32534–32541, doi: 10.1074/jbc.M804060200 (2008). Bissar-Tadmouri, N. et al. X chromosome exome sequencing reveals a novel ALG13 mutation in a nonsyndromic intellectual disability family with multiple affected male siblings. Am J Med Genet A 164A, 164–169, doi: 10.1002/ajmg.a.36233 (2014). de Ligt, J. et al. Diagnostic exome sequencing in persons with severe intellectual disability. N Engl J Med 367, 1921–1929, doi: 10.1056/NEJMoa1206524 (2012). Timal, S. et al. Gene identification in the congenital disorders of glycosylation type I by whole-exome sequencing. Hum Mol Genet 21, 4151–4161, doi: 10.1093/hmg/dds123 (2012). Selamat, S. A. et al. Genome-scale analysis of DNA methylation in lung adenocarcinoma and integration with mRNA expression. Genome Res 22, 1197–1211, doi: 10.1101/gr.132662.111 (2012). Yamagata, N. et al. A training-testing approach to the molecular classification of resected non-small cell lung cancer. Clin Cancer Res 9, 4695–4704 (2003). Deng, J., Hou, G., Fang, Z., Liu, J. & Lv, X. D. Distinct expression and prognostic value of OTU domain-containing proteins in non-small-cell lung cancer. Oncol Lett 18, 5417–5427, doi: 10.3892/ol.2019.10883 (2019). De Antonellis, P. et al. Early targets of miR-34a in neuroblastoma. Mol Cell Proteomics 13, 2114–2131, doi: 10.1074/mcp.M113.035808 (2014). Salmena, L., Poliseno, L., Tay, Y., Kats, L. & Pandolfi, P. P. A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell 146, 353–358, doi: 10.1016/j.cell.2011.07.014 (2011). Hugosson, J. et al. A 16-yr Follow-up of the European Randomized study of Screening for Prostate Cancer. Eur Urol 76, 43–51, doi: 10.1016/j.eururo.2019.02.009 (2019). Schroder, F. H. et al. Screening for prostate cancer decreases the risk of developing metastatic disease: findings from the European Randomized Study of Screening for Prostate Cancer (ERSPC). Eur Urol 62, 745–752, doi: 10.1016/j.eururo.2012.05.068 (2012). Cucchiara, V. et al. Genomic Markers in Prostate Cancer Decision Making. Eur Urol 73, 572–582, doi: 10.1016/j.eururo.2017.10.036 (2018). Thompson, I. M. et al. Prevalence of prostate cancer among men with a prostate-specific antigen level < or = 4.0 ng per milliliter. N Engl J Med 350, 2239–2246, doi: 10.1056/NEJMoa031918 (2004). Hamdy, F. C. et al. 10-Year Outcomes after Monitoring, Surgery, or Radiotherapy for Localized Prostate Cancer. N Engl J Med 375, 1415–1424, doi: 10.1056/NEJMoa1606220 (2016). Chang, A. J., Autio, K. A., Roach, M., 3rd & Scher, H. I. High-risk prostate cancer-classification and therapy. Nat Rev Clin Oncol 11, 308–323, doi: 10.1038/nrclinonc.2014.68 (2014). Murciano-Goroff, Y. R., Warner, A. B. & Wolchok, J. D. The future of cancer immunotherapy: microenvironment-targeting combinations. Cell Res 30, 507–519, doi: 10.1038/s41422-020-0337-2 (2020). Zhang, Y. & Zhang, Z. The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications. Cell Mol Immunol 17, 807–821, doi: 10.1038/s41423-020-0488-6 (2020). Bilusic, M., Madan, R. A. & Gulley, J. L. Immunotherapy of Prostate Cancer: Facts and Hopes. Clin Cancer Res 23, 6764–6770, doi: 10.1158/1078-0432.CCR-17-0019 (2017). Vitkin, N., Nersesian, S., Siemens, D. R. & Koti, M. The Tumor Immune Contexture of Prostate Cancer. Front Immunol 10, 603, doi: 10.3389/fimmu.2019.00603 (2019). Comiskey, M. C., Dallos, M. C. & Drake, C. G. Immunotherapy in Prostate Cancer: Teaching an Old Dog New Tricks. Curr Oncol Rep 20, 75, doi: 10.1007/s11912-018-0712-z (2018). Yerlikaya, A. & Yontem, M. The significance of ubiquitin proteasome pathway in cancer development. Recent Pat Anticancer Drug Discov 8, 298–309, doi: 10.2174/1574891x113089990033 (2013). Grabbe, C., Husnjak, K. & Dikic, I. The spatial and temporal organization of ubiquitin networks. Nat Rev Mol Cell Biol 12, 295–307, doi: 10.1038/nrm3099 (2011). Rieser, E., Cordier, S. M. & Walczak, H. Linear ubiquitination: a newly discovered regulator of cell signalling. Trends Biochem Sci 38, 94–102, doi: 10.1016/j.tibs.2012.11.007 (2013). Yin, Z., Popelka, H., Lei, Y., Yang, Y. & Klionsky, D. J. The Roles of Ubiquitin in Mediating Autophagy. Cells 9, doi: 10.3390/cells9092025 (2020). Griffith, J. W., Sokol, C. L. & Luster, A. D. Chemokines and chemokine receptors: positioning cells for host defense and immunity. Annu Rev Immunol 32, 659–702, doi: 10.1146/annurev-immunol-032713-120145 (2014). Caronni, N. et al. Cancer and Chemokines. Methods Mol Biol 1393, 87–96, doi: 10.1007/978-1-4939-3338-9_8 (2016). Chow, M. T. & Luster, A. D. Chemokines in cancer. Cancer Immunol Res 2, 1125–1131, doi: 10.1158/2326-6066.CIR-14-0160 (2014). Marsico, G., Russo, L., Quondamatteo, F. & Pandit, A. Glycosylation and Integrin Regulation in Cancer. Trends Cancer 4, 537–552, doi: 10.1016/j.trecan.2018.05.009 (2018). Najafi, M., Farhood, B. & Mortezaee, K. Extracellular matrix (ECM) stiffness and degradation as cancer drivers. J Cell Biochem 120, 2782–2790, doi: 10.1002/jcb.27681 (2019). Bosc, C., Selak, M. A. & Sarry, J. E. Resistance Is Futile: Targeting Mitochondrial Energetics and Metabolism to Overcome Drug Resistance in Cancer Treatment. Cell Metab 26, 705–707, doi: 10.1016/j.cmet.2017.10.013 (2017). Boreel, D. F., Span, P. N., Heskamp, S., Adema, G. J. & Bussink, J. Targeting Oxidative Phosphorylation to Increase the Efficacy of Radio- and Immune-Combination Therapy. Clin Cancer Res 27, 2970–2978, doi: 10.1158/1078-0432.CCR-20-3913 (2021). Orsolic, I. et al. The relationship between the nucleolus and cancer: Current evidence and emerging paradigms. Semin Cancer Biol 37–38, 36–50, doi: 10.1016/j.semcancer.2015.12.004 (2016). Truitt, M. L. & Ruggero, D. New frontiers in translational control of the cancer genome. Nat Rev Cancer 16, 288–304, doi: 10.1038/nrc.2016.27 (2016). Bianco, C. & Mohr, I. Ribosome biogenesis restricts innate immune responses to virus infection and DNA. Elife 8, doi: 10.7554/eLife.49551 (2019). Chen, R. et al. LncRNA UCC promotes epithelial-mesenchymal transition via the miR-143-3p/SOX5 axis in non-small-cell lung cancer. Lab Invest 101, 1153–1165, doi: 10.1038/s41374-021-00586-6 (2021). Guo, R. et al. LncRNA RCAT1 promotes tumor progression and metastasis via miR-214-5p/E2F2 axis in renal cell carcinoma. Cell Death Dis 12, 689, doi: 10.1038/s41419-021-03955-7 (2021). Lin, J. et al. LncRNA FGD5-AS1 accelerates cell proliferation in pancreatic cancer by regulating miR-520a-3p/KIAA1522 axis. Cancer Biol Ther 22, 257–266, doi: 10.1080/15384047.2021.1883184 (2021). Yang, A. et al. LncRNA UCA1 promotes development of gastric cancer via the miR-145/MYO6 axis. Cell Mol Biol Lett 26, 33, doi: 10.1186/s11658-021-00275-8 (2021). Lu, M. et al. Novel Immune-Related Ferroptosis Signature in Esophageal Cancer: An Informatics Exploration of Biological Processes Related to the TMEM161B-AS1/hsa-miR-27a-3p/GCH1 Regulatory Network. Front Genet 13, 829384, doi: 10.3389/fgene.2022.829384 (2022). Chen, Q. et al. Over-expression of lncRNA TMEM161B-AS1 promotes the malignant biological behavior of glioma cells and the resistance to temozolomide via up-regulating the expression of multiple ferroptosis-related genes by sponging hsa-miR-27a-3p. Cell Death Discov 7, 311, doi: 10.1038/s41420-021-00709-4 (2021). Salmani, T. et al. Hsa-miR-27a-3p and epidermal growth factor receptor expression analysis in glioblastoma FFPE samples. Asia Pac J Clin Oncol 17, e185-e190, doi: 10.1111/ajco.13399 (2021). Wang, J. et al. Expression profile of serum-related exosomal miRNAs from parathyroid tumor. Endocrine 72, 239–248, doi: 10.1007/s12020-020-02535-7 (2021). Pane, K. et al. Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB. Biomedicines 10, doi: 10.3390/biomedicines10061306 (2022). Tang, Y. et al. Identification of Hypoxia-Related Prognostic Signature and Competing Endogenous RNA Regulatory Axes in Hepatocellular Carcinoma. Int J Mol Sci 23, doi: 10.3390/ijms232113590 (2022). Kuner, R. et al. The maternal embryonic leucine zipper kinase (MELK) is upregulated in high-grade prostate cancer. J Mol Med (Berl) 91, 237–248, doi: 10.1007/s00109-012-0949-1 (2013). Li, T. et al. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res 77, e108-e110, doi: 10.1158/0008-5472.CAN-17-0307 (2017). Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 4, 2612, doi: 10.1038/ncomms3612 (2013). Jiang, P. et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med 24, 1550–1558, doi: 10.1038/s41591-018-0136-1 (2018). Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545–15550, doi: 10.1073/pnas.0506580102 (2005). Li, J. H., Liu, S., Zhou, H., Qu, L. H. & Yang, J. H. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 42, D92-97, doi: 10.1093/nar/gkt1248 (2014). Chandrashekar, D. S. et al. UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia 19, 649–658, doi: 10.1016/j.neo.2017.05.002 (2017). Chandrashekar, D. S. et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia 25, 18–27, doi: 10.1016/j.neo.2022.01.001 (2022). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx 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-2680822","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":184095429,"identity":"90471f97-c861-479d-918a-aeba795cf083","order_by":0,"name":"Maolin Xiao","email":"","orcid":"","institution":"Chongqing General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maolin","middleName":"","lastName":"Xiao","suffix":""},{"id":184095430,"identity":"b3bb6425-4f52-4a47-8cda-c5a885b379cd","order_by":1,"name":"Yunfeng Xiao","email":"","orcid":"","institution":"North Sichuan Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yunfeng","middleName":"","lastName":"Xiao","suffix":""},{"id":184095431,"identity":"432c2405-4d00-4a07-9f0f-db78b7188cfe","order_by":2,"name":"Wanlan Liu","email":"","orcid":"","institution":"Chongqing General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wanlan","middleName":"","lastName":"Liu","suffix":""},{"id":184095433,"identity":"de552140-8614-47fc-a2e3-2e883d507037","order_by":3,"name":"Xiao Xiao","email":"","orcid":"","institution":"Chongqing General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Xiao","suffix":""},{"id":184095435,"identity":"b2f825e0-e257-4823-836b-f86cbfca5ab4","order_by":4,"name":"Zongke Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDADCQbmAwc+/CBNC1viwZk9pGnhMT7MwUaESoPjZw+//FFxx25me8+Hwww8DPL8YgcIaDmTl2YhceZZ8myesxsOF1gwGM6cnUBAy4EcMwPDtsPJchK5Gw7P4GFIMLhNSMv5N2YGiSAt8m8eHOZhI0bLjRzjBwfbDttJS/AwEKdF8sYbM8aGM4cTJHvSDICBLEHYL3znc4w//qg4bC9x/PDjDx9+2MjzSxPQonCAgU0CSCc2QPgS+JWDgHwDA/MHIG1PWOkoGAWjYBSMWAAAkq9MRscyss8AAAAASUVORK5CYII=","orcid":"","institution":"Dianjiang People’s Hospital of Chongqing","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zongke","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2023-03-11 10:44:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2680822/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2680822/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":34528743,"identity":"ce1a80fb-8dde-4a50-bebd-dc3a9bc728bf","added_by":"auto","created_at":"2023-03-20 15:10:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":177131,"visible":true,"origin":"","legend":"\u003cp\u003eALG13 expression status in tumors. (a) mRNA expression of ALG13 in different types of tumor tissues and normal tissues based on the TCGA database. (b) mRNA expression of ALG13 in PRAD and unpaired normal tissues. (c) mRNA expression of ALG13 in PRAD and paired normal tissues. (d) mRNA expression of ALG13 in PRAD tissues and normal tissues based on the GEO database. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2680822/v1/e490cdea35ba04e416ba6ee6.png"},{"id":34530605,"identity":"df346fb9-e094-47a0-bc58-3c679dc98ba3","added_by":"auto","created_at":"2023-03-20 15:26:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":467556,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression level of ALG13 in PRAD tissue and benign prostate hyperplasia (BPH) tissue based on the HPA database. (a-c): PRAD tissue, (d-f): BPH tissue.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2680822/v1/9d67ff9f511a5c1a66619a30.png"},{"id":34528747,"identity":"f9fe7bb1-005e-44fc-b93d-00583b0d74df","added_by":"auto","created_at":"2023-03-20 15:10:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":107377,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between ALG13 Expression and Clinicopathologic Variables. Data are shown for (a) clinical T stage; (b) N stage; (c) M stage; (d) pathological T stage; (e) Gleason score; (f) PSA(ng/ml); (g) OS event; (h) DSS event; (i) PFI event.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2680822/v1/493879a9577aae128d37a7e4.png"},{"id":34529759,"identity":"8a66bcce-547f-437e-926d-df0ef29cb9d8","added_by":"auto","created_at":"2023-03-20 15:18:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":177476,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves comparing high and low expression of ALG13 in PRAD. (a) OS; (b) DSS; (c) PFI. OS and DSS analysis with ALG13 mRNA expression for (d, e) Gleason score: 6\u0026amp;7\u0026amp;8\u0026amp;9\u0026amp;10; (f, g) pathologic stage:T2\u0026amp;T3\u0026amp;T4; (h, i) clinical M stage: M0\u0026amp;M1.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2680822/v1/8270b61d87abe611b63125eb.png"},{"id":34531796,"identity":"0d22ff6e-8dc8-4a5a-af20-ff26d7a61f6b","added_by":"auto","created_at":"2023-03-20 15:34:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":221887,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between the ALG13 expression and immune infltration level in PRAD. (a, b) The relation between ALG 13 expression with 22 common immune cells infiltration analyzed by CIBERSORT. (c) Infiltration level of various immune cells under different copy numbers of ALG13 in PRAD. (d) The ESTIMATE score, immune scores and stromal scores in the high and low ALG13 expression groups analyzed by the ESTIMATE algorithm. (e) Relationship between ALG13 expression levels and immune cell infiltration by TIMER *\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2680822/v1/fd42c74eb436d3eee0e36c6a.png"},{"id":34530610,"identity":"a3421971-2e75-4e8c-87d2-44b16baa1368","added_by":"auto","created_at":"2023-03-20 15:26:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":132827,"visible":true,"origin":"","legend":"\u003cp\u003eCo-expression of ALG13 with immune-related genes and the expression of immune-checkpoints in PRAD. (a) Different responses to immune-checkpoint blockade in high and low ALG13 expression of PRAD. (b) Different responses to immune-checkpoint blockade in high and low ALG13 expression of PRAD. (c) Co-expression of ALG13 with MHC genes, (d) chemokine-related genes, (e) chemokine receptor–related genes, (f) immune-stimulator genes, and (g) immuno-inhibitor genes. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2680822/v1/88d6a51d16a457ae43565228.png"},{"id":34528751,"identity":"3c1502a8-09e7-4643-9155-4511772052d5","added_by":"auto","created_at":"2023-03-20 15:10:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":346110,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA analysis results. (a) Enrichment of pathways in KEGG with high ALG13 expression. (b) Enrichment of pathways in KEGG with low ALG13 expression.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-2680822/v1/1c8c6b448840460e46292542.png"},{"id":34531797,"identity":"dfedd187-a441-473c-8372-9822f2ef3a20","added_by":"auto","created_at":"2023-03-20 15:34:54","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":200791,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of potential miRNA upstream of ALG13. (a) Correlation analysis of 34 miRNAs associated with ALG13 in PRAD. (b) starBase was used to identify ALG13-related mRNA in PRAD, and the mRNA overlapped with the analysis results of R(has-miR-24-1-5p, has-miR-27a-3p and has-miR-381-3p). (c) The expression of has-miR-24-1-5p, has-miR-27a-3p and has-miR-381-3p in PRAD. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-2680822/v1/1c688f7a3e3a5d8799a0e0a4.png"},{"id":34530607,"identity":"c710e759-9141-4237-8e17-b3c4a786890e","added_by":"auto","created_at":"2023-03-20 15:26:54","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":679492,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of potential lncRNA upstream of has-miR-27a-3p and has-miR-381-3p. (a) The lncRNA-has-miR-381-3p regulatory network established by cytoscape. (b) Expression analysis and survival analysis for upstream lncRNAs of has-miR-381-3p by UALCAN. (c) The lncRNA-has-miR-27a-3p regulatory network established by cytoscape. (d) Expression analysis and survival analysis for upstream lncRNAs of has-miR-27a-3p by UALCAN. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-2680822/v1/21df74f039ab7b8d23a08638.png"},{"id":39953331,"identity":"d84d3abb-3f7f-41a0-b418-67060191ca89","added_by":"auto","created_at":"2023-07-13 06:35:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2721555,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2680822/v1/aab66a55-66ec-4ffd-b4c9-7d826a81971c.pdf"},{"id":34531795,"identity":"46dee2f6-0475-4bbe-a96d-68944125a4ed","added_by":"auto","created_at":"2023-03-20 15:34:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17966,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-2680822/v1/76f5e8b591fff2c053668ebf.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"ALG13 as a prognostic biomarker of prostate cancer associated with tumor immune infiltration and mediated by upstream ncRNA","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer (Pca) has the largest number of new cases among male patients worldwide every year, and it is also the second largest cause of tumor-related mortality\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Prostate adenocarcinoma (PRAD) is a common type of Pca. In China, approximately 125,646 new cases of Pca are reported, while about 56,239 patients die because of Pca\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. With the improvement of diagnostic technology and early screening, an increasing number of patients with Pca are diagnosed, but many patients are initially diagnosed with metastatic cancer; thus, they lose the opportunity for surgery. In addition, castrate-resistant prostate cancer appeared after treatment. Pca is a \u0026ldquo;cold\u0026rdquo; tumor, with little success in dealing with various immune-related treatments\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Therefore, biomarkers are urgently needed for the diagnosis of Pca, and exploring immune-related molecular markers is an important focus of Pca research.\u003c/p\u003e \u003cp\u003eAsparagine-linked glycosylation 13 (ALG13) is a putative bifunctional uridine diphospho-N-acetylglucosamine(UDP-GlcNAc) transferase and deubiquitinase, a highly conserved protein in most eukaryotes. ALG13 belongs to the ovarian tumor(OUT) domain-containing protein (OTUDs) family. OTUD is also a subfamily of ovarian tumor-associated proteases (OTUs), and it a subtype of the deubiquitinating enzyme system\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. ALG13 is located on the X chromosome, and it encodes a protein heterodimerized with ALG14, which forms a functional UDP-GlcNAc glycosyltransferase on the endoplasmic reticulum and catalyzes the second step of protein N-glycosylation\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. ALG13 plays an important role in human malignant and nonmalignant diseases. Previous reports implicated that ALG13 gene mutations caused X-linked congenital disorder of glycosylation type I (CDG-I), which is also known as ALG13-CDG\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Similarly, a missense mutation of the X-linked gene ALG13 was found in a child with seizures and early death caused by CDG-1\u003csup\u003e10\u003c/sup\u003e. In tumor diseases, some studies have shown that the expression level of ALG13 mRNA decreased in lung adenocarcinoma and squamous cell carcinoma\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In a recent study, the mRNA expression level of ALG13 decreased in lung cancer tissues compared with normal tissues, including patients with adenocarcinoma and squamous cell carcinoma, and the high-expression level of ALG13mRNA was not significantly correlated with the prognosis of patients with squamous cell carcinoma, but it could predict the overall survival (OS) of patients with adenocarcinoma \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. ALG13 has also been identified as an early target of microRNA-34a, which was associated with the development of neuroblastoma\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, comprehensive studies on the expression, prognosis, and mechanism of ALG13 in Pca are still lacking, and the relationship between ALG13 and tumor immune infiltration in Pca remains unknown.\u003c/p\u003e \u003cp\u003eIn this study, we analyzed the expression and survival of ALG13 in PRAD. The data sets were extracted and analyzed from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Tumor Immune Estimation Resource (TIMER), and the Human Protein Atlas (HPA) to explore the potential carcinogenic mechanism of ALG13, including the relationship between ALG13 and the prognosis of PRAD, immune cell infiltration, immune cell biomarker, or immune-checkpoint. Gene set enrichment analysis (GSEA) were applied to investigate the potential function of ALG13. Finally, we explored ALG13-related noncoding RNA (ncRNA) regulation in PRAD, including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs).\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e \u003cb\u003eExpression of ALG13 in in different tumors and in PRAD patients.\u003c/b\u003e The pan-cancer analysis showed that ALG13 was differentially expressed in 13 cancers and was highly expressed in 11 cancers, including bile duct cancer (CHOL), colon cancer (COAD), head and neck cancer (HNSC), kidney clear cell carcinoma (KIRC), Stomach Cancer (STAD)( Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). In evaluating the ALG13 expression in PRAD and normal tissues, we found that ALG13 was overexpressed in PRAD compared with paired and unpaired normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, c). We further verified the expression of ALG13 in PRAD tumor tissues using GEO database, and the result was consistent with our findings. ALG13 was also highly expressed in PRAD tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFinally, in evaluating the ALG13 expression at the protein level, we obtained and analyzed the IHC results from the HPA database. The result showed that the staining intensity of ALG13-IHC was high or median in PRAD tumor tissues and low in normal prostate tissues, which indicated that ALG13 was highly expressed in PRAD tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRelationship between ALG13 Expression and Clinicopathologic Variable.\u003c/b\u003e Through the analysis of TCGA clinical data, we found that the high expression of ALG13 was only significantly correlated with pathological T stage( Supplementary Table\u0026nbsp;1). The expression of ALG13 was related to Clinical T stage, N stage, disease-specific survival (DSS) event and progression-free interval (PFI) event, the rest were not correlated with ALG13 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-i).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePrognostic values of ALG13 in PRAD.\u003c/b\u003e Next, the survival of ALG13 in PRAD was analyzed, including OS, DFS and PFI. High ALG13 expression indicates poor OS and DSS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, b), whereas PFI showed no statistical significance for the prognosis of patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Furthermore, we found that high expression of ALG13 significantly affected OS and DSS in PRAD patients with Gleason score 6, 7, 8, 9 and 10, Pathologic T2, T3, and T4, M0 and M1(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed-j).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRelationship between ALG13 and PRAD immune infiltration level.\u003c/b\u003e We used CIBERSORT for immune assessment to explore the role of immune cell infiltration in PRAD. ALG13 was divided into high- and low-expression groups. The results showed that B cell memory, regulatory T cells (Tregs), NK cell activation, macrophage M1, macrophage M2, myeloid dendritic cell resting, and myeloid dendritic cell activation were significantly correlated with ALG13 expression in PRAD tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, b). In investigating the relationship between ALG13 and immune infiltration, ALG13 with different copy numbers is associated with CD8 T\u003csup\u003e+\u003c/sup\u003e cells, macrophages, and neutrophil invasion level in PRAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). The ESTIMATE algorithm showed that the high-ALG13 group had lower stromal score, immune score and ESTIMATE score than the low-ALG13 group(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). We further revealed the correlation of ALG13 with infiltrating levels of six immune cell subtypes, including dendritic cells, neutrophils, B cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, macrophages, and CD4\u003csup\u003e+\u003c/sup\u003e T cells. The result showed that the ALG13 expression was significantly correlated with the infiltration of B cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, macrophages, neutrophils, and dendritic cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRelationship between ALG13 and immune-checkpoint and key immune-related genes.\u003c/b\u003e We divided ALG13 into high- and low-expression groups and observed the expression of immune-checkpoints, including SIGLEC15, TIGIT, CTLA4, CD274, HAVCR2, LAG3, PDCD1, and PDCD1LG2. The results showed that CD274, PDCD1LG2, and SIGLEC15 were upregulated in PRAD patients with high ALG13 expression, whereas LAG3 was downregulated in PRAD patients with low ALG13 expression. In addition, no difference in other immune-checkpoints was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Then, we used the TIDE algorithm to predict the potential immunotherapy response of different ALG13 expression levels. The results showed that the TIDE score of the low-expression group was higher than that of the high-expression group, and the difference was statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Therefore, tumor patients with high ALG13 expression may have a better effect on ICB and have a longer survival time than those with low-expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we performed ALG13 co-expression analysis to explore the relationship between ALG13 expression and tumor immune-related genes, including MHC, chemokines, chemokine receptors, immuno-inhibitors, and immunostimulators. The results showed that ALG13 was positively co-expressed with B2M, HLA\u0026thinsp;\u0026minus;\u0026thinsp;C, HLA\u0026thinsp;\u0026minus;\u0026thinsp;DMB, HLA\u0026thinsp;\u0026minus;\u0026thinsp;G, TAP1, TAP2, and TAPBP among the listed MHC genes Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). ALG13 was significantly associated with nearly half of chemokines (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). It is also significantly related to most chemokine receptors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Furthermore, most immunostimulators, immuno-inhibitors, and ALG13 were co-expressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef-g).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGene set enrichment analysis of ALG13.\u003c/b\u003e In studying the potential molecular mechanism of ALG13 in Pca, we applied GSEA pathway analyses. Correlated Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analyses revealed that 3 pathways are connected with the high-ALG13-expression group: \u0026ldquo;glycosylphosphatidylinositol (GPI)-anchor biosynthesis\u0026rdquo;, \u0026ldquo;basal transcription factors\u0026rdquo;, \u0026ldquo;ubiquitin-mediated proteolysis\u0026rdquo;( Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). A total of 40 pathways are related to the low ALG13 expression group, several signaling pathways that are acknowledged to influence the pathological behavior of malignant tumors and are related to immunity were enriched, such as: \u0026ldquo;Cell adhesion molecules (CAMs) \u0026rdquo;, \u0026ldquo;ribosome\u0026rdquo;, \u0026ldquo;extracellular matrix-receptor (ECM-receptor) interaction\u0026rdquo;, \u0026ldquo;chemokine signaling pathway\u0026rdquo;, \u0026ldquo;oxidative phosphorylation (OXPHOS) \u0026rdquo;(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). These GSEA analyses reveal ALG13-related molecular changes, including phospholipid metabolism and oxidative stress, and they may be involved in immune-related pathways of PRAD TME.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePrediction and analysis of upstream miRNAs of ALG13.\u003c/b\u003e Salmena \u003cem\u003eet al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e first proposed the competitive endogenous RNA (ceRNA) hypothesis, which described that ceRNAs could alter the function of target miRNAs by competing for the common binding site of mRNAs on target miRNAs. In determining whether ALG13 is regulated by some upstream miRNAs, we used the miRWalk database to predict the upstream miRNAs that might bind ALG13. Finally, 34 miRNAs were found. Through R and Spearman\u0026rsquo;s correlation analysis, eight miRNAs were associated with ALG13 in Pca (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). From the perspective of the mechanism of miRNA regulating the expression of target genes, a negative correlation should be observed between miRNA and ALG13, and miRNA should have a low-expression level in Pca tissues. Based on this analysis, six miRNAs were negatively correlated with ALG13. We continuously used starBase to verify the 34 miRNAs and found that three miRNAs were negatively related to ALG13, which overlapped with the results analyzed by R (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). In addition, only has-miR-27a-3p and has-miR-381-3p showed low-expression in PRAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). These results indicated that has-miR-27a-3p and has-miR-381-3p may be potential upstream miRNAs of ALG13 in PRAD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePrediction and analysis of upstream lncRNAs of has-miR-27a-3p and has-miR-381-3p.\u003c/b\u003e Subsequently, starBase was used to predict the upstream lncRNAs of has-miR-27a-3p and has-miR-381-3p. A total of 64 lncRNAs were related to has-miR-381-3p. We constructed the lncRNA-has-miR-381-3p regulatory network using cytoscape for visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea). Then, UALCAN was used to detect the expression level and prognosis of these lncRNAs in PRAD, and the results showed that only AL390728.6, KCNQ1OT1, and AC012645.1 were significantly upregulated compared with normal tissues, and patients with PRAD had poor OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb). Based on the ceRNA hypothesis, lncRNA should be negatively correlated with miRNA and positively correlated with mRNA. We detected the expression correlation between three lncRNAs and has-miR-381-3p or ALG13 in PRAD and found that only AL390728.6 was negatively related to has-miR-381-3p and positively correlated with ALG13 by using starBase (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Next, we used the same method to predict the upstream lncRNA of has-miR-27a-3p and found a total of 154 lncRNAs. Similarly, we established the lncRNA-has-miR-27a-3p regulatory network for visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec). Based on the ULACAN database, six lncRNAs with poor OS were upregulated in PRAD, including LINC01355, PSMD6-AS2, PVT1, KCNQ1OT1, AL162171.3, and Z83843.1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ed). They were all positively correlated with ALG13 but not negatively correlated with has-miR-27a-3p as detected by starBase (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, ALG13 is likely regulated by the AL390728.6/has-miR-381-3p axis to play a role in Pca PRAD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis between lncRNA and has-miR-381-3p or lncRNA and ALG13 in PRAD determined by starBase.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003elncRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emiRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAL390728.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehas-miR-381-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.76E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKCNQ1OT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehas-miR-381-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.60E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC012645.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehas-miR-381-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elncRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAL390728.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.51E-24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKCNQ1OT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40E-19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC012645.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.65E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis between lncRNA and has-miR-27a-3p or lncRNA and ALG13 in PRAD determined by starBase.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003elncRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emiRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR\u0026ndash;27a\u0026ndash;3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSMD6-AS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR\u0026ndash;27a\u0026ndash;3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.25E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR\u0026ndash;27a\u0026ndash;3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.84E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKCNQ1OT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR\u0026ndash;27a\u0026ndash;3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.54E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAL162171.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR\u0026ndash;27a\u0026ndash;3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.34E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ83843.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR\u0026ndash;27a\u0026ndash;3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elncRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53E-32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSMD6-AS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.23E-17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.10E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKCNQ1OT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40E-19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAL162171.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53E-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ83843.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.30E-41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eProstate cancer is a common tumor of the male genitourinary system worldwide. The popularity of prostate-specific antigen (PSA) screening has led to an increase in the number of men undergoing prostate biopsies and diagnosed with early-stage Pca\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Given the lack of specificity in the PSA test, a large number of prostate biopsies were negative\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In addition, unnecessary biopsies may lead to overdiagnosis of clinically unimportant tumors\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Although most patients with Pca are diagnosed with inertia or slow progression, about 20% of patients are at risk of developing potentially high-risk and fatal diseases during the course of the disease\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Therefore, finding promising prognostic biomarkers or effective treatment targets is necessary for the diagnosis and treatment of Pca. ALG13 is highly conserved in most eukaryotes, and it belongs to the OTU family\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Evidence suggests that ALG13 plays an important role in human diseases, including malignant tumors. However, the role of ALG13 in PRAD and its related biological functions in Pca remains unknown.\u003c/p\u003e \u003cp\u003eIn this study, we initially used TCGA-PRAD data to analyze the expression of ALG13 in PRAD and then the GEO database to verify the expression of ALG13. We found that ALG13 was highly expressed in PRAD tumor tissues, and the protein expression of ALG13 was significantly higher than that in normal tissues based on the HPA database. Furthermore, we found that high ALG13 expression was related to poor OS and DSS in patients with PRAD.\u003c/p\u003e \u003cp\u003eDifferent types of immune cells play a key regulatory role in TME. An increasing number of evidence shows that the interaction between cancer cells and various components of TME promotes tumor immune escape, thereby leading to tumor proliferation, recurrence, and metastasis. Although immunotherapy has made some breakthroughs in cancer treatment, its successful application still faces many challenges\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In particular, “cold” tumors such as Pca has little success in responding to various immune-related treatments\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Sipuleucel-T is the only immunotherapeutic drug approved by FDA for Pca\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Therefore, the identification of new targets and biomarkers is critical to the development of immunotherapy for Pca. However, the mechanism by which ALG13 participates in TME and affects TIICs in Pca remains to be explored.\u003c/p\u003e \u003cp\u003ewe divided ALG13 into high- and low-expression groups for CIBERSORT analysis to evaluate the proportion of TIICs in PRAD. The expression of ALG13 in PRAD is related to B cell memory, Tregs, NK cell activation, macrophage M1, macrophage M2, myeloid dendritic cell resting, and myeloid dendritic cell activation. Next, we used the TIMER database to study whether ALG13 expression in PRAD was related to TIICs. The results showed that ALG13 was positively correlated with B cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, macrophages, neutrophils, and dendritic cells and negatively correlated with CD4 + T cells. These results suggested that ALG13 may be involved in immune infiltration.\u003c/p\u003e \u003cp\u003eIn addition, we found that PRAD patients with high ALG13 expression had higher expression at CD274, PDCD1LG2, and SIGLEC15, whereas those with low ALG13 expression had higher expression at LAG3. Combined with ICB analysis, patients with high ALG13 expression had better response to ICB. Finally, we confirmed that ALG13 is co-expressed with several key immune regulatory genes, such as MHC genes, immune-stimulators, immuno-inhibitors, chemokines, and chemokine receptors, indicating that ALG13 may be a potential target for immunotherapy.\u003c/p\u003e \u003cp\u003eFurthermore, applied GESA analysis to identify the potential molecular mechanisms and biological functions of ALG13 in promoting PRAD. Enrichment analysis showed that ALG13 affected tumor development through a variety of cancer and immune-related pathways. In the high ALG13 expression group, we enriched the “ubiquitin mediated proteolysis” signaling pathway, the ubiquitin proteasome pathway is the most significant intracellular proteolytic pathway\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, which involved in almost all cellular processes, such as DNA repair, transcription, autophagy, cell cycle progression, the unfolded protein response, apoptosis, endocytosis, protein-protein interaction, intracellular trafficking, inflammatory signaling\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e–\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Another pathway related to tumor and immunity is the chemokine signaling pathway. Chemokine is a small, secretory, and structurally related family of cytokines that play an important role in inflammation and immunity\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, which could directly affect the proliferation and metastasis of cancer cells\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. We have also enriched ECM-receptor interaction, and specific interactions between ECM and cells are mediated by transmembrane molecules, including integrins, proteoglycans, and other cell surface-related components. These interactions can lead to malignant biological behaviors of tumor cells, such as adhesion, migration, proliferation, and apoptosis\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. OXPHOS, and ribosome pathways were significantly enriched in the low ALG13 expression group. The increase of OXPHOS is associated with drug resistance to chemotherapy. In Pca, OXPHOS inhibition can address resistance to docetaxel\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e while responding to the immune response\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Oncogenes or the loss of tumor suppressor genes leads to the hyperactivation of ribosome biogenesis, which plays a key role in cancer initiation and progression\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In addition, the relationship between ribosome abundance and cell intrinsic immunity has been confirmed\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. These pathways are widely involved in the occurrence and development of tumors, indicating that changes in ALG13 expression level play an important role in the occurrence and development of PRAD, and that ALG13 was involved in immune-related pathways in the TME of PRAD. However, the mechanism of these signal pathways involved in the regulation of PRAD by ALG13 remains to be further studied and verified.\u003c/p\u003e \u003cp\u003eAt present, numerous studies have indicated that lncRNA can be used as ceRNA to regulate the expression of target mRNA by sponge-adsorbing miRNA and jointly regulate tumor progression\u003csup\u003e\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e–\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. In exploring the upstream miRNA that regulates ALG13, we used the miRWalk database to predict the potential upstream miRNA of ALG13. After correlation, expression, and survival analysis, we screened out hsa-miR-27a-3p and hsa-miR-381-3p as ALG13 upstream miRNA to inhibit tumor progression. A previous study has also shown that hsa-miR-27a-3p has an inhibitory effect on the proliferation and migration of esophageal cancer\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. hsa-miR-27a-3p over-expression can also inhibit the progression of glioblastoma\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Moreover, has-miR-381-3p was upregulated in the serum exosomes of patients with parathyroid tumor\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. However, the expression was low in breast cancer, endometrial cancer, and ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNext, we predicted the upstream lncRNA of the has-miR-27a-3p and has-miR-381-3p/ALG13 axes. Based on expression, survival, and correlation analyses, lncRNA was associated with hsa-miR-27a-3p and positively correlated with ALG13 in PRAD but not or negatively related to hsa-miR-27a-3p. Among the lncRNAs associated with hsa-miR-381-3p, only AL390728.6 in PRAD was negatively correlated with hsa-miR-381-3p and positively correlated with ALG13. At present, AL390728.6 was highly expressed in hepatocellular carcinoma, and it may be used as a potential biomarker\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Therefore, the AL390728.6/hsa-miR-381-3p/ALG13 axis was considered to be a potential pathway for regulating PRAD progression.\u003c/p\u003e \u003cp\u003eHowever, this study still has some limitations because it is based on public data and assumptions to generate forecasts. First, more clinical samples are needed to verify the high-expression of ALG13 in PRAD. Second, basic experiments are needed to verify the exact role of ALG13-related pathways and AL390728.6/hsa-miR-381-3p/ALG13 axes in PRAD. Finally, the mechanism of ALG13 regulating immune cell infiltration remains to be further studied.\u003c/p\u003e \u003cp\u003eWe clarified that the increased expression of ALG13 in PRAD tumor tissues was positively correlated with poor OS of patients with PRAD. ALG13 may affect tumor development by regulating TIICs in TME, and it may be a potential target for immunotherapy. In addition, we found the upstream regulation mechanism of ALG13 in PRAD, namely, AL390728.6/hsa-miR-381-3p/ALG13 axis.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eData source and analysis of differential expressions.\u003c/b\u003e We downloaded cancer-related RNA sequences as well as clinicopathological and survival data of PRAD from the Cancer Genome Atlas (TCGA) database(\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). We also downloaded 33 tumors RNA-sequencing expression (level 3) profiles from the TCGA dataset(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.com\u003c/span\u003e\u003cspan address=\"https://portal.gdc.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for pan-caner analysis. R v4.0.3 was used for statistical analysis. The Wilcoxon test was used to evaluate the differential mRNA expression of ALG13 in PRAD, and P value \u0026lt; 0.05 was considered as statistically significant.\u003c/p\u003e\u003cp\u003eWe downloaded GSE32571 gene expression profiling data from the 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) to verify the expression of ALG13 in PRAD, which containing 59 Pca and 39 benign tissue samples\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The raw data were downloaded as MINiML files. Box plots are drawn by boxplot.\u003c/p\u003e\u003cp\u003e \u003cb\u003eImmunohistochemistry (IHC) staining.\u003c/b\u003e We performed immunohistochemical image analysis using the HPA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"http://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to assess the differential expression of ALG13 in PRAD and normal tissues at the protein level. The HPA043684 antibody was used for IHC.\u003c/p\u003e\u003cp\u003e \u003cb\u003ePrognostic analysis of ALG13.\u003c/b\u003e We extracted the survival information of each sample in TCGA. Then, we selected several indexes, including OS, DSS, and PFI, to clarify the relationship between the expression of ALG13 and the prognosis of patients with PRAD. Survival analysis of PRAD was performed by using the Kaplan–Meier method and evaluated by R package “survival” and “survminer” packages. Then, Cox analysis was performed using R packet “survival” to determine the correlation between ALG13 expression and survival.\u003c/p\u003e\u003cp\u003e \u003cb\u003eRelationship between ALG13 and immune cell infiltration in the tumor microenvironment (TME) of PRAD.\u003c/b\u003e TIMER (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer/\u003c/span\u003e\u003cspan address=\"https://cistrome.shinyapps.io/timer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a web server for comprehensive analysis of the abundance of tumor-infiltrating immune cells (TIICs) across diverse cancer types\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. The TIICs in TIMER included B cells, CD4 + T cells, CD8 + T cells, macrophages, neutrophils, and dendritic cells. In assessing the reliable results of immune score evaluation, we used “immuneeconv.” It is an R software package that integrates six latest algorithms, including TIMER, xCell, MCP-counter, CIBERSORT, EPIC, and quanTIseq.\u0026nbsp;These algorithms had been benchmarked, and each algorithm had a unique advantage. Subsequently, we applied the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression (ESTIMATE) algorithm\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e to calculate and compare stromal scores, immune scores, and ESTIMATE scores between high and low ALG13 expression groups of PRAD samples. In this study, we used the R software packages “ggplot” and “ggpubr” to visualize the analysis results.\u003c/p\u003e\u003cp\u003e \u003cb\u003eImmune-checkpoint and immuno-regulatory factor analysis.\u003c/b\u003e We divided ALG13 into the high-expression and low-expression groups and then extracted the expression data of SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2 genes associated with immune-checkpoint. The R packages “ggplot2” and “immuneeconv” were used to evaluate the expression of ALG13 at different levels in PRAD. Subsequently, we predicted the potential immune-checkpoint blockade (ICB) response based on the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. TIDE used a set of gene expression markers to evaluate two different mechanisms of tumor immune escape, including dysfunction of tumor-infiltrating cytotoxic T lymphocytes (CTL) and rejection of CTL caused by immunosuppressive factors, high TIDE score, poor efficacy of ICB, and short survival after ICB treatment\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. In addition, we analyzed the relationship between ALG13 and immunomodulators, including chemokines, chemokine receptors, immune-inhibitors, immune-stimulators, and MHC molecules, and used the R packages “pheatmap” and “immuneeconv” to evaluate the expression of immunomodulators and co-expression of ALG13 and immune genes.\u003c/p\u003e\u003cp\u003e \u003cb\u003eGene set enrichment analysis (GSEA).\u003c/b\u003e In exploring the biological signaling pathway, GSEA was performed in the high- and low-expression groups compared with the median level of ALG13 expression. GSEA 4.2.3 was also used to perform this analysis. “c2.cp.kegg.v2022.1.Hs.symbols.gmt” from the MSigDB gene set were selected as the reference gene set. Gene sets with |NES|\u0026gt;1, NOM P \u0026lt; 0.05, and FDR q \u0026lt; 0.25 were considered significant\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e \u003cb\u003eALG13 upstream miRNA and lncRNA prediction.\u003c/b\u003e Upstream binding miRNAs of ALG13 were predicted by the miRWalk database (mirwalk.umm.uni-heidelberg.de). We selected the first 100 miRNAs whose miRBD program value is “0”. These predicted miRNAs were regarded as candidate miRNAs of ALG13. The correlation between filtered miRNAs and ALG13 was demonstrated by R package “pheatmap,” and their correlation with ALG13 was verified by starBase.\u003c/p\u003e\u003cp\u003eThen, starBase, which is a database for exploring miRNA-related studies\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, was used to predict upstream lncRNAs associated with candidate miRNAs. In verifying the differential expression and prognosis of ncRNAs associated with ALG13 in PRAD and normal tissues, we further verified our results using the University of ALabama at Birmingham Caner Data Analysis Portal (UALCAN, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.path.uab.edu\u003c/span\u003e\u003cspan address=\"http://ualcan.path.uab.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e and constructed boxplot and survival curves.\u003c/p\u003e\u003cp\u003e \u003cb\u003eStatistical analysis.\u003c/b\u003e Gene expression data were normalized by log2-transformation. Normal and tumor tissues were compared using a two-group wilcox-test. The relationship between prognosis and ALG13 expression was presented using the Kaplan–Meier survival curve and compared using a log-rank test. Continuous variables were described as mean ± SD. The Cox proportional hazards regression model was used for univariate and multivariate analyses. The correlation between ALG13 expression and abundance scores of immune cells was evaluated by Spearman or Pearson correlation analysis. Furthermore, P value \u0026lt; 0.05 or log-rank P value \u0026lt; 0.05 was considered statistically significant. R (version 4.0.3) was used for statistical analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are sincerely grateful to all the authors in the study. At the same time, we would like to express our sincere respect to the founders and data collectors of TCGA, GEO, HPA, TIMER, GSEA, UALCAN and other online databases. It is precisely because of such convenient biological information platform that clinical researchers have such a broad platform to learn and complete corresponding scientific research like us.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.X. and Z.Y. designed and supervised the study, M.X. analyzed data and wrote the manuscript, Y.X. collected the data, W.L. and X.X. processed the figures and tables. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed for this study are included in this article. Further details are available from the corresponding author upon request. Patient data that supported the findings of this study are available in The Cancer Genome Atlas (TCGA) datasets at https://portal.gdc.cancer.gov/repository.The datasets presented in this study were obtained from the GEO (https://www.ncbi.nlm.nih.gov/geo/)(GSE32571, \u0026nbsp;https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi/acc.cgi)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper relates to no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel, R. L., Miller, K. D., Fuchs, H. E. \u0026amp; Jemal, A. Cancer statistics, 2022. CA Cancer J Clin 72, 7\u0026ndash;33, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21708\u003c/span\u003e\u003cspan address=\"10.3322/caac.21708\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia, C. \u003cem\u003eet al.\u003c/em\u003e Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J (Engl) 135, 584\u0026ndash;590, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/CM9.0000000000002108\u003c/span\u003e\u003cspan address=\"10.1097/CM9.0000000000002108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaccetti, A. L. \u0026amp; Subudhi, S. K. Immunotherapy for metastatic prostate cancer: immuno-cold or the tip of the iceberg? Curr Opin Urol 27, 566\u0026ndash;571, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MOU.0000000000000433\u003c/span\u003e\u003cspan address=\"10.1097/MOU.0000000000000433\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMevissen, T. E. \u003cem\u003eet al.\u003c/em\u003e OTU deubiquitinases reveal mechanisms of linkage specificity and enable ubiquitin chain restriction analysis. Cell 154, 169\u0026ndash;184, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2013.05.046\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2013.05.046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, X., Weldeghiorghis, T., Zhang, G., Imperiali, B. \u0026amp; Prestegard, J. H. Solution structure of Alg13: the sugar donor subunit of a yeast N-acetylglucosamine transferase. Structure 16, 965\u0026ndash;975, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.str.2008.03.010\u003c/span\u003e\u003cspan address=\"10.1016/j.str.2008.03.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAverbeck, N., Gao, X. D., Nishimura, S. \u0026amp; Dean, N. Alg13p, the catalytic subunit of the endoplasmic reticulum UDP-GlcNAc glycosyltransferase, is a target for proteasomal degradation. Mol Biol Cell 19, 2169\u0026ndash;2178, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1091/mbc.e07-10-1077\u003c/span\u003e\u003cspan address=\"10.1091/mbc.e07-10-1077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao, X. D., Moriyama, S., Miura, N., Dean, N. \u0026amp; Nishimura, S. Interaction between the C termini of Alg13 and Alg14 mediates formation of the active UDP-N-acetylglucosamine transferase complex. J Biol Chem 283, 32534\u0026ndash;32541, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M804060200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M804060200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBissar-Tadmouri, N. \u003cem\u003eet al.\u003c/em\u003e X chromosome exome sequencing reveals a novel ALG13 mutation in a nonsyndromic intellectual disability family with multiple affected male siblings. Am J Med Genet A 164A, 164\u0026ndash;169, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ajmg.a.36233\u003c/span\u003e\u003cspan address=\"10.1002/ajmg.a.36233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Ligt, J. \u003cem\u003eet al.\u003c/em\u003e Diagnostic exome sequencing in persons with severe intellectual disability. N Engl J Med 367, 1921\u0026ndash;1929, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa1206524\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa1206524\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimal, S. \u003cem\u003eet al.\u003c/em\u003e Gene identification in the congenital disorders of glycosylation type I by whole-exome sequencing. Hum Mol Genet 21, 4151\u0026ndash;4161, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/hmg/dds123\u003c/span\u003e\u003cspan address=\"10.1093/hmg/dds123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSelamat, S. A. \u003cem\u003eet al.\u003c/em\u003e Genome-scale analysis of DNA methylation in lung adenocarcinoma and integration with mRNA expression. Genome Res 22, 1197\u0026ndash;1211, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/gr.132662.111\u003c/span\u003e\u003cspan address=\"10.1101/gr.132662.111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamagata, N. \u003cem\u003eet al.\u003c/em\u003e A training-testing approach to the molecular classification of resected non-small cell lung cancer. Clin Cancer Res 9, 4695\u0026ndash;4704 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng, J., Hou, G., Fang, Z., Liu, J. \u0026amp; Lv, X. D. Distinct expression and prognostic value of OTU domain-containing proteins in non-small-cell lung cancer. Oncol Lett 18, 5417\u0026ndash;5427, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3892/ol.2019.10883\u003c/span\u003e\u003cspan address=\"10.3892/ol.2019.10883\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Antonellis, P. \u003cem\u003eet al.\u003c/em\u003e Early targets of miR-34a in neuroblastoma. Mol Cell Proteomics 13, 2114\u0026ndash;2131, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/mcp.M113.035808\u003c/span\u003e\u003cspan address=\"10.1074/mcp.M113.035808\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalmena, L., Poliseno, L., Tay, Y., Kats, L. \u0026amp; Pandolfi, P. P. A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell 146, 353\u0026ndash;358, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2011.07.014\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2011.07.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHugosson, J. \u003cem\u003eet al.\u003c/em\u003e A 16-yr Follow-up of the European Randomized study of Screening for Prostate Cancer. Eur Urol 76, 43\u0026ndash;51, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eururo.2019.02.009\u003c/span\u003e\u003cspan address=\"10.1016/j.eururo.2019.02.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchroder, F. H. \u003cem\u003eet al.\u003c/em\u003e Screening for prostate cancer decreases the risk of developing metastatic disease: findings from the European Randomized Study of Screening for Prostate Cancer (ERSPC). Eur Urol 62, 745\u0026ndash;752, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eururo.2012.05.068\u003c/span\u003e\u003cspan address=\"10.1016/j.eururo.2012.05.068\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCucchiara, V. \u003cem\u003eet al.\u003c/em\u003e Genomic Markers in Prostate Cancer Decision Making. Eur Urol 73, 572\u0026ndash;582, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eururo.2017.10.036\u003c/span\u003e\u003cspan address=\"10.1016/j.eururo.2017.10.036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson, I. M. \u003cem\u003eet al.\u003c/em\u003e Prevalence of prostate cancer among men with a prostate-specific antigen level \u0026lt; or = 4.0 ng per milliliter. N Engl J Med 350, 2239\u0026ndash;2246, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa031918\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa031918\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamdy, F. C. \u003cem\u003eet al.\u003c/em\u003e 10-Year Outcomes after Monitoring, Surgery, or Radiotherapy for Localized Prostate Cancer. N Engl J Med 375, 1415\u0026ndash;1424, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa1606220\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa1606220\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang, A. J., Autio, K. A., Roach, M., 3rd \u0026amp; Scher, H. I. High-risk prostate cancer-classification and therapy. Nat Rev Clin Oncol 11, 308\u0026ndash;323, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrclinonc.2014.68\u003c/span\u003e\u003cspan address=\"10.1038/nrclinonc.2014.68\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurciano-Goroff, Y. R., Warner, A. B. \u0026amp; Wolchok, J. D. The future of cancer immunotherapy: microenvironment-targeting combinations. Cell Res 30, 507\u0026ndash;519, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41422-020-0337-2\u003c/span\u003e\u003cspan address=\"10.1038/s41422-020-0337-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y. \u0026amp; Zhang, Z. The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications. Cell Mol Immunol 17, 807\u0026ndash;821, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41423-020-0488-6\u003c/span\u003e\u003cspan address=\"10.1038/s41423-020-0488-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBilusic, M., Madan, R. A. \u0026amp; Gulley, J. L. Immunotherapy of Prostate Cancer: Facts and Hopes. Clin Cancer Res 23, 6764\u0026ndash;6770, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078-0432.CCR-17-0019\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-17-0019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVitkin, N., Nersesian, S., Siemens, D. R. \u0026amp; Koti, M. The Tumor Immune Contexture of Prostate Cancer. Front Immunol 10, 603, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2019.00603\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2019.00603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComiskey, M. C., Dallos, M. C. \u0026amp; Drake, C. G. Immunotherapy in Prostate Cancer: Teaching an Old Dog New Tricks. Curr Oncol Rep 20, 75, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11912-018-0712-z\u003c/span\u003e\u003cspan address=\"10.1007/s11912-018-0712-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYerlikaya, A. \u0026amp; Yontem, M. The significance of ubiquitin proteasome pathway in cancer development. Recent Pat Anticancer Drug Discov 8, 298\u0026ndash;309, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2174/1574891x113089990033\u003c/span\u003e\u003cspan address=\"10.2174/1574891x113089990033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrabbe, C., Husnjak, K. \u0026amp; Dikic, I. The spatial and temporal organization of ubiquitin networks. Nat Rev Mol Cell Biol 12, 295\u0026ndash;307, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrm3099\u003c/span\u003e\u003cspan address=\"10.1038/nrm3099\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRieser, E., Cordier, S. M. \u0026amp; Walczak, H. Linear ubiquitination: a newly discovered regulator of cell signalling. Trends Biochem Sci 38, 94\u0026ndash;102, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tibs.2012.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.tibs.2012.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin, Z., Popelka, H., Lei, Y., Yang, Y. \u0026amp; Klionsky, D. J. The Roles of Ubiquitin in Mediating Autophagy. Cells 9, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cells9092025\u003c/span\u003e\u003cspan address=\"10.3390/cells9092025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriffith, J. W., Sokol, C. L. \u0026amp; Luster, A. D. Chemokines and chemokine receptors: positioning cells for host defense and immunity. Annu Rev Immunol 32, 659\u0026ndash;702, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev-immunol-032713-120145\u003c/span\u003e\u003cspan address=\"10.1146/annurev-immunol-032713-120145\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaronni, N. \u003cem\u003eet al.\u003c/em\u003e Cancer and Chemokines. Methods Mol Biol 1393, 87\u0026ndash;96, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-1-4939-3338-9_8\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4939-3338-9_8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChow, M. T. \u0026amp; Luster, A. D. Chemokines in cancer. Cancer Immunol Res 2, 1125\u0026ndash;1131, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/2326-6066.CIR-14-0160\u003c/span\u003e\u003cspan address=\"10.1158/2326-6066.CIR-14-0160\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarsico, G., Russo, L., Quondamatteo, F. \u0026amp; Pandit, A. Glycosylation and Integrin Regulation in Cancer. Trends Cancer 4, 537\u0026ndash;552, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.trecan.2018.05.009\u003c/span\u003e\u003cspan address=\"10.1016/j.trecan.2018.05.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNajafi, M., Farhood, B. \u0026amp; Mortezaee, K. Extracellular matrix (ECM) stiffness and degradation as cancer drivers. J Cell Biochem 120, 2782\u0026ndash;2790, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcb.27681\u003c/span\u003e\u003cspan address=\"10.1002/jcb.27681\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBosc, C., Selak, M. A. \u0026amp; Sarry, J. E. Resistance Is Futile: Targeting Mitochondrial Energetics and Metabolism to Overcome Drug Resistance in Cancer Treatment. Cell Metab 26, 705\u0026ndash;707, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cmet.2017.10.013\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2017.10.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoreel, D. F., Span, P. N., Heskamp, S., Adema, G. J. \u0026amp; Bussink, J. Targeting Oxidative Phosphorylation to Increase the Efficacy of Radio- and Immune-Combination Therapy. Clin Cancer Res 27, 2970\u0026ndash;2978, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078-0432.CCR-20-3913\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-20-3913\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrsolic, I. \u003cem\u003eet al.\u003c/em\u003e The relationship between the nucleolus and cancer: Current evidence and emerging paradigms. Semin Cancer Biol 37\u0026ndash;38, 36\u0026ndash;50, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.semcancer.2015.12.004\u003c/span\u003e\u003cspan address=\"10.1016/j.semcancer.2015.12.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTruitt, M. L. \u0026amp; Ruggero, D. New frontiers in translational control of the cancer genome. Nat Rev Cancer 16, 288\u0026ndash;304, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrc.2016.27\u003c/span\u003e\u003cspan address=\"10.1038/nrc.2016.27\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBianco, C. \u0026amp; Mohr, I. Ribosome biogenesis restricts innate immune responses to virus infection and DNA. Elife 8, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7554/eLife.49551\u003c/span\u003e\u003cspan address=\"10.7554/eLife.49551\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, R. \u003cem\u003eet al.\u003c/em\u003e LncRNA UCC promotes epithelial-mesenchymal transition via the miR-143-3p/SOX5 axis in non-small-cell lung cancer. Lab Invest 101, 1153\u0026ndash;1165, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41374-021-00586-6\u003c/span\u003e\u003cspan address=\"10.1038/s41374-021-00586-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo, R. \u003cem\u003eet al.\u003c/em\u003e LncRNA RCAT1 promotes tumor progression and metastasis via miR-214-5p/E2F2 axis in renal cell carcinoma. Cell Death Dis 12, 689, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41419-021-03955-7\u003c/span\u003e\u003cspan address=\"10.1038/s41419-021-03955-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, J. \u003cem\u003eet al.\u003c/em\u003e LncRNA FGD5-AS1 accelerates cell proliferation in pancreatic cancer by regulating miR-520a-3p/KIAA1522 axis. Cancer Biol Ther 22, 257\u0026ndash;266, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/15384047.2021.1883184\u003c/span\u003e\u003cspan address=\"10.1080/15384047.2021.1883184\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, A. \u003cem\u003eet al.\u003c/em\u003e LncRNA UCA1 promotes development of gastric cancer via the miR-145/MYO6 axis. Cell Mol Biol Lett 26, 33, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s11658-021-00275-8\u003c/span\u003e\u003cspan address=\"10.1186/s11658-021-00275-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, M. \u003cem\u003eet al.\u003c/em\u003e Novel Immune-Related Ferroptosis Signature in Esophageal Cancer: An Informatics Exploration of Biological Processes Related to the TMEM161B-AS1/hsa-miR-27a-3p/GCH1 Regulatory Network. Front Genet 13, 829384, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fgene.2022.829384\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2022.829384\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Q. \u003cem\u003eet al.\u003c/em\u003e Over-expression of lncRNA TMEM161B-AS1 promotes the malignant biological behavior of glioma cells and the resistance to temozolomide via up-regulating the expression of multiple ferroptosis-related genes by sponging hsa-miR-27a-3p. Cell Death Discov 7, 311, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41420-021-00709-4\u003c/span\u003e\u003cspan address=\"10.1038/s41420-021-00709-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalmani, T. \u003cem\u003eet al.\u003c/em\u003e Hsa-miR-27a-3p and epidermal growth factor receptor expression analysis in glioblastoma FFPE samples. Asia Pac J Clin Oncol 17, e185-e190, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/ajco.13399\u003c/span\u003e\u003cspan address=\"10.1111/ajco.13399\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, J. \u003cem\u003eet al.\u003c/em\u003e Expression profile of serum-related exosomal miRNAs from parathyroid tumor. Endocrine 72, 239\u0026ndash;248, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12020-020-02535-7\u003c/span\u003e\u003cspan address=\"10.1007/s12020-020-02535-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePane, K. \u003cem\u003eet al.\u003c/em\u003e Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB. \u003cem\u003eBiomedicines\u003c/em\u003e 10, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biomedicines10061306\u003c/span\u003e\u003cspan address=\"10.3390/biomedicines10061306\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang, Y. \u003cem\u003eet al.\u003c/em\u003e Identification of Hypoxia-Related Prognostic Signature and Competing Endogenous RNA Regulatory Axes in Hepatocellular Carcinoma. Int J Mol Sci 23, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms232113590\u003c/span\u003e\u003cspan address=\"10.3390/ijms232113590\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuner, R. \u003cem\u003eet al.\u003c/em\u003e The maternal embryonic leucine zipper kinase (MELK) is upregulated in high-grade prostate cancer. J Mol Med (Berl) 91, 237\u0026ndash;248, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00109-012-0949-1\u003c/span\u003e\u003cspan address=\"10.1007/s00109-012-0949-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, T. \u003cem\u003eet al.\u003c/em\u003e TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res 77, e108-e110, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/0008-5472.CAN-17-0307\u003c/span\u003e\u003cspan address=\"10.1158/0008-5472.CAN-17-0307\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshihara, K. \u003cem\u003eet al.\u003c/em\u003e Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 4, 2612, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ncomms3612\u003c/span\u003e\u003cspan address=\"10.1038/ncomms3612\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, P. \u003cem\u003eet al.\u003c/em\u003e Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med 24, 1550\u0026ndash;1558, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41591-018-0136-1\u003c/span\u003e\u003cspan address=\"10.1038/s41591-018-0136-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian, A. \u003cem\u003eet al.\u003c/em\u003e Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545\u0026ndash;15550, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.0506580102\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0506580102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, J. H., Liu, S., Zhou, H., Qu, L. H. \u0026amp; Yang, J. H. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 42, D92-97, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkt1248\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkt1248\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandrashekar, D. S. \u003cem\u003eet al.\u003c/em\u003e UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia 19, 649\u0026ndash;658, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neo.2017.05.002\u003c/span\u003e\u003cspan address=\"10.1016/j.neo.2017.05.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandrashekar, D. S. \u003cem\u003eet al.\u003c/em\u003e UALCAN: An update to the integrated cancer data analysis platform. Neoplasia 25, 18\u0026ndash;27, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neo.2022.01.001\u003c/span\u003e\u003cspan address=\"10.1016/j.neo.2022.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\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":"ALG13, prostate cancer, prognosis, biomarker, immune infiltration, ncRNA","lastPublishedDoi":"10.21203/rs.3.rs-2680822/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2680822/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAsparagine-linked glycosylation 13 (ALG13) is a highly conserved protein in most eukaryotes, which belongs to the OTU family. It plays a role in neuroblastoma and non-small cell lung cancer. However, the role of ALG13 in prostate cancer (Pca) and its correlation with tumor-infiltrating immune cells remain unclear. Thus, in this study, we extracted and analyzed The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Tumor Immune Estimation Resource (TIMER), and Human Protein Atlas (HPA) data sets to study the potential carcinogenic mechanism of ALG13, including ALG13 expression, prognosis and the correlation of ALG13 expression in immune cell infiltration in Pca. Furthermore, the potential biological signaling pathway of ALG13 in Pca was studied by using Gene set enrichment analysis (GSEA). Upstream microRNA and lncRNA related to ALG13 were found through the prediction of miRWalk and starBase. Results showed that ALG13 was highly expressed in Pca tissues and associated with poor overall survival (OS) and disease-specific survival (DSS). ALG13 expression was correlated with immune cell infiltration. In addition, ALG13 was co-expressed with most immune-related genes, and the high-expression of ALG13 was effective for immune-checkpoint blockade treatment. ALG13 may regulate the pathogenesis of Pca through tumor and immune-related pathways. Finally, AL390728.6/hsa-miR-381-3p axis is considered as a potential upstream ncRNA-related pathway of ALG13 in Pca. Our results demonstrate that the ncRNA-mediated upregulation of ALG13 is associated with poor OS in Prostate adenocarcinoma (PRAD) and tumor immune infiltration. ALG13 may be a new potential prognostic biomarker.\u003c/p\u003e","manuscriptTitle":"ALG13 as a prognostic biomarker of prostate cancer associated with tumor immune infiltration and mediated by upstream ncRNA","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-20 15:10:48","doi":"10.21203/rs.3.rs-2680822/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":"9ce60591-4635-4dbe-aa08-fc2863f7cd81","owner":[],"postedDate":"March 20th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":19977735,"name":"Health sciences/Biomarkers"},{"id":19977736,"name":"Health sciences/Oncology"},{"id":19977737,"name":"Health sciences/Urology"}],"tags":[],"updatedAt":"2023-07-13T06:35:35+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-20 15:10:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2680822","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2680822","identity":"rs-2680822","version":["v1"]},"buildId":"ApUGefWb6u5IBVtyqm6d5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}