Identification of PRIM2 as a new prognostic and immune-related marker in cancer based on a comprehensive pan-cancer analysis

Identification of PRIM2 as a new prognostic and immune-related marker in cancer based on a comprehensive pan-cancer analysis | 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 Identification of PRIM2 as a new prognostic and immune-related marker in cancer based on a comprehensive pan-cancer analysis Jinqun Jiang, Hai Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2201390/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 DNA Primase Subunit 2 (PRIM2) encodes a large subunit (p58C) of DNA primase, which plays an important role in DNA replication. However, the role of PRIM2 in cancer and immune regulation has not been elucidated. Therefore, in the present study, the expression profile of PRIM2 in cancer was investigated using publicly available databases like the Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx), the Broad Institute Cancer Cell Line Encyclopedia (CCLE) from the University of California Santa Cruz (UCSC) Xena website. PRIM2 expression was analyzed in paired normal to the adjacent tumor and unpaired cancer and normal tissues. The prognosis of PRIM2 in various cancers was studied using Cox regression and Kaplan-Meier analysis. The relationship between PRIM2 and tumor staging, tumor mutation burden (TMB), and microsatellite instability (MSI) was analyzed. Algorithms like cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT), xCell, ssGSEA, and MCP-counter were used to study the correlation between PRIMA2 and tumor immune microenvironment, immune infiltrating cells, and immune-related genes (antigen processing and presentation genes, chemokines, chemokine receptors, HLA-related genes, immune checkpoints). Further, the correlation between PRIM2 and methyltransferase (DNMT1, DNMT3A, DNMT3B) from different databases. The biological processes and signaling pathways associated with PRIM2 in various tumors were studied. Finally, the correlation between the PRIM2 and the sensitivity of multiple drugs was analyzed using the National Cancer Institute (NCI)-60 database. The results show that PRIM2 was up-regulated in most tumors, high PRIM2 expression was associated with the different stages of cancer, and poor prognosis was observed. The results indicate that PRIM2 could potentially be used as a prognostic and immunotherapy target in tumors. Health sciences/Medical research/Biomarkers Health sciences/Diseases/Cancer/Breast cancer PRIM2 pan-carcinoma prognosis immune infiltration immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction Cancer is the second leading cause of mortality worldwide. In 2020, the number of cancer cases increased by approximately 19.3 million globally, and about 10 million cancer-related death were reported, as per the latest data released by the International Agency for Research on Cancer. This puts an unimaginable burden on the global economy. Breast cancer has surpassed lung cancer as the leading cancer in women worldwide, which calls for attention to women’s health 1. With the emergence of targeted immunotherapy, marked improvement in survival has been observed in lung cancers 2, glioblastoma multiforme 3, and melanomas 4. Cancer is a heterogeneous disease due to which many patients with pancreatic cancer 5, hepatocellular carcinoma, and cholangiocarcinoma 6 fail to respond to immunotherapy tumors. The cancer heterogeneity also affects the diagnosis and prognosis of the patients 7. Hence, there is an urgent need for early diagnosis and prognostic biomarkers. PRIM2 is located on chromosome 6 and encodes for DNA primase subunit 2. It forms a DNA primase complex with the PRIM1 and participates in DNA replication 8. Studies have shown single nucleotide polymorphism in PRIMA2 can affect the prognosis of patients with non-small cell carcinoma 9. Dihydroartemisinin inhibits cell proliferation and colony formation of colonies by targeting PRIMA2 and is used in treating lung cancer 10. High-throughput sequencing of circRNA in liver cancer cells treated with Nitidine Chloride (NC) shows PRIM2 acts as a "miRNA sponge" with hsa_circ_0088364 and hsa_circ_0090049 11. In cervical cancer, PRIM2 is regulated by Sine oculis homeobox homolog 1 ( SIX1 ), which promotes DNA synthesis and increases cell proliferation in cervical cancer 12. It may be the therapeutic target of muaddil sapra in treating acute liver injury 13. These studies provide insight into the role of PRIM2 in cancer; however, additional research is required to understand the association between PRIM2 and cancer. In this study, the expression of the PRIM2 in different cancers was studied using different databases. The relationship between PRIM2 and tumor stage, the prognostic significance in cancer, and its correlation with tumor mutation burden (TMB), microsatellite instability (MSI), and DNA methyltransferase ( DNMT1, DNMT3A, DNMT3B ) were analyzed. Further, the association of PRIMA2 and immune cells, infiltrating immune cell score, and immune-related genes were evaluated. The biological processes and the pathways enrichment by PRIM2 in the top ten cancers with the highest morbidity and mortality were assessed. Finally, based on the CellMiner database 15, the correlation between PRIM2 and anticancer drugs of 60 cancer cells was analyzed. Methods And Materials Data source and difference analysis The RNA expression data, tumor mutations, MSI, and clinical data from the Cancer Genome Atlas (TCGA) pan-cancer, cancer cell line encyclopedia (CCLE), and the Genotype-Tissue Expression (GTEx) were retrieved from the University of California, Santa Cruz University (UCSC) Xena browser ( http://xena.ucsc.edu/ ) 14. The duplicates and samples with low expression values were removed from the analysis. Dataset with immunotherapy results was obtained from IMvigor210 (Urothelial carcinoma)16. Paired normal to adjacent tumor samples were retrieved from TCGA database to analyze the expression of PRIM2 in tumor and adjacent tumor tissues. Few paracancerous samples for some tumors were available with the TCGA database. Hence data from TCGA was combined with the normal tissues in the GTEx database to analyze the difference in expression of PRIM2 in normal tissues and cancer tissue. p < 0.05 is considered statistically significant. Prognostic value of PRIM2 and its correlation with clinical characteristics Cox regression analysis was used to evaluate the correlation between PRIM2 expression (as a continuous variable) and overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). Kaplan-Meier analysis was used to evaluate the correlation between PRIM2 (as a classification variable) and OS, DSS, DFI, and PFI. R package “survival” and “survminer” was to represent Kaplan-Meier results, and R package “forestplot” was to display Cox regression analysis results. R package “ggpubr” was used to analyze the correlation between PRIM2 and tumor staging. p < 0.05 was considered statistically significant. The correlation between PRIM2 and TMB (ordered by a single nucleotide) and MSI was analyzed. Correlation between PRIM2 and DNA methyltransferase family The expression data of genes from the DNA methyltransferase family ( DNMT1, DNMT3A, DNMT3B ) in pan-cancer tissues and normal tissue were extracted from TCGA, CCLE, and GTEx, respectively, from the UCSC Xena website, to calculate the correlation between PRIM2 and DNMT1, DNMT3A DNMT3B . p < 0.05 was considered statistically significant. Correlation analysis between PRIM2 and immune microenvironment, immune cells, immune-related genes The adjacent sample data from all tumor types were deleted in the pan-cancer tissues. The R package "estimate"17 was used to calculate the immune cell score, stromal cell score, total score, and tumor purity of each sample. Further, their correlation with the PRIM2 in different tumor types was calculated with p < 0.05 and visualized using the R package "ggpur.” Cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to calculate the proportion of 22 immune cell subtypes in each sample 18. xCell estimated the abundance score of 64 immune cell types. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used for single samples that were ineligible for GSEA. ssGSEA was used to extract the enriched fraction of 24 immune cells, and the MCP-counter method was used to calculate the absolute abundance of eight immune cells and two stromal cells 19. CIBERSORT, xCell, GSVA, and MCPcounter algorithm were used to calculate the immune cell score of each tumor tissue in TCGA pan-cancer. The correlation between the expression levels of PRIM2 and the immune cells score was calculated and visualized using the heat map. The correlation p-value and R-value of the four algorithms were used to show the correlation. The expression data for chemokines, chemokine receptors, antigen processing and presentation, and HLA-related genes were retrieved from https://www.immport.org . The expression data for classic immune checkpoint genes such as PD-1 (programmed death receptor 1), PD-L1 (programmed death-ligand 1), CTLA4 (cytotoxic T lymphocyte-associated protein 4), LAG3 (lymphocyte activation gene 3), TIGIT (T cell immune receptor with immunoglobulin and ITIM domain), and TIM-3 (T lymphocyte immunoglobulin mucin 3) was obtained from TCGA pan-cancer for correlation analysis with PRIM2 and visualized using a heat map. The biological function of PRIM2 in different tumors The biological function of PRIM2 in different tumors was assessed. The samples for each cancer were divided into high and low expression groups ​​according to the mean value of PRIM2 . The differences between the high and low expression groups were analyzed, and log FC > 1 was selected and corrected. Genes with p < 0.05 were considered significantly different, and GO analysis was conducted on these genes. GSEA was used to analyze all the genes for differential analysis using c5.all.v7.symbols.GMT file. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the top ten cancer types with the highest incidence and mortality rates were finally selected to study the biological function of PRIM2 . PRIM2 and drug sensitivity analysis The National Cancer Institute (NCI)-60 database includes the expression levels of 22,379 genes in 60 human cancer cell lines and the data of 20,503 analyzed compounds (including 102 drugs approved by the U.S. Food and Drug Administration). The website is https://discover.nci.nih.gov/cellminer20 . Pearson correlation was used to analyze the sensitivity of PRIM2 and the drugs. p < 0.05 was considered statistically significant. Correlation between PRIM2 and immunotherapy To investigate the correlation between PRIM2 and immunotherapy, the transcriptome data and clinical information were obtained from the IMvigor210 study. The survival difference and response to immunotherapy between the PRIM2 high and low expression group was calculated. Further, the correlation between the expression of PRIM2 and different immune response types and clinical subtypes was evaluated. Peng Jiang et al. designed a novel computational framework called Tumor Immune Dysfunction and Rejection Score (TIDE), which uses transcriptional expression data to evaluate the ability of tumor immune escape 21. TIDE scores were calculated for each sample of 33 tumors, and the difference in TIDE scores between PRIM2 high and low expression groups was assessed. Statistical analysis Paired samples from TCGA were analyzed using a paired t-test. The combined analysis of GTEX and TCGA was carried out using t-test. The correlation between PRIM2 and tumor staging was carried out using the Kruskal-Wallis test. Pearson or Spearman’s rank test is used for correlation analyses. p < 0.05 was considered statistically significant. All statistical analyses were carried out using R software (version 4.1.0). Results Expression of PRIM2 gene in pan-cancer The expression of PRIM2 in 18 tumors with matched samples retrieved from TCGA was analyzed (Fig. 1A). No significant difference in the expression of PRIM2 was observed in pancreatic cancer (PAAD) and prostate cancer (PRAD). Compared to paired adjacent normal tissues, a significant difference in PRIM2 expression was observed in tumor samples of 16 cancers like bladder urothelial carcinoma (BLCA), invasive breast carcinoma (BRCA), cholangiocarcinoma (CHOL), colon cancer (COAD), esophageal cancer (ESCA), head and neck squamous cell carcinoma (HNSC), renal papillary cell cancer (KIRP), renal clear cell carcinoma (KIRC), hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), rectal adenocarcinoma (READ), gastric cancer (STAD), endometrial cancer (UCEC). Interestingly, tumor samples from renal chromophobe cell carcinoma (KICH), a thyroid cancer (THCA,) had lower PRIM2 expression than paired adjacent samples. Because some tumors in TCGA, such as adrenal cortical carcinoma (ACC), brain low-grade glioma (LGG), ovarian serous cystic carcinoma (OV), etc., had no or few adjacent samples, the expression in normal tissue samples was retrieved from the GTEx database. As shown in Fig. 1B, the expression of PRIM2 was significantly different in most tumors, except for chromophobe (KICH), pheochromocytoma (PCPG), and paraganglioma sarcoma (SARC). In tumor samples for acute myeloid leukemia (AML) and renal chromophobe cell carcinoma (KICH), the expression of PRIM2 was significantly higher in tumor samples than in normal samples. To fully understand the expression of PRIM2 , its expression was analyzed in samples retrieved from the GTEx database (Fig. 1C). The results reveal that the expression of PRIM2 was highest in prostate and skin tissue, while the expression in liver tissue was relatively low. In the CCLE, PRIM2 expression was relatively high in salivary cells but relatively low in kidney cells (Fig. 1D). Correlation between PRIM2 , clinical features, and its prognostic value The correlation between PRIM2 and tumor stage in different tumors was calculated (Fig. 2A-G). A significant difference in PRIM2 expression between any two of the four tumor stages was required to fit the criteria, and the results showed a significant correlation in seven tumors: BRCA, ESCA, KICH, KIRP, LIHC, LUAD, and THCA. Cox regression analysis evaluated the correlation between the PRIM2 as a continuous variable and the overall survival. Kaplan-Meier analysis divided the PRIM2 levels into high and low expression groups based on the mean expression value of PRIM2 . The correlation between the two groups and OS was studied (Fig. 3A, Figure S1A-I). COX regression analysis showed that PRIM2 expression is correlated with the overall survival in ACC, KICH, KIRP, LGG, LIHC, PAAD, SARC, THYM, UCEC, and in THYM, hazard ratios (HR) were less than 1. Hence PRIM2 was considered a protective factor. On the contrary, for several other tumors, PRIM2 was regarded as an inhibitory factors. The Kaplan-Meier analysis further verified the results. As per the survival curve, the high expression of PRIM2 in THYM indicated a good prognosis, while the high PRIM2 expression in several other tumors indicated a poor prognosis. Cox regression and Kaplan-Meier analysis were used to evaluate the correlation between PRIM2 and DSS (Fig. 3B, Figure S2A-K), DFI (Fig. 3C, Figure S3A-E), and PFI (Fig. 3D, Figure S4A-K). The results show that high expression of PRIM2 in ACC, KICH, KIRC, KIRP, LGG, LIHC, PAAD, PCPG, SARC, and UCEC had a poor prognosis. In KIRC, OV, the high PRIM2 expression has a good prognosis based on DSS, and the results from COX regression analysis and Kaplan-Meier analysis were consistent (except for KIRC). High PRIM2 expression in KIRP, LIHC, LUSC, PAAD, and SARC had a poor prognosis based on DFI and Cox regression analysis. In ACC, KICH, KIRP, LGG, LIHC, MESO, PAAD, SARC, and UCEC, high expression of PRIM2 has a poor prognosis, while in GBM, OV, high expression of PRIM2 has a better prognosis based on PFI and the Cox regression analysis. Correlation between PRIM2 with TMB and MSI TMB and MSI are the hotspots in research due to their association with immunotherapy 22–27. The correlation between PRIM2 and TMB (Fig. 4A) and MSI (Fig. 4B) in pan-cancer was studied. The results showed a statistically significant positive correlation between PRIM2 expression and TMB in SARC, STAD, ACC, LUAD, LGG, PAAD, MESO, BRCA, BLCA, OV, and LUSC tumors. Further, PRIM2 expression negatively correlated with TMB in THCA, PRAD, and THYM, and the correlation was statistically significant. A statistically significant positive correlation was observed between PRIM2 expression and MSI value in STAD and SARC, while in LGG and DLBC, PRIM2 expression negatively correlated with MSI, and the correlation was statistically significant. Correlation between PRIM2 and DNA methyltransferase family (DNMT1, DNMT3A, DNMT3B) DNA methylation plays an important role in biological processes where the gene expression can be altered without changing the DNA sequence 28. The correlation between PRIM2 and DNMT1, DNMT3A , and DNMT3B was studied in pan-cancer from the three databases GTEx (Fig. 5A), TCGA (Fig. 5B), and CCLE (Figure S5A). In the GTEx database, a positive correlation was observed between PRIM2 and DNMT1, DNMT3A , and DNMT3B in most normal tissues. Spleen positively correlated with DNMT1 (correlation value: 0.97), and kidney tissues positively correlated with DNMT3A and DNMT3B (correlations values: 0.97 and 0.92, respectively). Only a few tissues showed a negative correlation. For example, a negative correlation between the ovary and DNMT1 was observed. The prostate negatively correlated with DNMT1 and DNMT3A , and a negative correlation between skin and DNMT3B was observed. In the TCGA database, most tumors showed a positive correlation between P RIM2 and DNMT1, DNMT3A , and DNMT3B. A positive correlation was observed between DNMT1 and DNMT3B in LUAD (the correlation value: 0.85 and 0.79, respectively), and the most relevant correlation was observed between DNMT3A is MESO (the correlation is 0.78). Only DNMT1 is correlated with DNMT1, DNMT3A, and DNMT3B in cancer cell lines, and the correlations are 0.47, 0.06, and 0.15, respectively 29. Correlation between PRIM2 and immune microenvironment, immune cells, immune-related genes The tumor immune microenvironment includes immune cells and stromal cells, which play an important role in tumor development, invasion, and metastasis 29,30. PRIM2 has prognostic value in most tumors. Hence the correlation with tumor microenvironment was evaluated. As shown in Fig. 6A-S, a negative correlation was observed between PRIM2 with immune scores in most tumors, such as BRCA, CESC, ESCA, HNSC, KIRP, LUAD, LUSC, etc. Further, UCEC showed the least correlation value of -0.36. Tumors positively correlated with immune scores were BLCA, KIRC, and LGG, among which the highest correlation was observed with LGG. The infiltration of immune cells can alter the prognosis of tumors 31. Different algorithms were used to explore the relationship between PRIM2 and different types of immune cells (Fig. 7A-D). CIBERSORT is a deconvolution method. The results show that M1 macrophages and PRIM2 positively correlated in most tumor types, and the correlation was significant. The monocytes and PRIM2 negatively correlated with 13 tumor types. In LGG, PRIM2 correlated with 13 immune cell types. A significant positive correlation was observed with resting CD4 memory T cells, and a significant negative correlation was observed with memory B lymphocytes. In BRCA, LUAD, STAD, and THYM, PRIM2 correlated with 12 types of immune cells. In BRCA, a significant positive correlation was observed between PRIM2 and M1 type macrophages; a significant negative correlation was observed between PRIM2 and monocytes. In LUAD and STAD, a significant positive correlation was observed between PRIM2 and M1 type macrophages, and a significant negative correlation was observed between PRIM2 and quiescent mast cells. In THYM, a significant positive correlation was observed between PRIM2 and follicular helper T cells, and a significant negative correlation was observed between PRIM2 and M2 macrophages (Fig. 7A). xCell is a gene set enrichment method that can evaluate the abundance scores of 64 immune cells. As shown in Figure, a strong positive correlation between PRIM2 and Th 2 cells was observed in various tumors. In THYM, a correlation was observed between PRIM2 and most immune cells, of which a significant positive correlation was observed with Th2 cells, and a significant negative correlation was observed with keratinocytes (Fig. 7B). Unlike xCell, ssGSEA is a gene set enrichment method and can evaluate the infiltration of 28 immune cells. ssGSEA results reveal a positive correlation between PRIM2 and activating CD4 T cells, memory B cells, and type 2 helper cells in most tumors. Furthermore, in LUSC, a correlation was observed between PRIM2 and most immune cells. Of which 24 immune cells correlated negatively with PRIM2 , and three immune cells positively related with PRIM2 . In KIRC, PRIM2 positively correlated with most immune cells (Fig. 7C). MCP-counter is used to quantify infiltrating immune cells like fibroblasts and epithelial cells. Figure 7D shows a positive correlation between PRIM2 and centrioles in most tumors and with most immune cells in UVM (Fig. 7D). To fully understand the relationship between PRIM2 and immunity, the correlation between PRIM2 and immune-related cells was further analyzed. Immune-related genes analyzed were antigen presentation and processing, chemokines, chemokine receptors, HLA, and immune checkpoint (Fig. 8A-E). The heat map shows a correlation between PRIM2 and most antigen presentation and processing genes, chemokines, and cytokines in most tumors and positively correlated with various proteasome subunits. Further, a positive correlation was also observed between PRIM2 and immune checkpoints. In BLCA, KIRC, LIHC, PAAD, and UVM, a significant positive correlation between PRIM2 and all immune checkpoints was observed. Role of PRIM2 in different tumors To study the functions of PRIM2 in different tumors, GO enrichment analysis and GSEA were conducted on the top ten tumors with the highest morbidity and mortality. In LUAD, LIHC, STAD, BRCA, and PAAD, the correlation between PRIM2 and pathways associated with mitosis, nuclear division, organelle fission, chromosome separation, etc., were observed. In COAD, ESCA, READ, and CESC, the correlation between PRIM2 with DNA replication and repair were observed. In PRAD, PRIM2 correlated with only zinc ion homeostasis and copper ion detoxification (Fig. 9A). KEGG pathway analysis showed that in LUAD, LIHC, STAD, BRCA, COAD, ESCA, PAAD, READ, PRIM2 positively regulated pathways associated with cell proliferation and division-related, such as DNA replication and cell cycle. PRAD and CESC, PRIM2 positively correlated with metabolic-related pathways, such as arachidonic acid metabolism, unsaturated fatty acid biosynthesis, etc. (Fig. 10A). Analysis of drug sensitivity of PRIM2 The expression of PRIM2 is associated with the prognosis in most tumors, and immune cells and immune-related genes, the correlation between PRIM2 and drug sensitivity was analyzed. The results showed that the PRIM2 positively correlated with fenretinide (tumor preventive drugs; p < 0.01, r = 0.442, Fig. 11A), nelarabine (p = 0.004, r = 0.363, Fig. 11B), dexamethasone (p = 0.014, r = 0.317, Fig. 11C), curcumin (p = 0.022, r = 0.296, Fig. 11D), fluda, Rabine (p = 0.028, r = 0.283, Fig. 11E), and 6-mercaptopurine (p = 0.041, r = 0.268, Fig. 11F). PRIM2 can be used as a potential target for immunotherapy A comprehensive analysis of the IMvigor210 urothelial cancer data set and PRIM2 revealed that the survival rate was higher in the PRIM2 high expression group compared to the low expression group (p = 0.003, Fig. 12A). Compared to the low expression group, the PRIM2 high expression had a better response to PD-L1 immune check blockers (Fisher exact probability test, p = 0.004, Fig. 12B, Kruskal-Wallis test, p < 0.001, Fig. 12C). Further, patients with high expression of PRIM2 had a higher TMB (p = 0.004, Fig. 12D) and a high neoantigen negative (p < 0.001, Fig. 12F). The expression of PRIM2 in the inflammation group in the immunophenotype is also higher than that in the other two groups (p = 0.049, Fig. 12E), and these three indicators are closely related to immunotherapy. Significant difference was observed in the TIDE score between the PRIM2 high and low expression groups in 25 tumors (Fig. 13A-Y). The TIDE score of the PRIM2 high expression group was low compared to the low expression group, indicating that the patients in the PRIM2 low expression group had more obvious immune escape. Discussion Pan-cancer analysis allows simultaneous evaluation of expression and the role of a gene in different tumors, which aids in developing therapy 32. DNA primase is a type of RNA polymerase which synthesizes short RNA primers required by DNA polymerase 33. DNA primase is a heterodimer composed of a small catalytic subunit (PRIM1) and a large auxiliary subunit (PRIM2). Studies have shown that the C-terminal of the large subunit plays a major role in primer synthesis 34. Our team has previously shown high expression of PRIM1 in liver cancer is closely related to pathological staging. Through a series of functional experiments, it has been shown that PRIM1 promotes the proliferation of cells and inhibits apoptosis in liver cancer 35. A similar role of PRIM1 has been observed in estrogen-related breast cancer 36. However, the mechanism of PRIM2 in cancer has not been thoroughly evaluated. To the best of our knowledge, this study, for the first time, explores the expression, prognosis, and biological function of PRIM2 in pan-cancer and its correlation with immunity. Our results show the difference in the expression of PRIM2 in most tumors, irrespective of the data retrieved from TCGA database tumor paired with normal tissues and adjacent cancer tissues or from data retrieved from the GTEx database (cancer and normal tissues). This suggests PRIM2 may play a role in the development and tumorigenesis of some tumors. In BRCA, ESCA, KICH, KIRP, LIHC, LUAD, and THCA tumors, PRIM2 expression is related to the tumor stage. Interestingly higher levels of PRIM2 were observed at advanced stages in ESCA, KICH, and LUAD, suggesting that PRIM2 may be involved in tumor development. Further, the role of PRIM2 in prognosis was studied in 33 tumors, and the results reveal that the expression of PRIM2 is related to OS, DSS, DFI, and PFI in KIRP, LIHC, and PAAD tumors. High PRIM2 expression was associated with poor prognosis in patients. These results indicate oncogenic expression of PRIM2 in some tumors. Mounting evidence has shown that tumor patients with high TMB respond better to immunotherapy 37,38. Our results reveal a positive correlation between PRIM2 and TMB in SARC, STAD, ACC, LUAD, LGG, PAAD, MESO, BRCA, BLCA, OV, and LUSC. The correlation was significant with SARC, while the negative correlation between PRIM2 and TMB was observed in THCA and PRAD, while the negative correlation was significant in THYM. We believe that PRIM2 could cooperate with TMB to predict tumor immunotherapy response. Studies have shown that high MSI has a higher tumor mutation burden and responds better to immunotherapy 39,40. Further, aberrant expression of PRIM2 positively correlated with MSI in STAD and SARC, and a negative correlation was observed between PRIM2 and MSI in THCA, LGG, and DLBC. These results suggest that the expression PRIM2 affects TMB and MSI and may alter immunotherapy response. DNA methyltransferase can affect gene expression at the epigenetic level. DNMT1 regulates the methylation level of X-linked protein 1 (BEX1) expressed in the brain, thereby altering BEX1 expression and ultimately regulating the characteristics of cancer stem cells in the liver 41. In acute myeloid leukemia (AML), about 25% of patients harbor mutations in DNMT3A . DNMT3A-mediated CpG island hypermethylation leads to further deterioration of AML 42. Mutations in DNMT3B cause centromeric instability, immune deficiency, and facial abnormalities, which lead to autosomal recessive genetic diseases 43. Our results show a positive correlation between PRIM2 expression in most normal tissues, tumor tissues, and tumor cell lines, and methyltransferase genes (DNMT1, DNMT3A, DNMT3B). This suggests that methyltransferase genes may alter PRIM2 expression and influence tumor development and prognosis. The tumor immune microenvironment is composed of immune and stromal cells 44. The occurrence of tumors is accompanied by the interaction of complex components in the immune microenvironment 45. The results reveal that PRIM2 correlated with immune cell scores of 19 types of tumors. The correlation was negative in most cases, suggesting that the higher PRIM2 expression lowers the immune cell scores, thus suggesting a poor prognosis in patients with high PRIM2 levels. Studies have shown that infiltrating cells mainly comprise tumor-associated macrophages and play an important role in helping tumor cells escape immune surveillance 46–48. The results reveal that using the CIBSERORT algorithm, PRIM2 and M1 type macrophages show a positive correlation in most tumors. However, contradictory results were obtained using the xCell and ssGSEA algorithm, where a negative correlation was observed between PRIM2 and tumor-associated macrophages. This discrepancy could be due to different principles used by these algorithms. Interestingly, we also observed a positive correlation between PRIM2 with helper Th 2 cells, activated CD4 T cells, and neutrophils in most tumors, indicative of complex regulation of tumor immune microenvironment. The subunit is the main component of the proteasome and plays an important role in the cell cycle, DNA replication, and signal transduction 49. Amongst all the subunits, proteasome 26S subunit ATPase 2 (PSMC2) and the proteasome 26S subunit ATPase 6 (PSMC6) are related to various tumors, such as breast cancer 50, gallbladder cancer 51, prostate cancer 52, lung adenocarcinoma 53. Non-ATPases 1 (PSMD1) and 3 (PSMD3) are also involved in chronic myeloid leukemia 54. Our results show that PRIM2 significantly correlates with proteasome subunit-related genes, suggesting that it may promote tumorigenesis through synergistic action with proteasome subunits and is positively correlated with most antigen presentation-related genes. PRIM2 also positively correlated with chemokines, chemokine receptors, and HLA-related genes. Interestingly, a positive correlation between PRIM2 and major immune checkpoint genes was observed in ACC, BLCA, BRCA, PAAD, and UVM. These results suggest that PRIM2 may help tumors evade immune surveillance. GO and GSEA analysis of the top ten tumors with high mortality and incidence shows the involvement of PRIM2 in the cell cycle, DNA replication, chromatin replication, etc., suggesting that the aberrant expression of PRIM2 may promote the occurrence of tumors by altering various pathways. Further NCI-60 database was used to study the correlation between PRIM2 and drug sensitivity. Results revealed that an increase in PRIM2 expression positively correlated with the drug sensitivity of fenretinide, nelarabine, dexamethasone, curcumin, fludarabine, and 6-mercaptopurine. Thereby indicating that PRIM2 can be used as a drug target. The discovery of the PD-L1 immune checkpoint has provided insight into immunotherapy for solid tumors 55. However, some tumors still do not respond to immunotherapy. Our study reveals that the expression of PRIM2 correlates with the prognosis of patients treated with anti-PD-L1 and the response to anti-PD-L1 therapy. It is well established that tumor mutation burden and neoantigen load can alter immunotherapy response 56–58. Our results show that patients with high expression of PRIM2 have a better prognosis in anti-PD-L1 immunotherapy and have higher tumor mutation burden and neoantigen load. Thus, it is tempting to postulate that expression of PRIM2 sensitizes the tumor to immunotherapy. This study comprehensively analyzes the PRIM2 expression in different tumors from multiple angles, dimensions, and prognosis. We have also established a correlation between PRIM2 with immune genes, immune cells, drug sensitivity, TMB, neoantigen load, and immunophenotypes, which makes PRIM2 potential targets for immunotherapy. The TIDE score was higher in PRIM2 low-expression group in most tumors, suggesting its role in immune escape. However, these discrepancies could be due to the complexity and diversity of the microenvironment in vivo . Hence, additional algorithms or experiments are required to understand the role of PRIM2 in cancer. In short, the results reveal the clinical characteristics, prognosis, and correlation between immune cells and immune-related genes with abnormally expressed PRIM2 in various tumors. However, the samples used in this study were retrieved from publicly available databases, which may impact the results. The function and mechanism of PRIM 2 need to be further verified in vivo , in vitro , and in clinical samples. References Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians 71, 209–249, doi: 10.3322/caac.21660 (2021). Sperduto, P. et al. 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Journal of thoracic oncology: official publication of the International Association for the Study of Lung Cancer 13, 1138–1145, doi: 10.1016/j.jtho.2018.03.035 (2018). Kim, K. et al. Predicting clinical benefit of immunotherapy by antigenic or functional mutations affecting tumour immunogenicity. Nature communications 11, 951, doi: 10.1038/s41467-020-14562-z (2020). Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif FigureS2.tif FigureS3.tif FigureS4.tif FigureS5.tif 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2201390","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":152580864,"identity":"a29a1759-0b62-48f9-b601-0a0bbd46a139","order_by":0,"name":"Jinqun Jiang","email":"","orcid":"","institution":"Yue Bei People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinqun","middleName":"","lastName":"Jiang","suffix":""},{"id":152580865,"identity":"f4cdf613-e3e9-444f-a3d5-58280d776e32","order_by":1,"name":"Hai Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACAxCRUGFTz8/MfPgB8VoenElLkGxnSzMgWgvjw7bDCQbneRQkiNJiLpH+8ENiW1qe8WEeoP4am2iCWixn5BhLJJyzKTY7zHvgAcOxtNwGgg67kcMgkVCWxrjtMF+CAWPDYWK0pD/+kcB2mHFzM4+BBJFaEswkEtoOJ25gJlrLmTdmFgln0owlDgMDOYEovxxPf3zzR4WNHH//4cMPPtTYENbCIJCAxEnAoQgV8B8gStkoGAWjYBSMZAAADBJEYoD1Ty4AAAAASUVORK5CYII=","orcid":"","institution":"The first Hospital of ShaoGuan","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hai","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2022-10-25 08:29:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2201390/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2201390/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":29264374,"identity":"17aac96d-bc92-4a68-ac7e-9379e04ab696","added_by":"auto","created_at":"2022-11-18 22:28:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":801806,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePRIM2\u003c/em\u003eexpression levels in different data sets. A: Difference of \u003cem\u003ePRIM2\u003c/em\u003eexpression in 18 tumors with paired data on cancer and paracancerous in TCGA. B: The difference in \u003cem\u003ePRIM2\u003c/em\u003e expression after combining GTEX normal tissues, TCGA tumors, and adjacent tissues. C: The expression level of \u003cem\u003ePRIM2 \u003c/em\u003ein the GTEX dataset. D: \u003cem\u003ePRIM2\u003c/em\u003e expression levels in different tumor cell lines. *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/f6e3f9bf98bcb85db727a0c5.png"},{"id":29264728,"identity":"3b9e644f-57e2-40ca-8527-fdbcee6f3572","added_by":"auto","created_at":"2022-11-18 22:36:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":474718,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between \u003cem\u003ePRIM2\u003c/em\u003e expression and tumor stage. A: Breast invasive carcinoma (BRCA). B: Esophageal carcinoma (ESCA). C: Kidney Chromophobe (KICH). D: Kidney renal papillary cell carcinoma (KIRP). E: Liver hepatocellular carcinoma (LIHC). F: Lung adenocarcinoma (LUAD). G: Thyroid carcinoma (THCA).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/d0ea20a413e8f26a134c2abb.png"},{"id":29264375,"identity":"8b22e4fd-22f6-40fb-b99a-e3c9dfd1e62e","added_by":"auto","created_at":"2022-11-18 22:28:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":935246,"visible":true,"origin":"","legend":"\u003cp\u003eCox analysis of \u003cem\u003ePRIM2\u003c/em\u003ein different tumors. A: The forest plot shows the Cox regression analysis to compare \u003cem\u003ePRIM2\u003c/em\u003e in different tumors and overall survival (OS). B: The forest plot shows the cox method to compare PRIM2 in different tumors and Disease-specific survival (DSS).C: The forest plot shows the Cox regression analysis to compare \u003cem\u003ePRIM2\u003c/em\u003e in different tumors and disease-free intervals (DFI). D: The forest plot shows the Cox regression analysis to compare \u003cem\u003ePRIM2\u003c/em\u003ein different tumors and progression-free interval (PFI).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/f8c9e2f4ab9371728ef40f68.png"},{"id":29265507,"identity":"cf5f5dee-439b-4760-ab71-a51c2a47f714","added_by":"auto","created_at":"2022-11-18 22:44:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":219074,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between \u003cem\u003ePRIM2\u003c/em\u003e expression level, tumor mutation burden (TMB), and microsatellite instability (MSI) in different tumors. A: The relationship between \u003cem\u003ePRIM2\u003c/em\u003eand TMB. Blue dots represent no correlation, red dots represent correlation, and the number of dots represents how much correlation. B: The relationship between \u003cem\u003ePRIM2 \u003c/em\u003eand MSI. Blue dots represent no correlation, red dots represent correlation, and the number of dots represents how much correlation.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/bf3cf5ce9ce3513babb3724a.png"},{"id":29264724,"identity":"8699df40-b68b-43e5-99d7-c5a64477fa9d","added_by":"auto","created_at":"2022-11-18 22:36:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":484470,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between \u003cem\u003ePRIM2\u003c/em\u003e and DNA methyltransferase is in different data sets. A: The data from Genotype Tissue Expression (GTEx), A dot represents a type of normal tissue. B: In the Cancer Genome Atlas (TCGA), A dot represents a tumor.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/b7ffd6178c8a9a55ca0f3321.png"},{"id":29264726,"identity":"8880d7b8-2b8e-4b9d-a047-6267d4ae0c0a","added_by":"auto","created_at":"2022-11-18 22:36:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1695259,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between \u003cem\u003ePRIM2\u003c/em\u003e and immune scores in the immune microenvironment in different tumors. A: BLCA (Bladder Urothelial Carcinoma). B: BRCA (Breast invasive carcinoma). C: CESC (Cervical squamous cell carcinoma and endocervical adenocarcinoma). D: ESCA (Esophageal carcinoma). E: GBM (Glioblastoma multiforme). F: HNSC (Head and Neck squamous cell carcinoma). G: KIRC (Kidney renal clear cell carcinoma). H: KIRP (Kidney renal papillary cell carcinoma). I: LGG (Brain Lower Grade Glioma). J: LIHC(Liver hepatocellular carcinoma. K:LUAD (Lung adenocarcinoma). L: LUSC (Lung squamous cell carcinoma). M: OV (Ovarian serous cystadenocarcinoma). N: PCPG (Pheochromocytoma and Paraganglioma). O: PRAD (Prostate adenocarcinoma). P: SARC (Sarcoma). Q: SKCM (Skin Cutaneous Melanoma). R: STAD (Stomach adenocarcinoma). S: UCEC (Uterine Corpus Endometrial Carcinoma).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/4d0253d60881a8104a8cf530.png"},{"id":29264376,"identity":"9508449a-1e69-4ab5-afc4-22686ebf20c9","added_by":"auto","created_at":"2022-11-18 22:28:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2520252,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between\u003cem\u003e PRIM2\u003c/em\u003e and immune cells in different tumors is based on four different methods of immune infiltration. A: CIBERSORT. B: xCell C: ssGSEA D: MCPcounter. Red represents positive correlation; purple expressed negative correlation. *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/22a65bcf608b0bb488b68885.png"},{"id":29264730,"identity":"051b1187-77c6-42eb-b7fb-e843187fc385","added_by":"auto","created_at":"2022-11-18 22:36:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":5458787,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between \u003cem\u003ePRIM2\u003c/em\u003e and immune-related genes in different tumors. *p\u0026lt; 0.05, **p \u0026lt; 0.01, ***p\u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/01b3007f4cfffb021bb60f94.png"},{"id":29264379,"identity":"a52c521f-4ff5-4558-930b-fb1ef8f6896f","added_by":"auto","created_at":"2022-11-18 22:28:35","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3370182,"visible":true,"origin":"","legend":"\u003cp\u003ePRIM2's GO analysis is among the top 10 tumors in the latest morbidity and mortality. A: LUAD(Lung adenocarcinoma),LIHC(Liver hepatocellular carcinoma).STAD(Stomach adenocarcinoma).BRCA(Breast invasive carcinoma).COAD(Colon adenocarcinoma).ESCA(Esophageal carcinoma).PAAD(Pancreatic adenocarcinoma).PRAD(Prostate adenocarcinoma).READ(Rectum adenocarcinoma).CESA(Cervical squamous cell carcinoma and endocervical adenocarcinoma)\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/3fc970c322f2c0fd542b6f43.png"},{"id":29265651,"identity":"28a542a5-c7a0-45c1-bf32-6c8390ccc143","added_by":"auto","created_at":"2022-11-18 22:52:35","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1196988,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of PRIM2 gene KEGG pathway in the latest top ten tumors with morbidity and mortality. A:LUAD(Lung adenocarcinoma),LIHC(Liver hepatocellular carcinoma).STAD(Stomach adenocarcinoma).BRCA(Breast invasive carcinoma).COAD(Colon adenocarcinoma).ESCA(Esophageal carcinoma).PAAD(Pancreatic adenocarcinoma).PRAD(Prostate adenocarcinoma).READ(Rectum adenocarcinoma).CESA(Cervical squamous cell carcinoma and endocervical adenocarcinoma)\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/97373af62563772981e0b72e.png"},{"id":29264382,"identity":"fbe0a82f-e10b-410f-a8b9-971061d1997c","added_by":"auto","created_at":"2022-11-18 22:28:35","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":721840,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between \u003cem\u003ePRIM2\u003c/em\u003e expression value and drug sensitivity. The method uses the Spearman rank’s correlation test; p \u0026lt;0.05 is considered statistically significant.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/3e1424c61ceb8eb5429bdae2.png"},{"id":29264386,"identity":"8cb17585-f232-4635-a0b3-4b6a71e8f866","added_by":"auto","created_at":"2022-11-18 22:28:35","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":1641899,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the correlation between \u003cem\u003ePRIM2\u003c/em\u003e and immunotherapy. A: Survival curve of \u003cem\u003ePRIM2\u003c/em\u003ehigh and low expression group. B-C: Ratio of high and low \u003cem\u003ePRIM2\u003c/em\u003eexpression group to immunotherapy response. CR, complete response; SD, stable disease; PR, partial response; PD, progressive disease. D-E: The relationship between high and low PRIM2 expression groups and tumor mutation burden and neoantigen burden. \u0026nbsp;(Wilcoxon signed-rank test) F: Correlation analysis between different immunophenotypes and \u003cem\u003ePRIM2\u003c/em\u003e expression (Wilcoxon signed-rank test)\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/3a57dae13d05b5b3abfcfb8f.png"},{"id":29265508,"identity":"85b8af67-5a11-436d-8dcf-c71a5e7310c6","added_by":"auto","created_at":"2022-11-18 22:44:35","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":1334574,"visible":true,"origin":"","legend":"\u003cp\u003eTIDE score was used to analyze the immune escape of \u003cem\u003ePRIM2\u003c/em\u003e. A: BLCA (Bladder Urothelial Carcinoma). B: BRCA (Breast invasive carcinoma). C: CESC (Cervical squamous cell carcinoma and endocervical adenocarcinoma). D: COAD (Colon adenocarcinoma). E: ESCA (Esophageal carcinoma). F: GBM (Glioblastoma multiforme). G: HNSC (Head and Neck squamous cell carcinoma). H: KICH (Kidney Chromophobe). I: KIRC (Kidney renal clear cell carcinoma). J: KIRP (Kidney renal papillary cell carcinoma). K: LGG (Brain Lower Grade Glioma), L: LIHC (Liver hepatocellular carcinoma), M: LUAD (Lung adenocarcinoma), N: LUSC (Lung squamous cell carcinoma), O: MESO (Mesothelioma) P: OV (Ovarian serous cystadenocarcinoma) Q: PAAD (Pancreatic adenocarcinoma) R: SARC (Sarcoma) S: SKCM (Skin Cutaneous Melanoma) T: STAD (Stomach adenocarcinoma) U: TGCT (Testicular Germ Cell Tumors) V: THYM (Thymoma) W: UCEC (Uterine Corpus Endometrial Carcinoma) X: UCS (Uterine Carcinosarcoma) Y: UVM (Uveal Melanoma)\u003c/p\u003e","description":"","filename":"Figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/724dfc33915c624f57dd8ac4.png"},{"id":48218950,"identity":"564636de-7f3d-4ded-96c1-5ac5d80fc4f7","added_by":"auto","created_at":"2023-12-14 16:52:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8436483,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/3cee9c11-1ec6-4da8-b61c-68aafd7279f9.pdf"},{"id":29264391,"identity":"3833a855-7d36-4437-a642-2dc8e86bf0c8","added_by":"auto","created_at":"2022-11-18 22:28:37","extension":"tif","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":28450084,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/af877d934e877db1ce8f6a18.tif"},{"id":29264390,"identity":"5cce52b7-7149-4fa6-877a-3587c614cceb","added_by":"auto","created_at":"2022-11-18 22:28:36","extension":"tif","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":25345512,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/4852e795b388ba37e222f90f.tif"},{"id":29264388,"identity":"f128dc38-a03a-49c0-ba9d-4d117db55078","added_by":"auto","created_at":"2022-11-18 22:28:36","extension":"tif","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":23144176,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/b18512a22dd9fc1dd8f7eaca.tif"},{"id":29264389,"identity":"c2318b21-1b89-46d9-9ca2-b3069cd7fcb4","added_by":"auto","created_at":"2022-11-18 22:28:36","extension":"tif","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":25173000,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/41ea351d20229daefa630222.tif"},{"id":29264387,"identity":"ee66b42b-a1f5-4a49-8ffa-14f9d414439e","added_by":"auto","created_at":"2022-11-18 22:28:36","extension":"tif","order_by":20,"title":"","display":"","copyAsset":false,"role":"supplement","size":10679032,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS5.tif","url":"https://assets-eu.researchsquare.com/files/rs-2201390/v1/be9ebc997a6a2336ccfcd7e5.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of PRIM2 as a new prognostic and immune-related marker in cancer based on a comprehensive pan-cancer analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer is the second leading cause of mortality worldwide. In 2020, the number of cancer cases increased by approximately 19.3\u0026nbsp;million globally, and about 10\u0026nbsp;million cancer-related death were reported, as per the latest data released by the International Agency for Research on Cancer. This puts an unimaginable burden on the global economy. Breast cancer has surpassed lung cancer as the leading cancer in women worldwide, which calls for attention to women\u0026rsquo;s health 1. With the emergence of targeted immunotherapy, marked improvement in survival has been observed in lung cancers 2, glioblastoma multiforme 3, and melanomas 4. Cancer is a heterogeneous disease due to which many patients with pancreatic cancer 5, hepatocellular carcinoma, and cholangiocarcinoma 6 fail to respond to immunotherapy tumors. The cancer heterogeneity also affects the diagnosis and prognosis of the patients 7. Hence, there is an urgent need for early diagnosis and prognostic biomarkers.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePRIM2\u003c/em\u003e is located on chromosome 6 and encodes for DNA primase subunit 2. It forms a DNA primase complex with the \u003cem\u003ePRIM1\u003c/em\u003e and participates in DNA replication 8. Studies have shown single nucleotide polymorphism in \u003cem\u003ePRIMA2\u003c/em\u003e can affect the prognosis of patients with non-small cell carcinoma 9. Dihydroartemisinin inhibits cell proliferation and colony formation of colonies by targeting \u003cem\u003ePRIMA2\u003c/em\u003e and is used in treating lung cancer 10. High-throughput sequencing of circRNA in liver cancer cells treated with Nitidine Chloride (NC) shows \u003cem\u003ePRIM2\u003c/em\u003e acts as a \"miRNA sponge\" with hsa_circ_0088364 and hsa_circ_0090049 11. In cervical cancer, \u003cem\u003ePRIM2\u003c/em\u003e is regulated by Sine oculis homeobox homolog 1 (\u003cem\u003eSIX1\u003c/em\u003e), which promotes DNA synthesis and increases cell proliferation in cervical cancer 12. It may be the therapeutic target of muaddil sapra in treating acute liver injury 13. These studies provide insight into the role of \u003cem\u003ePRIM2\u003c/em\u003e in cancer; however, additional research is required to understand the association between \u003cem\u003ePRIM2\u003c/em\u003e and cancer.\u003c/p\u003e \u003cp\u003eIn this study, the expression of the \u003cem\u003ePRIM2\u003c/em\u003e in different cancers was studied using different databases. The relationship between \u003cem\u003ePRIM2\u003c/em\u003e and tumor stage, the prognostic significance in cancer, and its correlation with tumor mutation burden (TMB), microsatellite instability (MSI), and DNA methyltransferase (\u003cem\u003eDNMT1, DNMT3A, DNMT3B\u003c/em\u003e) were analyzed. Further, the association of \u003cem\u003ePRIMA2\u003c/em\u003e and immune cells, infiltrating immune cell score, and immune-related genes were evaluated. The biological processes and the pathways enrichment by \u003cem\u003ePRIM2\u003c/em\u003e in the top ten cancers with the highest morbidity and mortality were assessed. Finally, based on the CellMiner database 15, the correlation between \u003cem\u003ePRIM2\u003c/em\u003e and anticancer drugs of 60 cancer cells was analyzed.\u003c/p\u003e"},{"header":"Methods And Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and difference analysis\u003c/h2\u003e \u003cp\u003eThe RNA expression data, tumor mutations, MSI, and clinical data from the Cancer Genome Atlas (TCGA) pan-cancer, cancer cell line encyclopedia (CCLE), and the Genotype-Tissue Expression (GTEx) were retrieved from the University of California, Santa Cruz University (UCSC) Xena browser (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://xena.ucsc.edu/\u003c/span\u003e\u003cspan address=\"http://xena.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) 14. The duplicates and samples with low expression values were removed from the analysis. Dataset with immunotherapy results was obtained from IMvigor210 (Urothelial carcinoma)16.\u003c/p\u003e \u003cp\u003ePaired normal to adjacent tumor samples were retrieved from TCGA database to analyze the expression of \u003cem\u003ePRIM2\u003c/em\u003e in tumor and adjacent tumor tissues. Few paracancerous samples for some tumors were available with the TCGA database. Hence data from TCGA was combined with the normal tissues in the GTEx database to analyze the difference in expression of \u003cem\u003ePRIM2\u003c/em\u003e in normal tissues and cancer tissue. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrognostic value of\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePRIM2\u003c/span\u003e \u003cb\u003eand its correlation with clinical characteristics\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCox regression analysis was used to evaluate the correlation between \u003cem\u003ePRIM2\u003c/em\u003e expression (as a continuous variable) and overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). Kaplan-Meier analysis was used to evaluate the correlation between \u003cem\u003ePRIM2\u003c/em\u003e (as a classification variable) and OS, DSS, DFI, and PFI. R package \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;survminer\u0026rdquo; was to represent Kaplan-Meier results, and R package \u0026ldquo;forestplot\u0026rdquo; was to display Cox regression analysis results.\u003c/p\u003e \u003cp\u003eR package \u0026ldquo;ggpubr\u0026rdquo; was used to analyze the correlation between \u003cem\u003ePRIM2\u003c/em\u003e and tumor staging. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eThe correlation between \u003cem\u003ePRIM2\u003c/em\u003e and TMB (ordered by a single nucleotide) and MSI was analyzed.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation between\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePRIM2\u003c/span\u003e \u003cb\u003eand DNA methyltransferase family\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe expression data of genes from the DNA methyltransferase family (\u003cem\u003eDNMT1, DNMT3A, DNMT3B\u003c/em\u003e) in pan-cancer tissues and normal tissue were extracted from TCGA, CCLE, and GTEx, respectively, from the UCSC Xena website, to calculate the correlation between \u003cem\u003ePRIM2\u003c/em\u003e and \u003cem\u003eDNMT1, DNMT3A DNMT3B\u003c/em\u003e. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation analysis between\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePRIM2\u003c/span\u003e \u003cb\u003eand immune microenvironment, immune cells, immune-related genes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe adjacent sample data from all tumor types were deleted in the pan-cancer tissues. The R package \"estimate\"17 was used to calculate the immune cell score, stromal cell score, total score, and tumor purity of each sample. Further, their correlation with the \u003cem\u003ePRIM2\u003c/em\u003e in different tumor types was calculated with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and visualized using the R package \"ggpur.\u0026rdquo;\u003c/p\u003e \u003cp\u003eCell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to calculate the proportion of 22 immune cell subtypes in each sample 18. xCell estimated the abundance score of 64 immune cell types. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used for single samples that were ineligible for GSEA. ssGSEA was used to extract the enriched fraction of 24 immune cells, and the MCP-counter method was used to calculate the absolute abundance of eight immune cells and two stromal cells 19. CIBERSORT, xCell, GSVA, and MCPcounter algorithm were used to calculate the immune cell score of each tumor tissue in TCGA pan-cancer. The correlation between the expression levels of \u003cem\u003ePRIM2\u003c/em\u003e and the immune cells score was calculated and visualized using the heat map. The correlation p-value and R-value of the four algorithms were used to show the correlation.\u003c/p\u003e \u003cp\u003eThe expression data for chemokines, chemokine receptors, antigen processing and presentation, and HLA-related genes were retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.immport.org\u003c/span\u003e\u003cspan address=\"https://www.immport.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The expression data for classic immune checkpoint genes such as PD-1 (programmed death receptor 1), PD-L1 (programmed death-ligand 1), CTLA4 (cytotoxic T lymphocyte-associated protein 4), LAG3 (lymphocyte activation gene 3), TIGIT (T cell immune receptor with immunoglobulin and ITIM domain), and TIM-3 (T lymphocyte immunoglobulin mucin 3) was obtained from TCGA pan-cancer for correlation analysis with \u003cem\u003ePRIM2\u003c/em\u003e and visualized using a heat map.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe biological function of\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePRIM2\u003c/span\u003e \u003cb\u003ein different tumors\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe biological function of \u003cem\u003ePRIM2\u003c/em\u003e in different tumors was assessed. The samples for each cancer were divided into high and low expression groups ​​according to the mean value of \u003cem\u003ePRIM2\u003c/em\u003e. The differences between the high and low expression groups were analyzed, and log FC\u0026thinsp;\u0026gt;\u0026thinsp;1 was selected and corrected. Genes with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly different, and GO analysis was conducted on these genes. GSEA was used to analyze all the genes for differential analysis using c5.all.v7.symbols.GMT file. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the top ten cancer types with the highest incidence and mortality rates were finally selected to study the biological function of \u003cem\u003ePRIM2\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePRIM2\u003c/span\u003e \u003cb\u003eand drug sensitivity analysis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe National Cancer Institute (NCI)-60 database includes the expression levels of 22,379 genes in 60 human cancer cell lines and the data of 20,503 analyzed compounds (including 102 drugs approved by the U.S. Food and Drug Administration). The website is \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://discover.nci.nih.gov/cellminer20\u003c/span\u003e\u003cspan address=\"https://discover.nci.nih.gov/cellminer20\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Pearson correlation was used to analyze the sensitivity of \u003cem\u003ePRIM2\u003c/em\u003e and the drugs. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation between\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePRIM2\u003c/span\u003e \u003cb\u003eand immunotherapy\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo investigate the correlation between \u003cem\u003ePRIM2\u003c/em\u003e and immunotherapy, the transcriptome data and clinical information were obtained from the IMvigor210 study. The survival difference and response to immunotherapy between the \u003cem\u003ePRIM2\u003c/em\u003e high and low expression group was calculated. Further, the correlation between the expression of \u003cem\u003ePRIM2\u003c/em\u003e and different immune response types and clinical subtypes was evaluated.\u003c/p\u003e \u003cp\u003ePeng Jiang et al. designed a novel computational framework called Tumor Immune Dysfunction and Rejection Score (TIDE), which uses transcriptional expression data to evaluate the ability of tumor immune escape 21. TIDE scores were calculated for each sample of 33 tumors, and the difference in TIDE scores between \u003cem\u003ePRIM2\u003c/em\u003e high and low expression groups was assessed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003ePaired samples from TCGA were analyzed using a paired t-test. The combined analysis of GTEX and TCGA was carried out using t-test. The correlation between \u003cem\u003ePRIM2\u003c/em\u003e and tumor staging was carried out using the Kruskal-Wallis test. Pearson or Spearman\u0026rsquo;s rank test is used for correlation analyses. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were carried out using \u003cem\u003eR\u003c/em\u003e software (version 4.1.0).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eExpression of PRIM2 gene in pan-cancer\u003c/h2\u003e \u003cp\u003eThe expression of \u003cem\u003ePRIM2\u003c/em\u003e in 18 tumors with matched samples retrieved from TCGA was analyzed (Fig.\u0026nbsp;1A). No significant difference in the expression of \u003cem\u003ePRIM2\u003c/em\u003e was observed in pancreatic cancer (PAAD) and prostate cancer (PRAD). Compared to paired adjacent normal tissues, a significant difference in \u003cem\u003ePRIM2\u003c/em\u003e expression was observed in tumor samples of 16 cancers like bladder urothelial carcinoma (BLCA), invasive breast carcinoma (BRCA), cholangiocarcinoma (CHOL), colon cancer (COAD), esophageal cancer (ESCA), head and neck squamous cell carcinoma (HNSC), renal papillary cell cancer (KIRP), renal clear cell carcinoma (KIRC), hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), rectal adenocarcinoma (READ), gastric cancer (STAD), endometrial cancer (UCEC). Interestingly, tumor samples from renal chromophobe cell carcinoma (KICH), a thyroid cancer (THCA,) had lower \u003cem\u003ePRIM2\u003c/em\u003e expression than paired adjacent samples. Because some tumors in TCGA, such as adrenal cortical carcinoma (ACC), brain low-grade glioma (LGG), ovarian serous cystic carcinoma (OV), etc., had no or few adjacent samples, the expression in normal tissue samples was retrieved from the GTEx database. As shown in Fig.\u0026nbsp;1B, the expression of \u003cem\u003ePRIM2\u003c/em\u003e was significantly different in most tumors, except for chromophobe (KICH), pheochromocytoma (PCPG), and paraganglioma sarcoma (SARC). In tumor samples for acute myeloid leukemia (AML) and renal chromophobe cell carcinoma (KICH), the expression of \u003cem\u003ePRIM2\u003c/em\u003e was significantly higher in tumor samples than in normal samples.\u003c/p\u003e \u003cp\u003eTo fully understand the expression of \u003cem\u003ePRIM2\u003c/em\u003e, its expression was analyzed in samples retrieved from the GTEx database (Fig.\u0026nbsp;1C). The results reveal that the expression of \u003cem\u003ePRIM2\u003c/em\u003e was highest in prostate and skin tissue, while the expression in liver tissue was relatively low. In the CCLE, \u003cem\u003ePRIM2\u003c/em\u003e expression was relatively high in salivary cells but relatively low in kidney cells (Fig.\u0026nbsp;1D).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation between\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePRIM2\u003c/span\u003e, \u003cb\u003eclinical features, and its prognostic value\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe correlation between \u003cem\u003ePRIM2\u003c/em\u003e and tumor stage in different tumors was calculated (Fig.\u0026nbsp;2A-G). A significant difference in \u003cem\u003ePRIM2\u003c/em\u003e expression between any two of the four tumor stages was required to fit the criteria, and the results showed a significant correlation in seven tumors: BRCA, ESCA, KICH, KIRP, LIHC, LUAD, and THCA.\u003c/p\u003e \u003cp\u003eCox regression analysis evaluated the correlation between the \u003cem\u003ePRIM2\u003c/em\u003e as a continuous variable and the overall survival. Kaplan-Meier analysis divided the \u003cem\u003ePRIM2\u003c/em\u003e levels into high and low expression groups based on the mean expression value of \u003cem\u003ePRIM2\u003c/em\u003e. The correlation between the two groups and OS was studied (Fig.\u0026nbsp;3A, Figure S1A-I). COX regression analysis showed that \u003cem\u003ePRIM2\u003c/em\u003e expression is correlated with the overall survival in ACC, KICH, KIRP, LGG, LIHC, PAAD, SARC, THYM, UCEC, and in THYM, hazard ratios (HR) were less than 1. Hence \u003cem\u003ePRIM2\u003c/em\u003e was considered a protective factor. On the contrary, for several other tumors, \u003cem\u003ePRIM2\u003c/em\u003e was regarded as an inhibitory factors. The Kaplan-Meier analysis further verified the results. As per the survival curve, the high expression of \u003cem\u003ePRIM2\u003c/em\u003e in THYM indicated a good prognosis, while the high \u003cem\u003ePRIM2\u003c/em\u003e expression in several other tumors indicated a poor prognosis. Cox regression and Kaplan-Meier analysis were used to evaluate the correlation between \u003cem\u003ePRIM2\u003c/em\u003e and DSS (Fig.\u0026nbsp;3B, Figure S2A-K), DFI (Fig.\u0026nbsp;3C, Figure S3A-E), and PFI (Fig.\u0026nbsp;3D, Figure S4A-K). The results show that high expression of \u003cem\u003ePRIM2\u003c/em\u003e in ACC, KICH, KIRC, KIRP, LGG, LIHC, PAAD, PCPG, SARC, and UCEC had a poor prognosis. In KIRC, OV, the high \u003cem\u003ePRIM2\u003c/em\u003e expression has a good prognosis based on DSS, and the results from COX regression analysis and Kaplan-Meier analysis were consistent (except for KIRC). High \u003cem\u003ePRIM2\u003c/em\u003e expression in KIRP, LIHC, LUSC, PAAD, and SARC had a poor prognosis based on DFI and Cox regression analysis. In ACC, KICH, KIRP, LGG, LIHC, MESO, PAAD, SARC, and UCEC, high expression of \u003cem\u003ePRIM2\u003c/em\u003e has a poor prognosis, while in GBM, OV, high expression of \u003cem\u003ePRIM2\u003c/em\u003e has a better prognosis based on PFI and the Cox regression analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between PRIM2 with TMB and MSI\u003c/h2\u003e \u003cp\u003eTMB and MSI are the hotspots in research due to their association with immunotherapy 22\u0026ndash;27. The correlation between \u003cem\u003ePRIM2\u003c/em\u003e and TMB (Fig.\u0026nbsp;4A) and MSI (Fig.\u0026nbsp;4B) in pan-cancer was studied. The results showed a statistically significant positive correlation between \u003cem\u003ePRIM2\u003c/em\u003e expression and TMB in SARC, STAD, ACC, LUAD, LGG, PAAD, MESO, BRCA, BLCA, OV, and LUSC tumors. Further, PRIM2 expression negatively correlated with TMB in THCA, PRAD, and THYM, and the correlation was statistically significant. A statistically significant positive correlation was observed between \u003cem\u003ePRIM2\u003c/em\u003e expression and MSI value in STAD and SARC, while in LGG and DLBC, PRIM2 expression negatively correlated with MSI, and the correlation was statistically significant.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation between\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePRIM2\u003c/span\u003e \u003cb\u003eand DNA methyltransferase family (DNMT1, DNMT3A, DNMT3B)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDNA methylation plays an important role in biological processes where the gene expression can be altered without changing the DNA sequence 28. The correlation between \u003cem\u003ePRIM2\u003c/em\u003e and \u003cem\u003eDNMT1, DNMT3A\u003c/em\u003e, and \u003cem\u003eDNMT3B\u003c/em\u003e was studied in pan-cancer from the three databases GTEx (Fig.\u0026nbsp;5A), TCGA (Fig.\u0026nbsp;5B), and CCLE (Figure S5A). In the GTEx database, a positive correlation was observed between \u003cem\u003ePRIM2\u003c/em\u003e and \u003cem\u003eDNMT1, DNMT3A\u003c/em\u003e, and \u003cem\u003eDNMT3B\u003c/em\u003e in most normal tissues. Spleen positively correlated with DNMT1 (correlation value: 0.97), and kidney tissues positively correlated with \u003cem\u003eDNMT3A\u003c/em\u003e and \u003cem\u003eDNMT3B\u003c/em\u003e (correlations values: 0.97 and 0.92, respectively). Only a few tissues showed a negative correlation. For example, a negative correlation between the ovary and \u003cem\u003eDNMT1\u003c/em\u003e was observed. The prostate negatively correlated with \u003cem\u003eDNMT1\u003c/em\u003e and \u003cem\u003eDNMT3A\u003c/em\u003e, and a negative correlation between skin and \u003cem\u003eDNMT3B\u003c/em\u003e was observed. In the TCGA database, most tumors showed a positive correlation between P\u003cem\u003eRIM2\u003c/em\u003e and \u003cem\u003eDNMT1, DNMT3A\u003c/em\u003e, and \u003cem\u003eDNMT3B.\u003c/em\u003e A positive correlation was observed between \u003cem\u003eDNMT1 and DNMT3B\u003c/em\u003e in LUAD (the correlation value: 0.85 and 0.79, respectively), and the most relevant correlation was observed between DNMT3A is MESO (the correlation is 0.78). Only DNMT1 is correlated with DNMT1, DNMT3A, and DNMT3B in cancer cell lines, and the correlations are 0.47, 0.06, and 0.15, respectively 29.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation between\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePRIM2\u003c/span\u003e \u003cb\u003eand immune microenvironment, immune cells, immune-related genes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe tumor immune microenvironment includes immune cells and stromal cells, which play an important role in tumor development, invasion, and metastasis 29,30. \u003cem\u003ePRIM2\u003c/em\u003e has prognostic value in most tumors. Hence the correlation with tumor microenvironment was evaluated. As shown in Fig.\u0026nbsp;6A-S, a negative correlation was observed between \u003cem\u003ePRIM2\u003c/em\u003e with immune scores in most tumors, such as BRCA, CESC, ESCA, HNSC, KIRP, LUAD, LUSC, etc. Further, UCEC showed the least correlation value of -0.36. Tumors positively correlated with immune scores were BLCA, KIRC, and LGG, among which the highest correlation was observed with LGG.\u003c/p\u003e \u003cp\u003eThe infiltration of immune cells can alter the prognosis of tumors 31. Different algorithms were used to explore the relationship between \u003cem\u003ePRIM2\u003c/em\u003e and different types of immune cells (Fig.\u0026nbsp;7A-D). CIBERSORT is a deconvolution method. The results show that M1 macrophages and \u003cem\u003ePRIM2\u003c/em\u003e positively correlated in most tumor types, and the correlation was significant. The monocytes and \u003cem\u003ePRIM2\u003c/em\u003e negatively correlated with 13 tumor types. In LGG, \u003cem\u003ePRIM2\u003c/em\u003e correlated with 13 immune cell types. A significant positive correlation was observed with resting CD4 memory T cells, and a significant negative correlation was observed with memory B lymphocytes. In BRCA, LUAD, STAD, and THYM, \u003cem\u003ePRIM2\u003c/em\u003e correlated with 12 types of immune cells. In BRCA, a significant positive correlation was observed between \u003cem\u003ePRIM2\u003c/em\u003e and M1 type macrophages; a significant negative correlation was observed between \u003cem\u003ePRIM2\u003c/em\u003e and monocytes. In LUAD and STAD, a significant positive correlation was observed between \u003cem\u003ePRIM2\u003c/em\u003e and M1 type macrophages, and a significant negative correlation was observed between \u003cem\u003ePRIM2\u003c/em\u003e and quiescent mast cells. In THYM, a significant positive correlation was observed between \u003cem\u003ePRIM2\u003c/em\u003e and follicular helper T cells, and a significant negative correlation was observed between \u003cem\u003ePRIM2\u003c/em\u003e and M2 macrophages (Fig.\u0026nbsp;7A). xCell is a gene set enrichment method that can evaluate the abundance scores of 64 immune cells. As shown in Figure, a strong positive correlation between \u003cem\u003ePRIM2\u003c/em\u003e and Th\u003csub\u003e2\u003c/sub\u003e cells was observed in various tumors. In THYM, a correlation was observed between \u003cem\u003ePRIM2\u003c/em\u003e and most immune cells, of which a significant positive correlation was observed with Th2 cells, and a significant negative correlation was observed with keratinocytes (Fig.\u0026nbsp;7B). Unlike xCell, ssGSEA is a gene set enrichment method and can evaluate the infiltration of 28 immune cells. ssGSEA results reveal a positive correlation between \u003cem\u003ePRIM2\u003c/em\u003e and activating CD4 T cells, memory B cells, and type 2 helper cells in most tumors. Furthermore, in LUSC, a correlation was observed between \u003cem\u003ePRIM2\u003c/em\u003e and most immune cells. Of which 24 immune cells correlated negatively with \u003cem\u003ePRIM2\u003c/em\u003e, and three immune cells positively related with \u003cem\u003ePRIM2\u003c/em\u003e. In KIRC, \u003cem\u003ePRIM2\u003c/em\u003e positively correlated with most immune cells (Fig.\u0026nbsp;7C). MCP-counter is used to quantify infiltrating immune cells like fibroblasts and epithelial cells. Figure\u0026nbsp;7D shows a positive correlation between \u003cem\u003ePRIM2\u003c/em\u003e and centrioles in most tumors and with most immune cells in UVM (Fig.\u0026nbsp;7D).\u003c/p\u003e \u003cp\u003eTo fully understand the relationship between \u003cem\u003ePRIM2\u003c/em\u003e and immunity, the correlation between \u003cem\u003ePRIM2\u003c/em\u003e and immune-related cells was further analyzed. Immune-related genes analyzed were antigen presentation and processing, chemokines, chemokine receptors, HLA, and immune checkpoint (Fig.\u0026nbsp;8A-E). The heat map shows a correlation between \u003cem\u003ePRIM2\u003c/em\u003e and most antigen presentation and processing genes, chemokines, and cytokines in most tumors and positively correlated with various proteasome subunits. Further, a positive correlation was also observed between \u003cem\u003ePRIM2\u003c/em\u003e and immune checkpoints. In BLCA, KIRC, LIHC, PAAD, and UVM, a significant positive correlation between \u003cem\u003ePRIM2\u003c/em\u003e and all immune checkpoints was observed.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRole of\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePRIM2\u003c/span\u003e \u003cb\u003ein different tumors\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo study the functions of \u003cem\u003ePRIM2\u003c/em\u003e in different tumors, GO enrichment analysis and GSEA were conducted on the top ten tumors with the highest morbidity and mortality. In LUAD, LIHC, STAD, BRCA, and PAAD, the correlation between \u003cem\u003ePRIM2\u003c/em\u003e and pathways associated with mitosis, nuclear division, organelle fission, chromosome separation, etc., were observed. In COAD, ESCA, READ, and CESC, the correlation between \u003cem\u003ePRIM2\u003c/em\u003e with DNA replication and repair were observed. In PRAD, \u003cem\u003ePRIM2\u003c/em\u003e correlated with only zinc ion homeostasis and copper ion detoxification (Fig.\u0026nbsp;9A). KEGG pathway analysis showed that in LUAD, LIHC, STAD, BRCA, COAD, ESCA, PAAD, READ, \u003cem\u003ePRIM2\u003c/em\u003e positively regulated pathways associated with cell proliferation and division-related, such as DNA replication and cell cycle. PRAD and CESC, \u003cem\u003ePRIM2\u003c/em\u003e positively correlated with metabolic-related pathways, such as arachidonic acid metabolism, unsaturated fatty acid biosynthesis, etc. (Fig.\u0026nbsp;10A).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysis of drug sensitivity of\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePRIM2\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe expression of \u003cem\u003ePRIM2\u003c/em\u003e is associated with the prognosis in most tumors, and immune cells and immune-related genes, the correlation between \u003cem\u003ePRIM2 and\u003c/em\u003e drug sensitivity was analyzed. The results showed that the \u003cem\u003ePRIM2\u003c/em\u003e positively correlated with fenretinide (tumor preventive drugs; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, r\u0026thinsp;=\u0026thinsp;0.442, Fig.\u0026nbsp;11A), nelarabine (p\u0026thinsp;=\u0026thinsp;0.004, r\u0026thinsp;=\u0026thinsp;0.363, Fig.\u0026nbsp;11B), dexamethasone (p\u0026thinsp;=\u0026thinsp;0.014, r\u0026thinsp;=\u0026thinsp;0.317, Fig.\u0026nbsp;11C), curcumin (p\u0026thinsp;=\u0026thinsp;0.022, r\u0026thinsp;=\u0026thinsp;0.296, Fig.\u0026nbsp;11D), fluda, Rabine (p\u0026thinsp;=\u0026thinsp;0.028, r\u0026thinsp;=\u0026thinsp;0.283, Fig.\u0026nbsp;11E), and 6-mercaptopurine (p\u0026thinsp;=\u0026thinsp;0.041, r\u0026thinsp;=\u0026thinsp;0.268, Fig.\u0026nbsp;11F).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePRIM2\u003c/span\u003e \u003cb\u003ecan be used as a potential target for immunotherapy\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA comprehensive analysis of the IMvigor210 urothelial cancer data set and \u003cem\u003ePRIM2\u003c/em\u003e revealed that the survival rate was higher in the \u003cem\u003ePRIM2\u003c/em\u003e high expression group compared to the low expression group (p\u0026thinsp;=\u0026thinsp;0.003, Fig.\u0026nbsp;12A). Compared to the low expression group, the \u003cem\u003ePRIM2\u003c/em\u003e high expression had a better response to PD-L1 immune check blockers (Fisher exact probability test, p\u0026thinsp;=\u0026thinsp;0.004, Fig.\u0026nbsp;12B, Kruskal-Wallis test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;12C). Further, patients with high expression of \u003cem\u003ePRIM2\u003c/em\u003e had a higher TMB (p\u0026thinsp;=\u0026thinsp;0.004, Fig.\u0026nbsp;12D) and a high neoantigen negative (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;12F). The expression of \u003cem\u003ePRIM2\u003c/em\u003e in the inflammation group in the immunophenotype is also higher than that in the other two groups (p\u0026thinsp;=\u0026thinsp;0.049, Fig.\u0026nbsp;12E), and these three indicators are closely related to immunotherapy.\u003c/p\u003e \u003cp\u003eSignificant difference was observed in the TIDE score between the \u003cem\u003ePRIM2\u003c/em\u003e high and low expression groups in 25 tumors (Fig.\u0026nbsp;13A-Y). The TIDE score of the \u003cem\u003ePRIM2\u003c/em\u003e high expression group was low compared to the low expression group, indicating that the patients in the \u003cem\u003ePRIM2\u003c/em\u003e low expression group had more obvious immune escape.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePan-cancer analysis allows simultaneous evaluation of expression and the role of a gene in different tumors, which aids in developing therapy 32. DNA primase is a type of RNA polymerase which synthesizes short RNA primers required by DNA polymerase 33. DNA primase is a heterodimer composed of a small catalytic subunit (PRIM1) and a large auxiliary subunit (PRIM2). Studies have shown that the C-terminal of the large subunit plays a major role in primer synthesis 34. Our team has previously shown high expression of \u003cem\u003ePRIM1\u003c/em\u003e in liver cancer is closely related to pathological staging. Through a series of functional experiments, it has been shown that \u003cem\u003ePRIM1\u003c/em\u003e promotes the proliferation of cells and inhibits apoptosis in liver cancer 35. A similar role of \u003cem\u003ePRIM1\u003c/em\u003e has been observed in estrogen-related breast cancer 36. However, the mechanism of \u003cem\u003ePRIM2\u003c/em\u003e in cancer has not been thoroughly evaluated.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this study, for the first time, explores the expression, prognosis, and biological function of \u003cem\u003ePRIM2\u003c/em\u003e in pan-cancer and its correlation with immunity. Our results show the difference in the expression of \u003cem\u003ePRIM2\u003c/em\u003e in most tumors, irrespective of the data retrieved from TCGA database tumor paired with normal tissues and adjacent cancer tissues or from data retrieved from the GTEx database (cancer and normal tissues). This suggests \u003cem\u003ePRIM2\u003c/em\u003e may play a role in the development and tumorigenesis of some tumors. In BRCA, ESCA, KICH, KIRP, LIHC, LUAD, and THCA tumors, \u003cem\u003ePRIM2\u003c/em\u003e expression is related to the tumor stage. Interestingly higher levels of \u003cem\u003ePRIM2\u003c/em\u003e were observed at advanced stages in ESCA, KICH, and LUAD, suggesting that \u003cem\u003ePRIM2\u003c/em\u003e may be involved in tumor development. Further, the role of \u003cem\u003ePRIM2\u003c/em\u003e in prognosis was studied in 33 tumors, and the results reveal that the expression of \u003cem\u003ePRIM2\u003c/em\u003e is related to OS, DSS, DFI, and PFI in KIRP, LIHC, and PAAD tumors. High \u003cem\u003ePRIM2\u003c/em\u003e expression was associated with poor prognosis in patients. These results indicate oncogenic expression of \u003cem\u003ePRIM2\u003c/em\u003e in some tumors.\u003c/p\u003e \u003cp\u003eMounting evidence has shown that tumor patients with high TMB respond better to immunotherapy 37,38. Our results reveal a positive correlation between \u003cem\u003ePRIM2\u003c/em\u003e and TMB in SARC, STAD, ACC, LUAD, LGG, PAAD, MESO, BRCA, BLCA, OV, and LUSC. The correlation was significant with SARC, while the negative correlation between \u003cem\u003ePRIM2\u003c/em\u003e and TMB was observed in THCA and PRAD, while the negative correlation was significant in THYM. We believe that \u003cem\u003ePRIM2\u003c/em\u003e could cooperate with TMB to predict tumor immunotherapy response. Studies have shown that high MSI has a higher tumor mutation burden and responds better to immunotherapy 39,40. Further, aberrant expression of \u003cem\u003ePRIM2\u003c/em\u003e positively correlated with MSI in STAD and SARC, and a negative correlation was observed between \u003cem\u003ePRIM2\u003c/em\u003e and MSI in THCA, LGG, and DLBC. These results suggest that the expression \u003cem\u003ePRIM2\u003c/em\u003e affects TMB and MSI and may alter immunotherapy response.\u003c/p\u003e \u003cp\u003eDNA methyltransferase can affect gene expression at the epigenetic level. \u003cem\u003eDNMT1\u003c/em\u003e regulates the methylation level of X-linked protein 1 (BEX1) expressed in the brain, thereby altering \u003cem\u003eBEX1\u003c/em\u003e expression and ultimately regulating the characteristics of cancer stem cells in the liver 41. In acute myeloid leukemia (AML), about 25% of patients harbor mutations in \u003cem\u003eDNMT3A\u003c/em\u003e. DNMT3A-mediated CpG island hypermethylation leads to further deterioration of AML 42. Mutations in \u003cem\u003eDNMT3B\u003c/em\u003e cause centromeric instability, immune deficiency, and facial abnormalities, which lead to autosomal recessive genetic diseases 43. Our results show a positive correlation between \u003cem\u003ePRIM2\u003c/em\u003e expression in most normal tissues, tumor tissues, and tumor cell lines, and methyltransferase genes (DNMT1, DNMT3A, DNMT3B). This suggests that methyltransferase genes may alter \u003cem\u003ePRIM2\u003c/em\u003e expression and influence tumor development and prognosis.\u003c/p\u003e \u003cp\u003eThe tumor immune microenvironment is composed of immune and stromal cells 44. The occurrence of tumors is accompanied by the interaction of complex components in the immune microenvironment 45. The results reveal that \u003cem\u003ePRIM2\u003c/em\u003e correlated with immune cell scores of 19 types of tumors. The correlation was negative in most cases, suggesting that the higher \u003cem\u003ePRIM2\u003c/em\u003e expression lowers the immune cell scores, thus suggesting a poor prognosis in patients with high \u003cem\u003ePRIM2\u003c/em\u003e levels. Studies have shown that infiltrating cells mainly comprise tumor-associated macrophages and play an important role in helping tumor cells escape immune surveillance 46\u0026ndash;48. The results reveal that using the CIBSERORT algorithm, \u003cem\u003ePRIM2\u003c/em\u003e and M1 type macrophages show a positive correlation in most tumors. However, contradictory results were obtained using the xCell and ssGSEA algorithm, where a negative correlation was observed between \u003cem\u003ePRIM2\u003c/em\u003e and tumor-associated macrophages. This discrepancy could be due to different principles used by these algorithms. Interestingly, we also observed a positive correlation between \u003cem\u003ePRIM2\u003c/em\u003e with helper Th\u003csub\u003e2\u003c/sub\u003e cells, activated CD4 T cells, and neutrophils in most tumors, indicative of complex regulation of tumor immune microenvironment. The subunit is the main component of the proteasome and plays an important role in the cell cycle, DNA replication, and signal transduction 49. Amongst all the subunits, proteasome 26S subunit ATPase 2 (PSMC2) and the proteasome 26S subunit ATPase 6 (PSMC6) are related to various tumors, such as breast cancer 50, gallbladder cancer 51, prostate cancer 52, lung adenocarcinoma 53. Non-ATPases 1 (PSMD1) and 3 (PSMD3) are also involved in chronic myeloid leukemia 54. Our results show that \u003cem\u003ePRIM2\u003c/em\u003e significantly correlates with proteasome subunit-related genes, suggesting that it may promote tumorigenesis through synergistic action with proteasome subunits and is positively correlated with most antigen presentation-related genes. \u003cem\u003ePRIM2\u003c/em\u003e also positively correlated with chemokines, chemokine receptors, and HLA-related genes. Interestingly, a positive correlation between \u003cem\u003ePRIM2\u003c/em\u003e and major immune checkpoint genes was observed in ACC, BLCA, BRCA, PAAD, and UVM. These results suggest that \u003cem\u003ePRIM2\u003c/em\u003e may help tumors evade immune surveillance.\u003c/p\u003e \u003cp\u003eGO and GSEA analysis of the top ten tumors with high mortality and incidence shows the involvement of \u003cem\u003ePRIM2\u003c/em\u003e in the cell cycle, DNA replication, chromatin replication, etc., suggesting that the aberrant expression of \u003cem\u003ePRIM2\u003c/em\u003e may promote the occurrence of tumors by altering various pathways.\u003c/p\u003e \u003cp\u003eFurther NCI-60 database was used to study the correlation between \u003cem\u003ePRIM2\u003c/em\u003e and drug sensitivity. Results revealed that an increase in \u003cem\u003ePRIM2\u003c/em\u003e expression positively correlated with the drug sensitivity of fenretinide, nelarabine, dexamethasone, curcumin, fludarabine, and 6-mercaptopurine. Thereby indicating that \u003cem\u003ePRIM2\u003c/em\u003e can be used as a drug target.\u003c/p\u003e \u003cp\u003eThe discovery of the PD-L1 immune checkpoint has provided insight into immunotherapy for solid tumors 55. However, some tumors still do not respond to immunotherapy. Our study reveals that the expression of \u003cem\u003ePRIM2\u003c/em\u003e correlates with the prognosis of patients treated with anti-PD-L1 and the response to anti-PD-L1 therapy. It is well established that tumor mutation burden and neoantigen load can alter immunotherapy response 56\u0026ndash;58. Our results show that patients with high expression of \u003cem\u003ePRIM2\u003c/em\u003e have a better prognosis in anti-PD-L1 immunotherapy and have higher tumor mutation burden and neoantigen load. Thus, it is tempting to postulate that expression of \u003cem\u003ePRIM2\u003c/em\u003e sensitizes the tumor to immunotherapy.\u003c/p\u003e \u003cp\u003eThis study comprehensively analyzes the \u003cem\u003ePRIM2\u003c/em\u003e expression in different tumors from multiple angles, dimensions, and prognosis. We have also established a correlation between \u003cem\u003ePRIM2\u003c/em\u003e with immune genes, immune cells, drug sensitivity, TMB, neoantigen load, and immunophenotypes, which makes \u003cem\u003ePRIM2\u003c/em\u003e potential targets for immunotherapy. The TIDE score was higher in \u003cem\u003ePRIM2\u003c/em\u003e low-expression group in most tumors, suggesting its role in immune escape. However, these discrepancies could be due to the complexity and diversity of the microenvironment \u003cem\u003ein vivo\u003c/em\u003e. Hence, additional algorithms or experiments are required to understand the role of \u003cem\u003ePRIM2\u003c/em\u003e in cancer.\u003c/p\u003e \u003cp\u003eIn short, the results reveal the clinical characteristics, prognosis, and correlation between immune cells and immune-related genes with abnormally expressed \u003cem\u003ePRIM2\u003c/em\u003e in various tumors. However, the samples used in this study were retrieved from publicly available databases, which may impact the results. The function and mechanism of \u003cem\u003ePRIM\u003c/em\u003e2 need to be further verified \u003cem\u003ein vivo\u003c/em\u003e, \u003cem\u003ein vitro\u003c/em\u003e, and in clinical samples.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung, H. et al. 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However, the role of PRIM2 in cancer and immune regulation has not been elucidated. Therefore, in the present study, the expression profile of PRIM2 in cancer was investigated using publicly available databases like the Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx), the Broad Institute Cancer Cell Line Encyclopedia (CCLE) from the University of California Santa Cruz (UCSC) Xena website. PRIM2 expression was analyzed in paired normal to the adjacent tumor and unpaired cancer and normal tissues. The prognosis of PRIM2 in various cancers was studied using Cox regression and Kaplan-Meier analysis. The relationship between PRIM2 and tumor staging, tumor mutation burden (TMB), and microsatellite instability (MSI) was analyzed. Algorithms like cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT), xCell, ssGSEA, and MCP-counter were used to study the correlation between PRIMA2 and tumor immune microenvironment, immune infiltrating cells, and immune-related genes (antigen processing and presentation genes, chemokines, chemokine receptors, HLA-related genes, immune checkpoints). Further, the correlation between PRIM2 and methyltransferase (DNMT1, DNMT3A, DNMT3B) from different databases. The biological processes and signaling pathways associated with PRIM2 in various tumors were studied. Finally, the correlation between the PRIM2 and the sensitivity of multiple drugs was analyzed using the National Cancer Institute (NCI)-60 database. The results show that PRIM2 was up-regulated in most tumors, high PRIM2 expression was associated with the different stages of cancer, and poor prognosis was observed. The results indicate that PRIM2 could potentially be used as a prognostic and immunotherapy target in tumors.","manuscriptTitle":"Identification of PRIM2 as a new prognostic and immune-related marker in cancer based on a comprehensive pan-cancer analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-18 22:28:29","doi":"10.21203/rs.3.rs-2201390/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":"95aca86c-f9a5-481b-938b-6168d43698f4","owner":[],"postedDate":"November 18th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":17010586,"name":"Health sciences/Medical research/Biomarkers"},{"id":17010587,"name":"Health sciences/Diseases/Cancer/Breast cancer"}],"tags":[],"updatedAt":"2023-12-14T16:44:23+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-18 22:28:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2201390","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2201390","identity":"rs-2201390","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}