Systemic Pan-Caner Analysis Identifies CACYBP as a Novel Biomarker for Cancer Prognosis and Immunity

Systemic Pan-Caner Analysis Identifies CACYBP as a Novel Biomarker for Cancer Prognosis and Immunity | 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 Systemic Pan-Caner Analysis Identifies CACYBP as a Novel Biomarker for Cancer Prognosis and Immunity He-jun Liang, Lan-hui Lin, Zhi-yu Li, Jing-yi Zhu, Feng Gu, Lei Ma, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3247132/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 Calcyclin-binding protein or siah-1-interacting protein (CACYBP/SIP), a target protein of calcyclin S100A6 and an essential component of E3 ubiquitin ligase, had been proven to play significant roles in some cancers, but its pan-cancer function remains unknown. In the present study, we used a series of databases, including TCGA, GTEx, CPTAC, HPA, cBioPortal, UCLCAN, UCSC, CancerSCEM, CancerSEA, CancerSEA, GEPIA2 and STRING to explore the potential roles of CACYBP in pan-cancer. We systematically revealed the expression patterns of CACYBP, and the potential associations between CACYB expression and genetic alternation, prognosis, DNA methylation, RNA modification, immune reactivity, tumor stemness and enrichment pathways in pan-cancer. The results showed that CACYBP was significantly increased in various cancers compared to corresponding normal tissues. CACYBP mutation was frequently presented in various cancers. In addition, CACYBP expression was significantly correlated with prognosis, DNA methylation, RNA methylation, immune cells infiltration, immune checkpoint genes (ICGs), immune scores, tumor mutational burden (TMB), microsatellite instability (MSI) and tumor stemness in various cancers. We also discovered that CACYBP was abundantly highly expressed in the majority of cancers at a single-cell level and was significantly positively correlated to the single-cell functions of certain tumors, such as the cell cycle, DNA damage and DNA repair. Furthermore, CACYBP-related genes were mainly enriched in signaling pathways correlated with the tumor microenvironment (TME) and cancer development. Taken together, CACYBP plays an essential role in oncogenesis, and might serve as a promising prognostic biomarker and immunotherapeutic target in human cancers. Biological sciences/Cancer Health sciences/Biomarkers CACYBP pan-cancer bioinformatics immunity biomarker 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 1 Introduction Cancer is a leading cause of death globally with increasing incidence and mortality rates annually and is second only to cardiovascular disease for the number of deaths, years of life lost and disability-adjusted life years, causing a huge burden on the global economy and health [ 1 , 2 ] . Despite advances in treatment technology, cancer remains one of the most dreaded diseases with a poor prognosis. More specific cancer-related therapeutic targets are necessary to effectively prevent and control cancer, and their molecular mechanisms need to be determined. Fortunately, with the development of bioinformatics technology and the appearance of numerous public large-scale databases, enabling researchers to conduct comprehensive analyses of cancer-related genes in multiple omics and dimensions way [ 3 ] . Calcyclin-binding protein (CACYBP), as a binding partner of S100A6, was initially found in the cytoplasm of Ehrlich ascites tumor cells and was subsequently identified as a siah-1 interaction protein (SIP) [ 4 – 6 ] . CACYBP expression levels differ in normal body tissues, with the highest in brain and spleen, the middle in stomach, liver and heart, and the lowest in kidney, lymph node and rectum [ 7 , 8 ] . Since CACYBP has been found, increasing evidence confirms that CACYBP involving in various cellular processes, including ubiquitination degradation, cell proliferation, cell differentiation, cytoskeletal rearrangement, transcriptional regulation and tumorigenesis through binding to various ligands, playing a critical role in human diseases. For example, CACYBP was found highly expressed in myocardial infarction and exerted a heart-protected effect by promoting DNA synthesis and differentiation of cardiomyocytes [ 9 ] . CACYBP was also found highly expressed in multiple brain structures and bound with tubulin, actin, tropomyosin, tau and heat shock protein 90 (Hsp90), played a critical role in neurodegenerative disorders, such as Alzheimer's disease, Parkinson’s disease and Huntington’s disease [ 10 , 11 ] . In human cancers, the role of CAYBP is still controversy and may depend on cell types. CACYBP was highly expressed in several cancers, such as pancreatic cancer, colon cancer and nasopharyngeal cancer, contributing to poor prognosis, recurrence and metastasis [ 12 – 14 ] . Our team revealed that CACYBP nuclear translocation may contribute to the progression of gastric cancer and colon cancer [ 15 – 17 ] . Conversely, the expression of CACYBP was markedly down-regulated in renal cancer and glioblastoma, while up-regulated CACYBP expression can suppress its migration and invasion [ 18 , 19 ] . In addition to some cancer types, the role of CACYBP in multiple cancers remains unclear. Considering the complexity of tumorigenesis, it is imperative to evaluate CACYBP expression in pan-cancer and to analyze its relevance to prognosis and potential molecular mechanisms. In the present study, we conducted a systemic and integrative pan-cancer analysis for the CACYBP gene via the bioinformatics method. We aim to illustrate the expression pattern, genetic alternation, prognosis, DNA methylation, RNA methylation, immune reactivity, cancer stemness, single-cell function, gene interaction and enrichment pathway of CACYBP in human cancers. 2 Materials and methods 2.1 Analysis of mRNA and protein expression The mRNA expression profiles of CACYBP in pan-cancer and corresponding normal tissues with a “TMP” format were obtained from the TCGA ( https://portal.gdc.cancer . gov, accessed on 15 June 2023) and GTEx database ( https://commonfund.nih.Gov/ GTEx, accessed on 15 June 2023). Each expression value was transformed using the log2 method. Then the differential mRNA expression analyses were performed with R software (Version 4.1.2) (“ggplot2 package”). The protein expression profiles of CACYBP in pan-cancer and corresponding normal tissues with a “TMP” format were obtained from the CPTAC module of the UALCAN database ( https://ualcan.path.uab.edu/analysis-prot.html , accessed on 15 June 2023) [ 20 ] . And the differential protein expression analyses were conducted using the GEPIA2 database ( https://gepia2.cancer-pku.cn/ , accessed on 15 June 2023) with the default parameters (“log2FC cutoff = 1, p -value cutoff = 0.01”) and matched the normal data from the TCGA and GTEx database [ 21 ] . The immunohistochemistry (IHC) data of CACYBP was obtained from the HPA database ( https://www.proteinatlas.org/ , accessed on 15 June 2023). 2.2 Analysis of genetic alternation The cBioPortal ( https://www.cbioportal.org/ , accessed on 15 June 2023) is a visual and multidimensional genomic public database, containing genomic characteristics of cancers at the DNA level [ 22 ] . In the present study, cBioPortal was used to explore the genetic alternations of CACYBP in pan-cancer based on “TCGA Pan Cancer Atlas Studies”, including mutation frequency and mutation type. In addition, the schematic diagram of the protein structure with mutation frequency, mutation type and enrich tumor type was performed based on the “mutation” module of this database. The highest mutation site was highlighted in the three-dimensional (3D) structure of CACYBP protein. 2.3 Analysis of survival and prognosis We used the GEPIA2 database ( https://gepia2.cancer-pku . cn/, accessed on 15 June 2023) to analyze survival points, including overall survival (OS) and disease-specific survival (DSS) from the TCGA database [ 21 ] . According to the median expression levels of CACYBP in pan-cancer, patients were divided into high- and low- expression subgroups and Kaplan-Meier analyses were performed to compare the survival time. Then diagnostic ROC analyses were conducted to identify the diagnostic value of CACYBP in certain cancers. In addition, time-dependent ROC analyses were conducted with R-software (“timeROC” package) to evaluate the predictive power of CACYBP for 1-, 3- and 5- year survival in several cancers. 2.4 Analysis of DNA methylation and MMR genes UCLAN ( https://ualcan.path.uab.edu/analysis.html , accessed on 20 June 2023) is a comprehensive omics database that allows researchers to perform gene expression and gene promoter methylation analyses [ 23 ] . “TCGA” and “methylation” modules of the UCLAN database were used to compare promoter DNA methylation levels between tumors and corresponding normal tissues. In addition, five critical mismatch modified genes (MMRs), including MLH1, MSH2, MSH6, PMS2, and EPCAM were selected to evaluate correlations with CACYB expression in TCGA pan-cancer. We further evaluated the correlations between four methyltransferases (DNMT1, DNMT2, DNMT3A, and DNMT3B) and CACYBP expression in TCGA pan-cancer. 2.5 Analysis of RNA methylation regulatory genes We downloaded the unified and standardized TCGA Pan-Cancer (PANCAN, N = 10535, G = 60499) dataset from the UCSC database ( https://xenabrowser.net/ , accessed on 20 June 2023). And the expression data of the ENSG00000116161 (CACYBP) gene and 44 marker genes of RNA modification genes (m1A, m5C and m6A) in each sample were extracted. The data was then log2(x + 0.001) transformed and the correlations between CACYBP and 44 marker genes were calculated with the spearman method. Finally, the correlation heat map is drawn by the SangerBox online tool ( http://sangerbox.com/ , accessed on 20 June 2023). 2.6 Analysis of immune reactivity The data of CACYBP expression and corresponding abundances of six types of immune cells, including B cells, neutrophils, macrophages, CD4 + T cells, CD8 + T cells, and dendritic cells was obtained from the TCGA database. Correlation coefficients between CACYBP and these immune cells were calculated and then transformed into three types of immune scores: the ESTIMATE score, the Immune score and the Stromal score via R software (“ESTIMATE” and “limma” package). Subsequently, the correlations between CACYBP and the three immune scores were further analyzed. In addition, the “Immune-Gene” module of the TIMER2 database ( http://timer.cistrome.org/ , accessed on 20 June 2023) was used to evaluate the infiltration levels of 22 immune cell subtypes [ 24 ] . Seven immune-related machine learning algorithms, including Timer, EPIC, IPS, MCPcount, xCELL, CIBERSORT, and QUANTISEQ were used to evaluate the immune infiltration of three critical immune cells infiltration (cancer-associated fibroblasts, CD4 + T cells and CD8 + T cells ). Furthermore, expression profiles of immune checkpoints, MSI and TMB were obtained from the TCGA database. Then the spearman correlation analyses of CACYBP expression with immune checkpoints, MSI and TMB in TCGA pan-caner were performed. 2.7 Analysis of single-cell expression and function CancerSCEM ( https://ngdc.cncb.ac.cn/cancerscem/index , accessed on 20 June 2023) is a public tool for studying tumor microenvironment, immune landscape and cellular heterogeneity within various cancers, integrating seven analysis functions, where researchers can perform scRNA-seq analyses [ 25 ] . In the present study, CancerSCEM was used to quantify tumor microenvironment. Cellular heterogeneity of CACYBP expression in all single cancer samples was quantified based on the database. And scRNA-seq analyses were performed to visualize the “UMPA_1” and “TSNE_1” landscapes and to quantify immune cellular heterogeneity of CACYBP in several cancers. CancerSEA ( http://biocc.hrbmu.edu.cn/CancerSEA/ , accessed on 20 June 2023) is a dedicated database to decode functional states of cancer single cells, containing 14 functional states of 41 900 cancer single cells from 25 cancer types [ 26 ] . We used the CancerSEA database to determine the correlation of CACYBP with 14 function states, including angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, EMT, hypoxia, inflammation, invasion, metastasis, proliferation, quiescence, and stemness in several tumors at the single-cell level. And the functional states that meet the standard of “|cor|>0.3, P < 0.05” were shown. 2.8 Analysis of tumor stemness Tumor stemness data, including DNAss, RNAss, DMPss, ENHss, EREG-EXPss and EREG-METHss was obtained from prior studies, and was used to evaluate the oncogenic dedifferentiation ability [ 27 ] . In the present study, we intersected CACYBP expression data with six stemness indices to conduct the Spearman correlation analysis. 2.9 Analysis of CACYBP-related genes The top 50 experimentally determined CACYBP-interacted genes were downloaded from the STRING database ( https://string-db.org/ , accessed on 22 June 2023), and visualized with a protein-protein interaction (PPI) network. The top 100 CACYBP correlated genes were obtained from the “Similar Genes Detection” module of the GEPIA2 database, and the correlations between the top 5 correlated genes and CACYBP in pan-cancer were analyzed. Venn plot analysis was performed to identify the common genes among the CACYBP interacted and correlated genes via R-software ( “ggplot2” and “VennDiagram” packages). KEGG and GO enrichment analyses were performed on the CACYBP-interacted genes from the STRING and visualized via the SangerBox online tool. 2.10. Statistical Analysis An unpaired Wilcoxon crossover test was used to analyze the differential expression of CACYBP in pan-cancer. Student’s t -test was used to analyze the differential promoter methylation level. Log-rank test was used to analyze the survival curves. The spearman method was used to calculate the correlation coefficients in the study. * p < 0.05, ** p < 0.01,*** p < 0.001 and **** p < 0.0001 were considered statistically significant in this study. 3 Results 3.1 Expression pattern of CACYBP in pan-cancer Among the 33 TCGA tumors, CACYBP mRNA expression was substantially up-regulated in 25 tumors, including AAC, BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, LGG, LIHC, LUAD, LUSC, OV, PAAD, PRAD, READ, SKCM, STAD, TGCT, THCA, THYM, UCEC and UCS. Conversely, we found substantial down-regulation of CACYBP in KICH and LAML (Fig. 1 A). In addition, CACYBP protein expression was substantially up-regulated in 8 tumors compared to corresponding normal tissues, including BRCA, COAD, OV, UCEC, LUAD, PAAD, HNNC and LIHC (Fig. 1 B). Furthermore, representative IHC results showed that CACYBP gene expression was dramatically increased in breast cancer, colon cancer, lung cancer and liver cancer tissues compared to corresponding healthy human tissues (Fig. 1 C). 3.2 Genetic alternations and features of CACYBP in pan-cancer Genetic alternations of genes had been proven to be significantly correlated with tumorigenesis. We evaluated the genetic alternation type and frequency of CACYBP in pan-cancer via the cBioPortal database. As shown in Fig. 2 A, we found three main genetic change types of CACYBP including amplification, mutation and deep deletion in TCGA pan-cancer. Among them, “Amplification” was observed in the majority of cancers, especially in Cholangiocarcinoma (> 8%), hepatobiliary cancer (> 8%) and breast cancer (> 6%). “Mutation” was mainly observed in endometrial cancer. “Deep Depletion” was observed in a few cancer types with a lower mutation frequency. The types, sites and case number of CACYBP genetic alterations are presented in the CACYBP protein structure landscape, and the highest mutation site M127I/V was enriched in LSCC, CRC and HNSC (Fig. 2 B). Furthermore, the CACYBP 3D structure with the M127I/V site was shown (Fig. 2 C). 3.3 Survival and prognosis analysis of CACYBP in pan-cancer To explore the diagnostic and prognostic value of CACYBP in human cancers, Kaplan-Meier survival analyses and ROC analyses were performed based on OS and DSS. We found that high CACYBP expression was associated with poor OS in several tumors, including ACC, BLCA, BRCA, CESC, KIRP and LUAD (Fig. 3 A). And high CACYBP expression was correlated with short DSS in several tumors, including ACC, BLCA, KIRP, LUAD and SARC (Fig. 3 B). Subsequently, diagnostic ROC curves indicated that CACYBP exerted a good diagnostic predictive power in BLCA, BRCA, CESC and LUAD (Figure S1 ). In addition, time-dependent ROC curves showed that CACYBP had good predictive accuracy for 1-, 3- and 5-year OS of ACC, BLCA, BRCA, CESC, KIRP and LUAD (Figure S2). CACYBP also had a good predictive accuracy for 1-, 3- and 5-year DSS of ACC, BLCA, KIRP, LUAD and SARC (Figure S3). 3.4 Methylation levels of CACYBP in pan-cancer To explore the epigenetic characteristics of CACYBP in cancers, we analyzed the methylation level, MMRs and methyltransferases. The results indicated that promoter methylation level of CACYBP was significantly increased in BRCA, CHOL, KIRC, SARC, THCA and UCES, while it was significantly decreased in BLCA, COAD, KIRP, LIHC, LUAD, LUSC, PCPG, READ and TGCT ( all P < 0.05) (Fig. 4 ). In addition, CACYBP expression was strongly positively associated with MMR genes (MLH1, MSH2, MSH6, PMS2 and EPCAM) in 32 tumors, except for UCS (Fig. 5 A). Furthermore, CACYBP was significantly positively associated with four methyltransferases (DNMT1, DNMT2, DNMT3A and DNMT3B) in 32 tumors, except for PAAD (Fig. 5 B). Above results indicated that CACYBP may be involved in tumorigenesis via regulating DNA methylation and repairment. 3.5 Relationship between CACYBP expression and RNA methylation regulatory genes in pan-cancer RNA methylation regulatory genes, including m1A , m5C and m6A , were confirmed to play critical roles in tumors and various diseases. Thus, we explore the correlations between CACYBP and the gene markers of three classes RNA regulatory genes in pan-cancer. As shown in the heat map, CACYBP was positively associated with gene markers of m1A , m5C and m6A in multiple cancers (Fig. 6 ). Particularly, a strong positive correlation was observed in m1A proteins TRMT10C, TRMT6 and YTHDF2, and m5C proteins NSUN2, NSUN4 and TRDMT1, and m6A proteins CBLL1, HNRNPC and ELAVL1, respectively. These findings indicated that CACYBP may exerted an oncogenic effect by regulating RNA methylation regulatory genes. 3.5 Relationship between CACYBP expression and immune reactivity in pan-cancer To explore the potential immune regulatory mechanisms of CACYBP in cancers, we first evaluate three immune scores: the ESTIMATEScore, the ImmuneScore and the StromalScore. As shown in Fig. 7 A, CACYBP expression was negatively correlated with immune scores in multiple tumors, except for KIPAN (KICH + KIRC + KIRP) (r > 0, p < 0.05). Especially, CACYBP expression was significantly negatively correlated with the ESTIMATEScore in LGG, TGCT and UCEC, the ImmuneScore in ESCA, GBM, LGG, UCSC, SKCM and UCEC, and the StromalScore in LGG, GBM, LUSC, SKCM, UCEC, TGCT and ESCA (all r < − 0.3, p < 0.05). Representative scatter plots also indicated a negative relationship between immune scores and CACYBP in common malignant tumors, such as BRCA, LUAD and LIHC (Fig. 7 B). The results indicated that high CACYBP expression may lead to a lower infiltration level of immune cells and stromal cells in tumor core and margin in pan-cancer. In addition, we focus on the effects of CACYBP on infiltration levels of fibroblasts, CD4 + T cells and CD8 + T cells based on the TCGA database. Seven immune algorithms were used to evaluate the correlation coefficients, and a statistically negative correlation between CACYBP expression and cancer-associated fibroblasts infiltration was observed in BRCA, LUSC and TGCT based on xCELL and MCPCOUNTER algorithms (Fig. 8 A). According to most algorithms, a statistically negative correlation of CACYBP expression and CD4 + T cells infiltration was observed in BLCA, COAD, LUSC and SKCM-M, while noted a positive correlation in ACC, KIPAN, LIHC, OV, PCPG and PRAD (Fig. 8 B). A statistically negative correlation of CACYBP expression and CD8 + T cells infiltration was observed in BRCA, CESC, ESCA, HNSC, SKCM and STES, while noted a positive correlation in COAD, GBM, KICH, OV and UVM (Fig. 8 C). Furthermore, we evaluate the correlations between CACBP and immune checkpoint genes (ICGs), TMB and MSI, which play critical roles in cancer immunotherapy. A positive correlation was observed between CACYBP expression and inhibitory ICGs in multiple tumors, such as CD276, VEGFA, CD274, IL10, HAVCR2 and TGFB (Fig. 9 A). And a positive correlation was also observed between CACYBP expression and stimulatory ICGs in multiple tumors, such as HMGB1, IL1B, IL1A, CD80, ICAM1, BTN3A2, ENTPD1, TNFSF4, BTN3A1 and TLR4. Especially, HMGB1 was positively related to CACYBP in all kinds of tumors (Fig. 9 A). Correlation analyses between CACYBP and TMB showed a positive correlation in BLCA, BRCA, COAD, HNSC, LUAD, LUSC, PAAD, SARC, SKCM, STAD and UCEC, but a negative correlation in LGG and THCA (all P < 0.05)(Fig. 9 B). In addition, correlation analyses between CACYBP and MSI showed a positive correlation in COAD, HNSC, KIRC, READ, SARC, STAD, THYM and UCEC, but a negative correlation in LUAD and OV (all P < 0.05)(Fig. 9 C). All the above results indicated that CACYBP is widely related to cancer immunity. 3.6 Single-cell expression and function of CACYBP in pan-cancer Single-cell analysis indicated that CACYBP was highly expressed in 70 single-cell samples within 20 tumors from the CancerSCEM database (Fig. 10 A). UAMP_1 and TSNE_1 landscapes decoded the cellular heterogeneity of single-cell in GBM, LUAD and HCC (Fig. 10 B-D). In addition, abundant expression levels of CACYBP were also observed in some immune cells in these tumors (Fig. 10 B-D). Furthermore, the functions of CACYBP were analyzed in single cancer cells via the CancerSEA database (Figure S4). CACYBP expression was positively linked with the cell cycle in BRCA, HNSCC and LUAD, and DNA repair in BRCA, LUAD and RCC. In addition, CACYBP was positively related to DNA damage in BRCA and LUAD, proliferation in LUAD and inflammation in RCC. Conversely, a negative correlation of inflammation, metastasis, EMT and angiogenesis with CACYBP expression was observed in GBM. In UVM, CACYBP expression was negatively related to DNA damage, apoptosis, quiescence, DNA repair, metastasis, invasion and inflammation. In addition, angiogenesis, differentiation and hypoxia were negatively related to CACYBP expression in LUAD and RCC. 3.7 Relationship between CACYBP expression and tumor stemness in pan-cancer To further investigate the function of CACYBP in cancers, correlation analyses between CACYBP and cancer stemness indices were conducted. The results indicated that high CACYBP were positively associated with six cancer stemness scores, including DNAss, RNAss, DMPss, ENHss, EREG-EXPss and EREG-METHss in the majority of cancers (Fig. 11 ). 3.8 Enrichment analysis of CACYBP-related genes We obtained the top 50 experimental determined CACYBP-interacted genes and the top 100 CACYBP-correlated genes from the STRING and GEPIA2 database (Table S1 ). A PPI network was developed to visualize the interactions among CACYBP and its interacted genes (Fig. 12 A). The top 5 CACYBP-correlated genes, including CCT3, UCHL5, CENPL, PTGES3 and UBE2T showed a strong association with CACYBP expression ( R > 0.6, p < 0.001) (Fig. 12 B). In addition, the heat map showed a significant and positive correlation between CACYBP and these 5 genes in 33 TCGA tumors (Fig. 12 C). The common gene CKS1B was identified by a Venn plot analysis (Fig. 12 D). Furthermore, KEGG and GO enrichment analyses were performed on the CACYBP-interacted genes. The results showed that CACYBP-interacted genes were mainly enriched in some KEGG pathways, including Wnt signaling, Hippo signaling, Ubiquitin mediated proteolysis, Signaling pathways regulating pluripotency of stem cells, Hedgehog signaling, Neurotrophin signaling, cell cycle, mTOR signaling, TGF-β signaling and T cell receptor signaling (Fig. 12 E). In addition, molecular function GO analysis suggested that CACYBP-interacted genes might be involved in regulating some functional pathways, including β-catenin binding, gamma-catenin binding, armadillo repeat domain binding, ubiquitin protein ligase binding, protein domain specific binding, protein binding, enzyme binding, catalytic activity, protein C-terminus binding, transferase activity, protein kinase binding and transcription factor binding (Fig. 12 F). These data indicated that the interactions among CACYBP and its related genes might potentially contribute to tumorigenesis and cancer progression. 4 Discussion With the rapid development of bioinformatics technology, researchers can conduct pan-cancer analysis to fully explore the potential mechanism of single genes in specific cancer types and compare the commonality and differences of specific genes in various tumors, contributing to develop effective specific genes for cancer prevention and treatment. CACYBP consists of three domains: the N-terminal helical hairpin domain, the CS domain, and the C-terminal SGS domain, which is a multifunctional protein present in various cells, especially in cancer cells [ 11 ] . Although some studies have attempted to reveal the mechanism of CACYBP in tumors, it is only limited to a few tumors and existed some controversies. Herein, a series of large public databases were used to conduct a systematic and comprehensive analysis of CACYBP in pan-cancer. The results showed that CACYBP was highly expressed in multiple tumors and led to a poor prognosis in some tumors. CACYBP expression was closely linked with immune cell infiltration. Furthermore, aberrant CACYBP expression was significantly correlated with DNA methylation, RNA methylation, MMRs, methyltransferases, TMB, MSI and tumor stemness in various cancers. Thus, CACYBP might become a novel prognostic, diagnostic biomarker and therapeutic target for human cancers. In the present study, we illustrated the expression pattern of CACYBP in pan-cancer and found CACYBP was overexpressed in 25 TCGA cancers. This finding was consistent with certain cancers in previous studies, such as gastric cancer, colon cancer, osteosarcoma, etc [ 28 – 30 ] . Notably, CACYBP was not only overexpressed in COAD, PAAD and BLCA but also promoted tumor metastasis and recurrence, contributing to a poor prognosis [ 13 , 31 , 32 ] . Our prognostic analysis also revealed that CACYBP performed well in predicting the survival of ACC, BLCA, LUAD and KIRP, constituting a prognostic biomarker. On the other hand, a low CACYBP expression was observed in KICH and LAML. KICH is a sort of renal cell carcinoma (RCC), and CACYBP was reported as a down-regulated and tumor suppressor gene in RCC, it can suppress RCC proliferation via degrading β-catenin and affecting the cell cycle [ 33 ] . A similar suppression mechanism of CACYBP was also found in gastric cancer [ 34 ] . CACYBP was found down-regulated in Chronic lymphocytic leukemia (CLL) and exerted a tumor suppressor role [ 35 ] . Nevertheless, the expression and the role of CACYBP in LAML have not been reported. The reason why the role of CACYBP differ in cancers may include: (i) The E3 enzyme formed by CACYBP varies in different cells; (ii) CACYBP acts on intracellular proteins with different functions;(iii) CACYBP, as a phosphatase, can regulate the expression of intracellular proteins [ 36 ] . Our study suggested that CACYBP may become a novel prognostic biomarker for human cancer. More studies are welcomed to illustrate the role of CACYBP in single tumors. In cancers, genetic alternations commonly occur, allowing stochastic gene activation and silencing and conferring a proliferative advantage to cells [ 37 – 39 ] . Indeed, we found that CACYBP genetic alternations frequently occurred in multiple TCGA cancers, predominantly amplification and followed by mutation. Among them, CACYBP amplification may be driven by chromothripsis in cancers [ 40 ] . We also found that M127I/V is the highest frequency mutation site located at the CS domain of the CACYBP gene, and mainly mutated in LSCC, CRC and HNSC. Epigenetics modifications, critical mediators of plasticity in cancer, participate in tumorigenesis and progression [ 41 ] . Among them, aberrant DNA methylation is a common epigenetic feature of cancer and might be detected in circulating cell-free DNA at the early stage of cancer development, constituting a valuable cancer biomarker [ 42 ] . Meanwhile, MMR genes and methyltransferases are also essential for the DNA repairment pathway, maintaining the accurate transmission of genetic materials [ 43 ] . Dysfunctions of MMR genes may increase the accumulation of mutation, resulting in a high MSI burden in cancers [ 44 ] . Indeed, the present study showed that the CACYBP promoter methylation level was obviously increased in six TCGA tumors, and was obviously decreased in nine TCGA tumors. And we also observed a positive correlation of CACYBP methylation with MMR genes and methyltransferases in multiple cancers, indicating that MMR genes and methyltransferases are involved in CACYBP DNA repairment. RNA methylation, as a critical regulator of transcript expression, is closely linked with cancer cell proliferation, cellular stress, metastasis and immune response, and is becoming a promising target of cancer therapy [ 45 ] . In this study, we also found a close relationship between CACYBP and RNA regulatory genes. Taken together, the common strong correlations of CACYBP with DNA methylation, MMR genes, methyltransferases and RNA methylation regulatory genes suggested that CACYBP might be an ideal epigenetic therapeutic target. The tumor microenvironment (TME) is essential for regulating tumor growth, progression and metastasis [ 46 ] . Immune cells represent a large fraction of TME, thus they may exert a critical role in regulating cancer immune responses, like immune escape [ 47 ] . The role of CACYBP in the immune microenvironment has gradually been revealed, recent study reported that CACYBP may enhance the host immunity to protect against malaria infection [ 48 ] . And some evidence indicated that CACYBP was involved in regulating TME in tumors, such as hepatocellular carcinoma, esophageal cancer and colorectal cancer [ 49 – 51 ] . CACYBP may be a promising target for cancer immunotherapy, especially for hepatocellular carcinoma [ 52 – 54 ] . In the present study, we found that CACYBP expression was negatively linked with the ESTIMATEScore, the ImmuneScore, and the StromalScore in multiple cancers. And the results indicated a lower fraction of immune cells and stromal cells present in the core and the invasive margin of tumors [ 55 ] . To identify the role of CACYBP on immune infiltration, our analysis focused on three critical immune cells in TME, including cancer-associated fibroblasts (CAFs), CD4 + T cells and CD8 + T cells [ 56 , 57 ] . A significant correlation was observed between CACYBP expression and infiltration of these immune cells in various cancers. Furthermore, we found that CACYBP expression was significantly correlated with immune checkpoints, TMB and MSI in various cancers. TMB and MSI can effectively predict the treatment efficacy of immune checkpoint inhibitors and stimulators [ 58 ] . Therefore, CACYBP may play a critical role in cancer immunity and might become an ideal therapeutic target for tumor immunotherapy. To further investigate to the latent role of CACYBP in cancers, we used single-cell data to analyze the expression and function of CACYBP in various tumors. Similarly, CACYBP was observed abundantly expressed in most single malignant samples. Single-cell function analysis indicated that CACYBP was significantly positively correlated with the cell cycle in BRCA, HNSC and LUAD. Previous studies had demonstrated that CACYBP may regulate the cell cycle in some cancers via degrading β-catenin [ 59 , 60 ] . In addition, we found that CACYBP was positively related to DNA repair and DNA damage in BRCA and LUAD. Conversely, a negative correlation between CACYBP and functional status was observed in GBM and UVM. However, further studies are welcomed to verify these results in the future. We also paid attention to the cancer stem cell, a kind of self-renewing cell, which can facilitate tumor initiation, promote metastasis, and enhance cancer therapy resistance [ 61 ] . We analyzed the correlation of CACYBP with several tumor stemness scores, including DNAss, RNAss, DMPss, ENHss, EREG-EXPss and EREG-METHss. We found that CACYBP was positively related to tumor stemness scores in multiple cancers, suggesting a stronger biological activity and weaker differentiation ability in tumor stem cells [ 62 ] . These findings may help the clinician to select suitable drugs and predict prognosis. Interactions among various genes and proteins are commonly existed and potentially contributed to cancer development. Thus, CACYBP may be involved in tumorigenesis by regulating its related genes and proteins. Indeed, the top five CACYBP correlated genes, including CCT3, UCHL5, CENPL, PTGES3 and UBE2T were identified in this study. And a strong positive correlation of CACYBP with these genes was observed in all TCGA tumors. Among the related genes, CCT3, as an important member of the chaperone protein family, was up-regulated in some cancers and involved in cell division, proliferation and apoptosis pathways [ 63 ] . UCHL5, a member of the ubiquitin-proteasome system, participates in protein ubiquitination and deubiquitination processes in cancer progression [ 64 ] . CENPL is one critical member of the centromere protein family, and may function as a potential biomarker and serve as an oncogene in pan-cancer, especially LUAD [ 65 ] . PTGES3, a molecule chaperone of Hsp90, participates in pathogenesis including DNA replication and spliceosome [ 66 ] . A Recent study reported that PTGES3 interacted with CACYBP, and they were highly co-expressed in lung adenocarcinoma, resulting in cancer progression [ 67 ] . UBE2T, a ubiquitin-coupled enzyme, had been confirmed as a critical oncogene and therapeutic target in the majority of cancers [ 68 – 70 ] . Furthermore, KEGG analysis showed that CACYBP-interacted genes participated in some signaling pathways, such as Wnt signaling, ubiquitin mediated proteolysis and cell cycle. MF-GO analysis also revealed that CACYBP-interacted genes involved in the regulation of some cellular processes, such as protein binding, transferase activity and transcription factor binding. These data suggested that the function of CACYBP-related genes and their interactions contributed significantly to tumorigenesis. And more studies are needed to further explore the interactions and functions of CACYBP-related genes, which may provide new insight into cancer therapeutic strategy. In the present study, we performed a systemic and integrative pan-cancer analysis, which contributed to a deep understanding of the potential roles of CACYBP in human cancers from multi-genomics and multi-dimension perspectives. Some limitations should be considered when interpreting the results of this study. First, this study was based on a series of public databases, thus lacking clinical sample data for validation. In addition, molecular experiments are lacking for verifying the tumorigenesis and progression mechanisms of CACYBP in various cancers. Therefore, sufficient actual clinical data and laboratory experiments are needed in future studies to determine the functional mechanism of CACYBP in human tumors. 5 Conclusion Taken together, this pan-cancer study indicated significant correlation of CACYBP with genetic alternation, prognosis, DNA methylation, RNA methylation, immune reactivity and tumor stemness, indicating CACYBP may become a promising prognostic biomarker and therapeutic target for human cancers. Abbreviations CACYBP Calcyclin-binding protein SIP Siah-1-interacting protein MMRs Mismatch modified genes ICGs Immune checkpoint genes TMB Tumor mutational burden MSI Microsatellite instability TME Tumor microenvironment OS Overall survival DSS Disease-specific survival ACC Adrenocortical carcinoma BLCA Bladder urothelial carcinoma BRCA Breast invasive carcinoma CESC Cervical squamous cell carcinoma CHOL Cholangiocarcinoma COAD Colon adenocarcinoma DLBC Lymphoid neoplasm diffuse large B-cell lymphoma ESCA Esophageal carcinoma GBM Glioblastoma multiforme HNSC Head and neck squamous cell carcinoma KICH Kidney chromophobe cell carcinoma KIRC Kidney renal clear cell carcinoma KIRP Kidney renal papillary cell carcinoma KIPAN Pan-kidney cohort (KICH + KIRC + KIRP) LAML Acute myeloid leukemia LGG Brain lower grade glioma LIHC Liver hepatocellular carcinoma LUAD Lung adenocarcinoma LUSC Lung squamous cell carcinoma MESO Mesothelioma OV Ovarian serous cystadenocarcinoma PAAD Pancreatic adenocarcinoma PCPG Pheochromocytoma and Paraganglioma PRAD Prostate adenocarcinoma READ Rectum adenocarcinoma SARC Sarcoma STAD Stomach adenocarcinoma SKCM Skin cutaneous melanoma TGCT Testicular germ cell tumors THCA Thyroid carcinoma THYM Thymoma UCEC Uterine corpus endometrial carcinoma UCS Uterine carcinosarcoma UVM Uveal melanoma Declarations Author Contributions: H.L. wrote the article and performed data analyses; L.L., Z.L. and J.Z. performed data analyses; F.G., L.M. and B.H. revised the final manuscript. 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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-3247132","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":225415179,"identity":"079a53cf-8671-47e6-a949-7d3f025336f3","order_by":0,"name":"He-jun Liang","email":"","orcid":"","institution":"Department of Gastroenterology, Xuanwu Hospital Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"He-jun","middleName":"","lastName":"Liang","suffix":""},{"id":225415180,"identity":"ebe0b2de-e68d-40a9-91b5-c8f2e9422983","order_by":1,"name":"Lan-hui 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02:29:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3247132/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3247132/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":41641174,"identity":"9fd03ca2-139b-4218-a780-76836108116d","added_by":"auto","created_at":"2023-08-16 14:22:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3890016,"visible":true,"origin":"","legend":"\u003cp\u003eExpression pattern of CACYBP in pan-cancer. (\u003cstrong\u003eA\u003c/strong\u003e) CACYBP mRNA\u003cem\u003e \u003c/em\u003eexpression levels in pan-cancer and corresponding normal tissues from the TCGA and GTEx database. (\u003cstrong\u003eB\u003c/strong\u003e) CACYBP protein expression levels in pan-cancer and corresponding normal tissues from the UALCAN database. (C) Representative IHC images of the CACYBP gene in human breast, colon, lung and liver tissues from the HPA database. *\u003cem\u003ep \u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep \u003c/em\u003e\u0026lt;0.01,***\u003cem\u003ep \u003c/em\u003e\u0026lt;0.001,****\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.0001; ns, no significance.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3247132/v1/cf9064b738cfda2eafa0c996.png"},{"id":41642597,"identity":"3ba92793-571d-4feb-a3b7-1cf54de0efe4","added_by":"auto","created_at":"2023-08-16 14:38:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":856928,"visible":true,"origin":"","legend":"\u003cp\u003eCACYBP genetic alterations in pan-cancer. (A) The alteration frequencies and types in pan-cancer from the TCGA database. (B) CACYBP mutation landscape, including mutation types, sites, and case numbers. (C) Three-dimensional structure landscape of the CACYBP protein.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3247132/v1/4f9d5313dcb90cc3a53967d5.png"},{"id":41642093,"identity":"09bacf24-1c18-47fe-94af-4ee9b769b870","added_by":"auto","created_at":"2023-08-16 14:30:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1024685,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic analyses comparing the survival time between high and low CACYBP subgroups in TCGA pan-cancer. (A) Overall survival analyses were performed using the GEPIA2 tool and the Kaplan-Meier curves with significance were shown. (B) Disease-free survival analyses were performed using the GEPIA2 tool and the Kaplan-Meier curves with significance were shown.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3247132/v1/8f884133e9cc663fac2ebc08.png"},{"id":41640090,"identity":"47f866e4-2647-4a82-8d0f-87c353a04d65","added_by":"auto","created_at":"2023-08-16 14:14:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":321060,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential promoter methylation levels of CACYBP in pan-cancer. Box plots of promoter methylation levels in tumors with significance, including BLCA, BRCA, CHOL, COAD, KIRC, KIRP, LIHC, LUAD, LUSC, PCPG, READ, SARC, TGCT, THCA and UCEC.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3247132/v1/a2086651c46733c1551c55d1.png"},{"id":41640087,"identity":"8e54f32c-7b05-4cbd-8d59-dde0b2e5b783","added_by":"auto","created_at":"2023-08-16 14:14:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1849096,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between CACYBP and MMR genes and methyltransferases in pan-cancer. (A) Correlation heat map of CACYBP and five MMR genes. (B) Correlation heat map of CACYBP and four DNA methyltransferases. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3247132/v1/ec6cbdb00c5595b9175a91bf.png"},{"id":41640088,"identity":"5562cd96-d92f-4e57-b690-9ac69d6ccedf","added_by":"auto","created_at":"2023-08-16 14:14:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1573598,"visible":true,"origin":"","legend":"\u003cp\u003ePan-cancer correlation between CACYBP and 44 RNA methylation regulatory proteins. RNA methylation modifications: \u003cem\u003em1A\u003c/em\u003e, \u003cem\u003em5A\u003c/em\u003e, and \u003cem\u003em6C\u003c/em\u003e; modified protein types: writer, reader and eraser.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3247132/v1/540faa26062bcceebbe68854.png"},{"id":41640096,"identity":"1921c7a9-07e4-497f-9ab7-3a0874a387c3","added_by":"auto","created_at":"2023-08-16 14:14:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1172871,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of CACYBP expression with the ESTIMATE score, the Immune score the and Stromal score in pan-cancer. (A) Circular heat map of the correlations between CACYBP expression and three immune scores in tumors. (B) Representative scatter plots of BRCA, LUAD, and LIHC were shown.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3247132/v1/41774719726cc83b15286445.png"},{"id":41642945,"identity":"6b45b6d0-d60e-44cb-ad79-1549b2a28a31","added_by":"auto","created_at":"2023-08-16 14:46:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":483829,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations of CACYBP expression with immune cell infiltration based on different algorithms. (A) Heat map of the correlation between CACYBP expression and cancer-associated fibroblast infiltration. (B) Heat map of the correlations between CACYBP expression and CD4\u003csup\u003e+ \u003c/sup\u003eT cells infiltration. (C) Heat map of the correlations between CACYBP expression and CD8\u003csup\u003e+ \u003c/sup\u003eT cells infiltration. *\u003cem\u003ep \u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep \u003c/em\u003e\u0026lt;0.01,***\u003cem\u003ep \u003c/em\u003e\u0026lt;0.001, spearman’s correlation coefficients.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3247132/v1/103bc4b2399748ac1fe1330d.png"},{"id":41640092,"identity":"f3f86614-2feb-44fa-9c19-d7b094daaff2","added_by":"auto","created_at":"2023-08-16 14:14:06","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1117080,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations of immune checkpoints, MSI, and TMB with CACYBP\u003cem\u003e \u003c/em\u003eexpression in pan-cancer. (A) Heat map of the correlations between CACYBP expression and immune checkpoint genes. (B) Radar chart of the correlations between CACYBP expression and TMB. (C) Radar chart of the correlations between CACYBP expression and MSI. * \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, spearman’s correlation coefficients.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-3247132/v1/2c450aaa3038720bcfdb76a4.png"},{"id":41641169,"identity":"ceca35ea-7776-447a-8ad5-918c9a10899f","added_by":"auto","created_at":"2023-08-16 14:22:06","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1187492,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell level analysis of CACYBP in pan-cancer. (A) CACYBP expression levels in all cancer single-cell samples. (B-D) Spatial transcription landscapes and single immune cells expression levels of CACYBP in GBM, LUAD and HCC.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-3247132/v1/c7edaa1ed54ae90e0eeb3142.png"},{"id":41642096,"identity":"e387cdd9-be2e-4341-868e-847f2cbf07e7","added_by":"auto","created_at":"2023-08-16 14:30:06","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":976118,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations of CACYBP expression with six cancer stemness scores in pan-cancer. (A) DNAss, (B) RNAss, (C) DMPss, (D) ENHss, (E) EREG-EXPss and (F) EREG-METHss.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-3247132/v1/2f8b73921acba98c23aecb75.png"},{"id":41640098,"identity":"71c51dd4-4ad1-4d83-afa7-6d3c0f4c4226","added_by":"auto","created_at":"2023-08-16 14:14:06","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":1351135,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of CACYBP-related genes. (A) PPI network of 50 experimental confirmed CACYBP-interacted proteins from STRING. (B) The top 100 CACYBP-correlated genes from GEPIA2. (C) The heat map data of the top 5 CACYBP-correlated genes in pan-cancer. (D) An intersection analysis of the 50 CACYBP-interacted genes and 100 CACYBP-correlated genes. (E) KEGG pathway analysis of the CACYBP-interacted genes. (F) GO-MF analysis of CACYBP-interacted genes.\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-3247132/v1/4a9eae24fcc9990fb63d8718.png"},{"id":53927834,"identity":"3c5003a8-9d8a-4356-b652-d5bb2bba2d3e","added_by":"auto","created_at":"2024-04-02 10:15:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7705240,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3247132/v1/603e54b5-cf7f-4f0a-a858-6de226368998.pdf"},{"id":41640101,"identity":"67293dc8-b3f0-446d-b24f-d9e692834ea2","added_by":"auto","created_at":"2023-08-16 14:14:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4159005,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-3247132/v1/def3f3e3fecfb3b33ef70487.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Systemic Pan-Caner Analysis Identifies CACYBP as a Novel Biomarker for Cancer Prognosis and Immunity","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eCancer is a leading cause of death globally with increasing incidence and mortality rates annually and is second only to cardiovascular disease for the number of deaths, years of life lost and disability-adjusted life years, causing a huge burden on the global economy and health\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Despite advances in treatment technology, cancer remains one of the most dreaded diseases with a poor prognosis. More specific cancer-related therapeutic targets are necessary to effectively prevent and control cancer, and their molecular mechanisms need to be determined. Fortunately, with the development of bioinformatics technology and the appearance of numerous public large-scale databases, enabling researchers to conduct comprehensive analyses of cancer-related genes in multiple omics and dimensions way\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCalcyclin-binding protein (CACYBP), as a binding partner of S100A6, was initially found in the cytoplasm of Ehrlich ascites tumor cells and was subsequently identified as a siah-1 interaction protein (SIP)\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. CACYBP expression levels differ in normal body tissues, with the highest in brain and spleen, the middle in stomach, liver and heart, and the lowest in kidney, lymph node and rectum\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Since CACYBP has been found, increasing evidence confirms that CACYBP involving in various cellular processes, including ubiquitination degradation, cell proliferation, cell differentiation, cytoskeletal rearrangement, transcriptional regulation and tumorigenesis through binding to various ligands, playing a critical role in human diseases. For example, CACYBP was found highly expressed in myocardial infarction and exerted a heart-protected effect by promoting DNA synthesis and differentiation of cardiomyocytes\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. CACYBP was also found highly expressed in multiple brain structures and bound with tubulin, actin, tropomyosin, tau and heat shock protein 90 (Hsp90), played a critical role in neurodegenerative disorders, such as Alzheimer's disease, Parkinson\u0026rsquo;s disease and Huntington\u0026rsquo;s disease\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn human cancers, the role of CAYBP is still controversy and may depend on cell types. CACYBP was highly expressed in several cancers, such as pancreatic cancer, colon cancer and nasopharyngeal cancer, contributing to poor prognosis, recurrence and metastasis\u003csup\u003e[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Our team revealed that CACYBP nuclear translocation may contribute to the progression of gastric cancer and colon cancer\u003csup\u003e[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Conversely, the expression of CACYBP was markedly down-regulated in renal cancer and glioblastoma, while up-regulated CACYBP expression can suppress its migration and invasion\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In addition to some cancer types, the role of CACYBP in multiple cancers remains unclear. Considering the complexity of tumorigenesis, it is imperative to evaluate CACYBP expression in pan-cancer and to analyze its relevance to prognosis and potential molecular mechanisms.\u003c/p\u003e \u003cp\u003eIn the present study, we conducted a systemic and integrative pan-cancer analysis for the CACYBP gene via the bioinformatics method. We aim to illustrate the expression pattern, genetic alternation, prognosis, DNA methylation, RNA methylation, immune reactivity, cancer stemness, single-cell function, gene interaction and enrichment pathway of CACYBP in human cancers.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Analysis of mRNA and protein expression\u003c/h2\u003e \u003cp\u003eThe mRNA expression profiles of CACYBP in pan-cancer and corresponding normal tissues with a \u0026ldquo;TMP\u0026rdquo; format were obtained from the TCGA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. gov, accessed on 15 June 2023) and GTEx database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://commonfund.nih.Gov/\u003c/span\u003e\u003cspan address=\"https://commonfund.nih.Gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e GTEx, accessed on 15 June 2023). Each expression value was transformed using the log2 method. Then the differential mRNA expression analyses were performed with R software (Version 4.1.2) (\u0026ldquo;ggplot2 package\u0026rdquo;).\u003c/p\u003e \u003cp\u003eThe protein expression profiles of CACYBP in pan-cancer and corresponding normal tissues with a \u0026ldquo;TMP\u0026rdquo; format were obtained from the CPTAC module of the UALCAN database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ualcan.path.uab.edu/analysis-prot.html\u003c/span\u003e\u003cspan address=\"https://ualcan.path.uab.edu/analysis-prot.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 15 June 2023)\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. And the differential protein expression analyses were conducted using the GEPIA2 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gepia2.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"https://gepia2.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 15 June 2023) with the default parameters (\u0026ldquo;log2FC cutoff\u0026thinsp;=\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e-value cutoff\u0026thinsp;=\u0026thinsp;0.01\u0026rdquo;) and matched the normal data from the TCGA and GTEx database\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. The immunohistochemistry (IHC) data of CACYBP was obtained from the HPA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 15 June 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Analysis of genetic alternation\u003c/h2\u003e \u003cp\u003eThe cBioPortal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 15 June 2023) is a visual and multidimensional genomic public database, containing genomic characteristics of cancers at the DNA level\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. In the present study, cBioPortal was used to explore the genetic alternations of CACYBP in pan-cancer based on \u0026ldquo;TCGA Pan Cancer Atlas Studies\u0026rdquo;, including mutation frequency and mutation type. In addition, the schematic diagram of the protein structure with mutation frequency, mutation type and enrich tumor type was performed based on the \u0026ldquo;mutation\u0026rdquo; module of this database. The highest mutation site was highlighted in the three-dimensional (3D) structure of CACYBP protein.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Analysis of survival and prognosis\u003c/h2\u003e \u003cp\u003eWe used the GEPIA2 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gepia2.cancer-pku\u003c/span\u003e\u003cspan address=\"https://gepia2.cancer-pku\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. cn/, accessed on 15 June 2023) to analyze survival points, including overall survival (OS) and disease-specific survival (DSS) from the TCGA database\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. According to the median expression levels of CACYBP in pan-cancer, patients were divided into high- and low- expression subgroups and Kaplan-Meier analyses were performed to compare the survival time. Then diagnostic ROC analyses were conducted to identify the diagnostic value of CACYBP in certain cancers. In addition, time-dependent ROC analyses were conducted with R-software (\u0026ldquo;timeROC\u0026rdquo; package) to evaluate the predictive power of CACYBP for 1-, 3- and 5- year survival in several cancers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analysis of DNA methylation and MMR genes\u003c/h2\u003e \u003cp\u003eUCLAN (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ualcan.path.uab.edu/analysis.html\u003c/span\u003e\u003cspan address=\"https://ualcan.path.uab.edu/analysis.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 20 June 2023) is a comprehensive omics database that allows researchers to perform gene expression and gene promoter methylation analyses\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. \u0026ldquo;TCGA\u0026rdquo; and \u0026ldquo;methylation\u0026rdquo; modules of the UCLAN database were used to compare promoter DNA methylation levels between tumors and corresponding normal tissues. In addition, five critical mismatch modified genes (MMRs), including MLH1, MSH2, MSH6, PMS2, and EPCAM were selected to evaluate correlations with CACYB expression in TCGA pan-cancer. We further evaluated the correlations between four methyltransferases (DNMT1, DNMT2, DNMT3A, and DNMT3B) and CACYBP expression in TCGA pan-cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Analysis of RNA methylation regulatory genes\u003c/h2\u003e \u003cp\u003eWe downloaded the unified and standardized TCGA Pan-Cancer (PANCAN, N\u0026thinsp;=\u0026thinsp;10535, G\u0026thinsp;=\u0026thinsp;60499) dataset from the UCSC database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 20 June 2023). And the expression data of the ENSG00000116161 (CACYBP) gene and 44 marker genes of RNA modification genes (m1A, m5C and m6A) in each sample were extracted. The data was then log2(x\u0026thinsp;+\u0026thinsp;0.001) transformed and the correlations between CACYBP and 44 marker genes were calculated with the spearman method. Finally, the correlation heat map is drawn by the SangerBox online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sangerbox.com/\u003c/span\u003e\u003cspan address=\"http://sangerbox.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 20 June 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Analysis of immune reactivity\u003c/h2\u003e \u003cp\u003eThe data of CACYBP expression and corresponding abundances of six types of immune cells, including B cells, neutrophils, macrophages, CD4\u003csup\u003e+\u003c/sup\u003e T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, and dendritic cells was obtained from the TCGA database. Correlation coefficients between CACYBP and these immune cells were calculated and then transformed into three types of immune scores: the ESTIMATE score, the Immune score and the Stromal score via R software (\u0026ldquo;ESTIMATE\u0026rdquo; and \u0026ldquo;limma\u0026rdquo; package). Subsequently, the correlations between CACYBP and the three immune scores were further analyzed.\u003c/p\u003e \u003cp\u003eIn addition, the \u0026ldquo;Immune-Gene\u0026rdquo; module of the TIMER2 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 20 June 2023) was used to evaluate the infiltration levels of 22 immune cell subtypes\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Seven immune-related machine learning algorithms, including Timer, EPIC, IPS, MCPcount, xCELL, CIBERSORT, and QUANTISEQ were used to evaluate the immune infiltration of three critical immune cells infiltration (cancer-associated fibroblasts, CD4\u003csup\u003e+\u003c/sup\u003e T cells and CD8\u003csup\u003e+\u003c/sup\u003e T cells ). Furthermore, expression profiles of immune checkpoints, MSI and TMB were obtained from the TCGA database. Then the spearman correlation analyses of CACYBP expression with immune checkpoints, MSI and TMB in TCGA pan-caner were performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Analysis of single-cell expression and function\u003c/h2\u003e \u003cp\u003eCancerSCEM (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/cancerscem/index\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/cancerscem/index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 20 June 2023) is a public tool for studying tumor microenvironment, immune landscape and cellular heterogeneity within various cancers, integrating seven analysis functions, where researchers can perform scRNA-seq analyses\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. In the present study, CancerSCEM was used to quantify tumor microenvironment. Cellular heterogeneity of CACYBP expression in all single cancer samples was quantified based on the database. And scRNA-seq analyses were performed to visualize the \u0026ldquo;UMPA_1\u0026rdquo; and \u0026ldquo;TSNE_1\u0026rdquo; landscapes and to quantify immune cellular heterogeneity of CACYBP in several cancers.\u003c/p\u003e \u003cp\u003eCancerSEA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biocc.hrbmu.edu.cn/CancerSEA/\u003c/span\u003e\u003cspan address=\"http://biocc.hrbmu.edu.cn/CancerSEA/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 20 June 2023) is a dedicated database to decode functional states of cancer single cells, containing 14 functional states of 41 900 cancer single cells from 25 cancer types\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. We used the CancerSEA database to determine the correlation of CACYBP with 14 function states, including angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, EMT, hypoxia, inflammation, invasion, metastasis, proliferation, quiescence, and stemness in several tumors at the single-cell level. And the functional states that meet the standard of \u0026ldquo;|cor|\u0026gt;0.3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026rdquo; were shown.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Analysis of tumor stemness\u003c/h2\u003e \u003cp\u003eTumor stemness data, including DNAss, RNAss, DMPss, ENHss, EREG-EXPss and EREG-METHss was obtained from prior studies, and was used to evaluate the oncogenic dedifferentiation ability\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. In the present study, we intersected CACYBP expression data with six stemness indices to conduct the Spearman correlation analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Analysis of CACYBP-related genes\u003c/h2\u003e \u003cp\u003eThe top 50 experimentally determined CACYBP-interacted genes were downloaded from the STRING database ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 22 June 2023), and visualized with a protein-protein interaction (PPI) network. The top 100 CACYBP correlated genes were obtained from the \u0026ldquo;Similar Genes Detection\u0026rdquo; module of the GEPIA2 database, and the correlations between the top 5 correlated genes and CACYBP in pan-cancer were analyzed. Venn plot analysis was performed to identify the common genes among the CACYBP interacted and correlated genes via R-software ( \u0026ldquo;ggplot2\u0026rdquo; and \u0026ldquo;VennDiagram\u0026rdquo; packages). KEGG and GO enrichment analyses were performed on the CACYBP-interacted genes from the STRING and visualized via the SangerBox online tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Statistical Analysis\u003c/h2\u003e \u003cp\u003eAn unpaired Wilcoxon crossover test was used to analyze the differential expression of CACYBP in pan-cancer. Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test was used to analyze the differential promoter methylation level. Log-rank test was used to analyze the survival curves. The spearman method was used to calculate the correlation coefficients in the study. *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01,***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and ****\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 were considered statistically significant in this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Expression pattern of CACYBP in pan-cancer\u003c/h2\u003e \u003cp\u003eAmong the 33 TCGA tumors, CACYBP mRNA expression was substantially up-regulated in 25 tumors, including AAC, BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, LGG, LIHC, LUAD, LUSC, OV, PAAD, PRAD, READ, SKCM, STAD, TGCT, THCA, THYM, UCEC and UCS. Conversely, we found substantial down-regulation of CACYBP in KICH and LAML (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In addition, CACYBP protein expression was substantially up-regulated in 8 tumors compared to corresponding normal tissues, including BRCA, COAD, OV, UCEC, LUAD, PAAD, HNNC and LIHC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Furthermore, representative IHC results showed that CACYBP gene expression was dramatically increased in breast cancer, colon cancer, lung cancer and liver cancer tissues compared to corresponding healthy human tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Genetic alternations and features of CACYBP in pan-cancer\u003c/h2\u003e \u003cp\u003eGenetic alternations of genes had been proven to be significantly correlated with tumorigenesis. We evaluated the genetic alternation type and frequency of CACYBP in pan-cancer via the cBioPortal database. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, we found three main genetic change types of CACYBP including amplification, mutation and deep deletion in TCGA pan-cancer. Among them, \u0026ldquo;Amplification\u0026rdquo; was observed in the majority of cancers, especially in Cholangiocarcinoma (\u0026gt;\u0026thinsp;8%), hepatobiliary cancer (\u0026gt;\u0026thinsp;8%) and breast cancer (\u0026gt;\u0026thinsp;6%). \u0026ldquo;Mutation\u0026rdquo; was mainly observed in endometrial cancer. \u0026ldquo;Deep Depletion\u0026rdquo; was observed in a few cancer types with a lower mutation frequency. The types, sites and case number of CACYBP genetic alterations are presented in the CACYBP protein structure landscape, and the highest mutation site M127I/V was enriched in LSCC, CRC and HNSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Furthermore, the CACYBP 3D structure with the M127I/V site was shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Survival and prognosis analysis of CACYBP in pan-cancer\u003c/h2\u003e \u003cp\u003eTo explore the diagnostic and prognostic value of CACYBP in human cancers, Kaplan-Meier survival analyses and ROC analyses were performed based on OS and DSS. We found that high CACYBP expression was associated with poor OS in several tumors, including ACC, BLCA, BRCA, CESC, KIRP and LUAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). And high CACYBP expression was correlated with short DSS in several tumors, including ACC, BLCA, KIRP, LUAD and SARC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Subsequently, diagnostic ROC curves indicated that CACYBP exerted a good diagnostic predictive power in BLCA, BRCA, CESC and LUAD (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In addition, time-dependent ROC curves showed that CACYBP had good predictive accuracy for 1-, 3- and 5-year OS of ACC, BLCA, BRCA, CESC, KIRP and LUAD (Figure S2). CACYBP also had a good predictive accuracy for 1-, 3- and 5-year DSS of ACC, BLCA, KIRP, LUAD and SARC (Figure S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Methylation levels of CACYBP in pan-cancer\u003c/h2\u003e \u003cp\u003eTo explore the epigenetic characteristics of CACYBP in cancers, we analyzed the methylation level, MMRs and methyltransferases. The results indicated that promoter methylation level of CACYBP was significantly increased in BRCA, CHOL, KIRC, SARC, THCA and UCES, while it was significantly decreased in BLCA, COAD, KIRP, LIHC, LUAD, LUSC, PCPG, READ and TGCT ( all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In addition, CACYBP expression was strongly positively associated with MMR genes (MLH1, MSH2, MSH6, PMS2 and EPCAM) in 32 tumors, except for UCS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Furthermore, CACYBP was significantly positively associated with four methyltransferases (DNMT1, DNMT2, DNMT3A and DNMT3B) in 32 tumors, except for PAAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Above results indicated that CACYBP may be involved in tumorigenesis via regulating DNA methylation and repairment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Relationship between CACYBP expression and RNA methylation regulatory genes in pan-cancer\u003c/h2\u003e \u003cp\u003eRNA methylation regulatory genes, including \u003cem\u003em1A\u003c/em\u003e, \u003cem\u003em5C\u003c/em\u003e and \u003cem\u003em6A\u003c/em\u003e, were confirmed to play critical roles in tumors and various diseases. Thus, we explore the correlations between CACYBP and the gene markers of three classes RNA regulatory genes in pan-cancer. As shown in the heat map, CACYBP was positively associated with gene markers of \u003cem\u003em1A\u003c/em\u003e, \u003cem\u003em5C\u003c/em\u003e and \u003cem\u003em6A\u003c/em\u003e in multiple cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Particularly, a strong positive correlation was observed in \u003cem\u003em1A\u003c/em\u003e proteins TRMT10C, TRMT6 and YTHDF2, and \u003cem\u003em5C\u003c/em\u003e proteins NSUN2, NSUN4 and TRDMT1, and \u003cem\u003em6A\u003c/em\u003e proteins CBLL1, HNRNPC and ELAVL1, respectively. These findings indicated that CACYBP may exerted an oncogenic effect by regulating RNA methylation regulatory genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Relationship between CACYBP expression and immune reactivity in pan-cancer\u003c/h2\u003e \u003cp\u003eTo explore the potential immune regulatory mechanisms of CACYBP in cancers, we first evaluate three immune scores: the ESTIMATEScore, the ImmuneScore and the StromalScore. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, CACYBP expression was negatively correlated with immune scores in multiple tumors, except for KIPAN (KICH\u0026thinsp;+\u0026thinsp;KIRC\u0026thinsp;+\u0026thinsp;KIRP) (r\u0026thinsp;\u0026gt;\u0026thinsp;0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Especially, CACYBP expression was significantly negatively correlated with the ESTIMATEScore in LGG, TGCT and UCEC, the ImmuneScore in ESCA, GBM, LGG, UCSC, SKCM and UCEC, and the StromalScore in LGG, GBM, LUSC, SKCM, UCEC, TGCT and ESCA (all r\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Representative scatter plots also indicated a negative relationship between immune scores and CACYBP in common malignant tumors, such as BRCA, LUAD and LIHC (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). The results indicated that high CACYBP expression may lead to a lower infiltration level of immune cells and stromal cells in tumor core and margin in pan-cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, we focus on the effects of CACYBP on infiltration levels of fibroblasts, CD4\u003csup\u003e+\u003c/sup\u003eT cells and CD8\u003csup\u003e+\u003c/sup\u003e T cells based on the TCGA database. Seven immune algorithms were used to evaluate the correlation coefficients, and a statistically negative correlation between CACYBP expression and cancer-associated fibroblasts infiltration was observed in BRCA, LUSC and TGCT based on xCELL and MCPCOUNTER algorithms (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). According to most algorithms, a statistically negative correlation of CACYBP expression and CD4\u003csup\u003e+\u003c/sup\u003eT cells infiltration was observed in BLCA, COAD, LUSC and SKCM-M, while noted a positive correlation in ACC, KIPAN, LIHC, OV, PCPG and PRAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). A statistically negative correlation of CACYBP expression and CD8\u003csup\u003e+\u003c/sup\u003eT cells infiltration was observed in BRCA, CESC, ESCA, HNSC, SKCM and STES, while noted a positive correlation in COAD, GBM, KICH, OV and UVM (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, we evaluate the correlations between CACBP and immune checkpoint genes (ICGs), TMB and MSI, which play critical roles in cancer immunotherapy. A positive correlation was observed between CACYBP expression and inhibitory ICGs in multiple tumors, such as CD276, VEGFA, CD274, IL10, HAVCR2 and TGFB (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). And a positive correlation was also observed between CACYBP expression and stimulatory ICGs in multiple tumors, such as HMGB1, IL1B, IL1A, CD80, ICAM1, BTN3A2, ENTPD1, TNFSF4, BTN3A1 and TLR4. Especially, HMGB1 was positively related to CACYBP in all kinds of tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Correlation analyses between CACYBP and TMB showed a positive correlation in BLCA, BRCA, COAD, HNSC, LUAD, LUSC, PAAD, SARC, SKCM, STAD and UCEC, but a negative correlation in LGG and THCA (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). In addition, correlation analyses between CACYBP and MSI showed a positive correlation in COAD, HNSC, KIRC, READ, SARC, STAD, THYM and UCEC, but a negative correlation in LUAD and OV (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). All the above results indicated that CACYBP is widely related to cancer immunity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Single-cell expression and function of CACYBP in pan-cancer\u003c/h2\u003e \u003cp\u003eSingle-cell analysis indicated that CACYBP was highly expressed in 70 single-cell samples within 20 tumors from the CancerSCEM database (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). UAMP_1 and TSNE_1 landscapes decoded the cellular heterogeneity of single-cell in GBM, LUAD and HCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e10\u003c/span\u003eB-D). In addition, abundant expression levels of CACYBP were also observed in some immune cells in these tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e10\u003c/span\u003eB-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the functions of CACYBP were analyzed in single cancer cells via the CancerSEA database (Figure S4). CACYBP expression was positively linked with the cell cycle in BRCA, HNSCC and LUAD, and DNA repair in BRCA, LUAD and RCC. In addition, CACYBP was positively related to DNA damage in BRCA and LUAD, proliferation in LUAD and inflammation in RCC. Conversely, a negative correlation of inflammation, metastasis, EMT and angiogenesis with CACYBP expression was observed in GBM. In UVM, CACYBP expression was negatively related to DNA damage, apoptosis, quiescence, DNA repair, metastasis, invasion and inflammation. In addition, angiogenesis, differentiation and hypoxia were negatively related to CACYBP expression in LUAD and RCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Relationship between CACYBP expression and tumor stemness in pan-cancer\u003c/h2\u003e \u003cp\u003eTo further investigate the function of CACYBP in cancers, correlation analyses between CACYBP and cancer stemness indices were conducted. The results indicated that high CACYBP were positively associated with six cancer stemness scores, including DNAss, RNAss, DMPss, ENHss, EREG-EXPss and EREG-METHss in the majority of cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Enrichment analysis of CACYBP-related genes\u003c/h2\u003e \u003cp\u003eWe obtained the top 50 experimental determined CACYBP-interacted genes and the top 100 CACYBP-correlated genes from the STRING and GEPIA2 database (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A PPI network was developed to visualize the interactions among CACYBP and its interacted genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003eA). The top 5 CACYBP-correlated genes, including CCT3, UCHL5, CENPL, PTGES3 and UBE2T showed a strong association with CACYBP expression ( \u003cem\u003eR\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003eB). In addition, the heat map showed a significant and positive correlation between CACYBP and these 5 genes in 33 TCGA tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003eC). The common gene CKS1B was identified by a Venn plot analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, KEGG and GO enrichment analyses were performed on the CACYBP-interacted genes. The results showed that CACYBP-interacted genes were mainly enriched in some KEGG pathways, including Wnt signaling, Hippo signaling, Ubiquitin mediated proteolysis, Signaling pathways regulating pluripotency of stem cells, Hedgehog signaling, Neurotrophin signaling, cell cycle, mTOR signaling, TGF-β signaling and T cell receptor signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003eE). In addition, molecular function GO analysis suggested that CACYBP-interacted genes might be involved in regulating some functional pathways, including β-catenin binding, gamma-catenin binding, armadillo repeat domain binding, ubiquitin protein ligase binding, protein domain specific binding, protein binding, enzyme binding, catalytic activity, protein C-terminus binding, transferase activity, protein kinase binding and transcription factor binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003eF). These data indicated that the interactions among CACYBP and its related genes might potentially contribute to tumorigenesis and cancer progression.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eWith the rapid development of bioinformatics technology, researchers can conduct pan-cancer analysis to fully explore the potential mechanism of single genes in specific cancer types and compare the commonality and differences of specific genes in various tumors, contributing to develop effective specific genes for cancer prevention and treatment. CACYBP consists of three domains: the N-terminal helical hairpin domain, the CS domain, and the C-terminal SGS domain, which is a multifunctional protein present in various cells, especially in cancer cells\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Although some studies have attempted to reveal the mechanism of CACYBP in tumors, it is only limited to a few tumors and existed some controversies. Herein, a series of large public databases were used to conduct a systematic and comprehensive analysis of CACYBP in pan-cancer. The results showed that CACYBP was highly expressed in multiple tumors and led to a poor prognosis in some tumors. CACYBP expression was closely linked with immune cell infiltration. Furthermore, aberrant CACYBP expression was significantly correlated with DNA methylation, RNA methylation, MMRs, methyltransferases, TMB, MSI and tumor stemness in various cancers. Thus, CACYBP might become a novel prognostic, diagnostic biomarker and therapeutic target for human cancers.\u003c/p\u003e \u003cp\u003eIn the present study, we illustrated the expression pattern of CACYBP in pan-cancer and found CACYBP was overexpressed in 25 TCGA cancers. This finding was consistent with certain cancers in previous studies, such as gastric cancer, colon cancer, osteosarcoma, etc\u003csup\u003e[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Notably, CACYBP was not only overexpressed in COAD, PAAD and BLCA but also promoted tumor metastasis and recurrence, contributing to a poor prognosis\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Our prognostic analysis also revealed that CACYBP performed well in predicting the survival of ACC, BLCA, LUAD and KIRP, constituting a prognostic biomarker. On the other hand, a low CACYBP expression was observed in KICH and LAML. KICH is a sort of renal cell carcinoma (RCC), and CACYBP was reported as a down-regulated and tumor suppressor gene in RCC, it can suppress RCC proliferation via degrading β-catenin and affecting the cell cycle\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. A similar suppression mechanism of CACYBP was also found in gastric cancer\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. CACYBP was found down-regulated in Chronic lymphocytic leukemia (CLL) and exerted a tumor suppressor role\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, the expression and the role of CACYBP in LAML have not been reported. The reason why the role of CACYBP differ in cancers may include: (i) The E3 enzyme formed by CACYBP varies in different cells; (ii) CACYBP acts on intracellular proteins with different functions;(iii) CACYBP, as a phosphatase, can regulate the expression of intracellular proteins\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Our study suggested that CACYBP may become a novel prognostic biomarker for human cancer. More studies are welcomed to illustrate the role of CACYBP in single tumors.\u003c/p\u003e \u003cp\u003eIn cancers, genetic alternations commonly occur, allowing stochastic gene activation and silencing and conferring a proliferative advantage to cells\u003csup\u003e[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Indeed, we found that CACYBP genetic alternations frequently occurred in multiple TCGA cancers, predominantly amplification and followed by mutation. Among them, CACYBP amplification may be driven by chromothripsis in cancers\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. We also found that M127I/V is the highest frequency mutation site located at the CS domain of the CACYBP gene, and mainly mutated in LSCC, CRC and HNSC.\u003c/p\u003e \u003cp\u003eEpigenetics modifications, critical mediators of plasticity in cancer, participate in tumorigenesis and progression\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Among them, aberrant DNA methylation is a common epigenetic feature of cancer and might be detected in circulating cell-free DNA at the early stage of cancer development, constituting a valuable cancer biomarker\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, MMR genes and methyltransferases are also essential for the DNA repairment pathway, maintaining the accurate transmission of genetic materials\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Dysfunctions of MMR genes may increase the accumulation of mutation, resulting in a high MSI burden in cancers\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Indeed, the present study showed that the CACYBP promoter methylation level was obviously increased in six TCGA tumors, and was obviously decreased in nine TCGA tumors. And we also observed a positive correlation of CACYBP methylation with MMR genes and methyltransferases in multiple cancers, indicating that MMR genes and methyltransferases are involved in CACYBP DNA repairment. RNA methylation, as a critical regulator of transcript expression, is closely linked with cancer cell proliferation, cellular stress, metastasis and immune response, and is becoming a promising target of cancer therapy\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. In this study, we also found a close relationship between CACYBP and RNA regulatory genes. Taken together, the common strong correlations of CACYBP with DNA methylation, MMR genes, methyltransferases and RNA methylation regulatory genes suggested that CACYBP might be an ideal epigenetic therapeutic target.\u003c/p\u003e \u003cp\u003eThe tumor microenvironment (TME) is essential for regulating tumor growth, progression and metastasis\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Immune cells represent a large fraction of TME, thus they may exert a critical role in regulating cancer immune responses, like immune escape\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. The role of CACYBP in the immune microenvironment has gradually been revealed, recent study reported that CACYBP may enhance the host immunity to protect against malaria infection\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. And some evidence indicated that CACYBP was involved in regulating TME in tumors, such as hepatocellular carcinoma, esophageal cancer and colorectal cancer\u003csup\u003e[\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. CACYBP may be a promising target for cancer immunotherapy, especially for hepatocellular carcinoma\u003csup\u003e[\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. In the present study, we found that CACYBP expression was negatively linked with the ESTIMATEScore, the ImmuneScore, and the StromalScore in multiple cancers. And the results indicated a lower fraction of immune cells and stromal cells present in the core and the invasive margin of tumors\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. To identify the role of CACYBP on immune infiltration, our analysis focused on three critical immune cells in TME, including cancer-associated fibroblasts (CAFs), CD4\u003csup\u003e+\u003c/sup\u003eT cells and CD8\u003csup\u003e+\u003c/sup\u003e T cells\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e. A significant correlation was observed between CACYBP expression and infiltration of these immune cells in various cancers. Furthermore, we found that CACYBP expression was significantly correlated with immune checkpoints, TMB and MSI in various cancers. TMB and MSI can effectively predict the treatment efficacy of immune checkpoint inhibitors and stimulators\u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e. Therefore, CACYBP may play a critical role in cancer immunity and might become an ideal therapeutic target for tumor immunotherapy.\u003c/p\u003e \u003cp\u003eTo further investigate to the latent role of CACYBP in cancers, we used single-cell data to analyze the expression and function of CACYBP in various tumors. Similarly, CACYBP was observed abundantly expressed in most single malignant samples. Single-cell function analysis indicated that CACYBP was significantly positively correlated with the cell cycle in BRCA, HNSC and LUAD. Previous studies had demonstrated that CACYBP may regulate the cell cycle in some cancers via degrading β-catenin\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. In addition, we found that CACYBP was positively related to DNA repair and DNA damage in BRCA and LUAD. Conversely, a negative correlation between CACYBP and functional status was observed in GBM and UVM. However, further studies are welcomed to verify these results in the future. We also paid attention to the cancer stem cell, a kind of self-renewing cell, which can facilitate tumor initiation, promote metastasis, and enhance cancer therapy resistance\u003csup\u003e[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e. We analyzed the correlation of CACYBP with several tumor stemness scores, including DNAss, RNAss, DMPss, ENHss, EREG-EXPss and EREG-METHss. We found that CACYBP was positively related to tumor stemness scores in multiple cancers, suggesting a stronger biological activity and weaker differentiation ability in tumor stem cells\u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e. These findings may help the clinician to select suitable drugs and predict prognosis.\u003c/p\u003e \u003cp\u003eInteractions among various genes and proteins are commonly existed and potentially contributed to cancer development. Thus, CACYBP may be involved in tumorigenesis by regulating its related genes and proteins. Indeed, the top five CACYBP correlated genes, including CCT3, UCHL5, CENPL, PTGES3 and UBE2T were identified in this study. And a strong positive correlation of CACYBP with these genes was observed in all TCGA tumors. Among the related genes, CCT3, as an important member of the chaperone protein family, was up-regulated in some cancers and involved in cell division, proliferation and apoptosis pathways\u003csup\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e. UCHL5, a member of the ubiquitin-proteasome system, participates in protein ubiquitination and deubiquitination processes in cancer progression\u003csup\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/sup\u003e. CENPL is one critical member of the centromere protein family, and may function as a potential biomarker and serve as an oncogene in pan-cancer, especially LUAD\u003csup\u003e[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/sup\u003e. PTGES3, a molecule chaperone of Hsp90, participates in pathogenesis including DNA replication and spliceosome\u003csup\u003e[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA Recent study reported that PTGES3 interacted with CACYBP, and they were highly co-expressed in lung adenocarcinoma, resulting in cancer progression\u003csup\u003e[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/sup\u003e. UBE2T, a ubiquitin-coupled enzyme, had been confirmed as a critical oncogene and therapeutic target in the majority of cancers\u003csup\u003e[\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e. Furthermore, KEGG analysis showed that CACYBP-interacted genes participated in some signaling pathways, such as Wnt signaling, ubiquitin mediated proteolysis and cell cycle. MF-GO analysis also revealed that CACYBP-interacted genes involved in the regulation of some cellular processes, such as protein binding, transferase activity and transcription factor binding. These data suggested that the function of CACYBP-related genes and their interactions contributed significantly to tumorigenesis. And more studies are needed to further explore the interactions and functions of CACYBP-related genes, which may provide new insight into cancer therapeutic strategy.\u003c/p\u003e \u003cp\u003eIn the present study, we performed a systemic and integrative pan-cancer analysis, which contributed to a deep understanding of the potential roles of CACYBP in human cancers from multi-genomics and multi-dimension perspectives. Some limitations should be considered when interpreting the results of this study. First, this study was based on a series of public databases, thus lacking clinical sample data for validation. In addition, molecular experiments are lacking for verifying the tumorigenesis and progression mechanisms of CACYBP in various cancers. Therefore, sufficient actual clinical data and laboratory experiments are needed in future studies to determine the functional mechanism of CACYBP in human tumors.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eTaken together, this pan-cancer study indicated significant correlation of CACYBP with genetic alternation, prognosis, DNA methylation, RNA methylation, immune reactivity and tumor stemness, indicating CACYBP may become a promising prognostic biomarker and therapeutic target for human cancers.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCACYBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCalcyclin-binding protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSIP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSiah-1-interacting protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMRs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMismatch modified genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImmune checkpoint genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTMB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor mutational burden\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMicrosatellite instability\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor microenvironment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDisease-specific survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdrenocortical carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBLCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBladder urothelial carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBRCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBreast invasive carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCESC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCervical squamous cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHOL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCholangiocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eColon adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDLBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLymphoid neoplasm diffuse large B-cell lymphoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEsophageal carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlioblastoma multiforme\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHNSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHead and neck squamous cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKICH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney chromophobe cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKIRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney renal clear cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKIRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney renal papillary cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKIPAN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePan-kidney cohort (KICH\u0026thinsp;+\u0026thinsp;KIRC\u0026thinsp;+\u0026thinsp;KIRP)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLAML\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute myeloid leukemia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrain lower grade glioma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLIHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLiver hepatocellular carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLUAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLung adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLUSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLung squamous cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMESO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMesothelioma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOvarian serous cystadenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePancreatic adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCPG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePheochromocytoma and Paraganglioma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProstate adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eREAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRectum adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSARC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSarcoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStomach adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSKCM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSkin cutaneous melanoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTGCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTesticular germ cell tumors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTHCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThyroid carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTHYM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThymoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUCEC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUterine corpus endometrial carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUterine carcinosarcoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUVM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUveal melanoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eH.L. wrote the article and performed data analyses; L.L., Z.L. and J.Z. performed data analyses; F.G., L.M. and B.H. revised the final manuscript. X.Z.,Y.D.,Y.G and Z.C. prepared literature. H.Z. conceived the study. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was supported by the National Natural Science Foundation of China (No. 8197031585).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe data used to support the findings of this study are available from the corresponding authors upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel R L, Miller K D, Fuchs H E, et al. 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Oncogene, 2021,40(5):1027\u0026ndash;1042.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CACYBP, pan-cancer, bioinformatics, immunity, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-3247132/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3247132/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCalcyclin-binding protein or siah-1-interacting protein (CACYBP/SIP), a target protein of calcyclin S100A6 and an essential component of E3 ubiquitin ligase, had been proven to play significant roles in some cancers, but its pan-cancer function remains unknown. In the present study, we used a series of databases, including TCGA, GTEx, CPTAC, HPA, cBioPortal, UCLCAN, UCSC, CancerSCEM, CancerSEA, CancerSEA, GEPIA2 and STRING to explore the potential roles of CACYBP in pan-cancer. We systematically revealed the expression patterns of CACYBP, and the potential associations between CACYB expression and genetic alternation, prognosis, DNA methylation, RNA modification, immune reactivity, tumor stemness and enrichment pathways in pan-cancer. The results showed that CACYBP was significantly increased in various cancers compared to corresponding normal tissues. CACYBP mutation was frequently presented in various cancers. In addition, CACYBP expression was significantly correlated with prognosis, DNA methylation, RNA methylation, immune cells infiltration, immune checkpoint genes (ICGs), immune scores, tumor mutational burden (TMB), microsatellite instability (MSI) and tumor stemness in various cancers. We also discovered that CACYBP was abundantly highly expressed in the majority of cancers at a single-cell level and was significantly positively correlated to the single-cell functions of certain tumors, such as the cell cycle, DNA damage and DNA repair. Furthermore, CACYBP-related genes were mainly enriched in signaling pathways correlated with the tumor microenvironment (TME) and cancer development. Taken together, CACYBP plays an essential role in oncogenesis, and might serve as a promising prognostic biomarker and immunotherapeutic target in human cancers.\u003c/p\u003e","manuscriptTitle":"Systemic Pan-Caner Analysis Identifies CACYBP as a Novel Biomarker for Cancer Prognosis and Immunity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-16 14:14:01","doi":"10.21203/rs.3.rs-3247132/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":"a21e1644-f990-4766-a2bc-74300531681e","owner":[],"postedDate":"August 16th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":23942214,"name":"Biological sciences/Cancer"},{"id":23942215,"name":"Health sciences/Biomarkers"}],"tags":[],"updatedAt":"2024-04-02T10:07:11+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-16 14:14:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3247132","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3247132","identity":"rs-3247132","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}