Unlocking hidden potential: The Prognostic Value and Immunoinfiltration of CACUL1 in Malignant Tumours

Unlocking hidden potential: The Prognostic Value and Immunoinfiltration of CACUL1 in Malignant Tumours | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unlocking hidden potential: The Prognostic Value and Immunoinfiltration of CACUL1 in Malignant Tumours Yuhan Tan, Ju Wang, Ying Kong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4015982/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: CDK2 associated cullin domain 1 (CACUL1), also named C10ORF46, is a poorly understood gene. Growing evidence illustrates that CACUL1 plays a potential role in malignant tumors. However, the prognostic value of CACUL1 in malignant tumors didn’t significant. Methods: In this study, HPA, TCGA, GEO, TIMER2, GEPIA, GTEx, CPTAC, TISCH, and a variety of other bioinformatics tools were used. The expression was verified by immunohistochemistry. Results: CACUL1 was markedly overexpressed in tumours and correlated with poor prognosis. It will be a potiental biomarker for predicting HCC prognosis. The evidence of a variety of genetic and epigenetic signatures of CACUL1 in different types of cancer has been studied, and some of the results are also in relation to prognosis. Additionally, CACUL1 is associated with the expression of currently recognised immune checkpoints or infiltrates. Further analysis of CACUL1 and tumour-associated immune cells revealed a link between CACUL1 and macrophages in multiple tumour types. The promotion of poor prognosis by CACUL1 may be associated with a tumor-promoting phenotype of macrophages. Functional prediction of CACUL1 has focused on the molecular pathways of metabolism and the pathways in cancer. It is suggested that metabolic pathways may be the mechanism by which CACUL1 exerts its function to affect macrophage polarisation and thus promote poor prognosis. Finally, immunohistochemistry staining demonstrated that CACUL1 expression is markedly higher in tumour tissues. Conclusion: This first pan-cancer study of CACUL1 suggests a carcinogenic function in multiple tumors, and its closeness to immune cells hints at its potential application in anti-tumor immunotherapy. bioinformatics CACUL1 tumour-associated immunity biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction From the initial gene mutation to the complex correlation of various metabolic components under the influence of the TME, the mechanism of cancer occurrence and development is complexed. Immunotherapy can be ineffective in some patients due to immune resistance caused by dynamic cancer cell-immune cell evolution and interaction. Failure to respond to immune therapy has been strongly linked to increased expression of certain genes involved in energy metabolism[ 1 ]. To better understand the mechanisms and provide new direction for diagnosis and treatment, it is essential to discover the new genes in multi-tumour species. In this study, data mining analysis was used to show CACUL1's prognostic value and analysed CACUL1's expression and its relationship with TIICs, immune checkpoints or response to immunotherapy. Our findings suggest that increased expression of the CACUL1 is detrimental to survival. Furthermore, CACUL1 has high affinity for immunity, including macrophages, and is co-expressed with M2-like macrophage surface markers. Taking these facts together, CACUL1 was shown to be a potential biomarker of poor survival and associated with tumour immunity. Material and Methods 1. Analysis of Gene Expression The mRNA expression of CACUL1 in normal tissues was determined using the HPA website ( https://www.proteinatlas.org/ ). TIMER2 ( http://timer.cistrome.org/ ) was a tool to determine mRNA levels between tumour and control samples. For tumour types that didn't contain the corresponding normal tissue, we use GEPIA data ( http://gepia2.cancer-pku.cn/ ) as a supplement. The gene abundance was also complemented by data from the GTEX and TCGA databases. Different protein levels in the CPTAC ( https://proteomics.cancer.gov/programs/cptac ) were analysed with the UALCAN website ( https://ualcan.path.uab.edu/analysis.html ). CACUL1 expression in BRCA subtypes was obtained using the TISDIB database ( http://cis.hku.hk/TISIDB/index.php ). Using the GEPIA2 stageplot module ( http://gepia2.cancer-pku.cn/ ), we also investigated the relationship of CACUL1 levels with clinical stage. 2. Analysis of Survival Prognosis Prognostic data for CACUL1 in different tumour types, including OS and DFS, were analysed using the GEPIA2 subunit's "survival map" module[ 2 ]. 3. Analysis of Genetic Alterations The cBioPortal ( http://www.cbioportal.org ) was used to evaluate genetic alterations[ 3 ]. 3D structure of CACUL1 as predicted according to the website of alphafold ( https://alphafold.ebi.ac.uk/ )[ 4 ]. In addition, CACUL1 SNV and CNV percentages are provided by GSCA[ 5 ] ( https://guolab.wchscu.cn/GSCA/#/ ). 4. Analysis of Immune Infiltration The association between CACUL1 levels and immune infiltration were collected with the TIMER2. The "Immunity Score" and "Stroma Score" integrated can be used to relate CACUL1 expression to immune infiltration. Using TISIDB dataset, we investigated the relationship between immunosuppressive and immunostimulatory factors and CACUL1 using the "Immunomodulators" module; and we assessed the correlation between chemokines and receptors and CACUL1 levels using the "Chemokines" module. The "Pan-Cancer" module is a component of ACLBI tool ( https://www.aclbi.com ), which analysed the relationship between the target genes and TMB and MSI. Furthermore, the "Immunity" part of the tool was used to assess CACUL1 expression with immunosuppressive factors and ICB response. We also analysed HCC using the "Immunity" module of the ACLBI tool. The TIDE score was taken to assess tumour immuno-escape. 5. Single-cell level Analysis TISCH database was used for single-cell analysis. The "gene" module was selected for pan-tumor cell "malignance" component analysis of CACUL1, and then the "major-lineage" component analysis was conducted for LIHC, CHOL, and BRCA. Finally, the distribution of CACUL1 cells in a single dataset was explored[ 6 ]. 6. Enrichment Analysis and Drug Sensitivity The GSE36376 dataset from LIHC was retrieved and differential genes were then chosen (fold change = 2, p < 0.05). The 467 differential genes were analysed for KM (OS, p < 0.05), leaving 259 genes that were subsequently correlated with CACUL1, with the final result suggesting 239 candidates. Using the DAVID database, GO and KEGG analysis was performed on 239 differential genes. Heatmap provided by an online platform ( https://www.bioinformatics.com.cn ) for analysing and visualising data (last accessed on 10 Nov 2023). In addition, the genes corresponding to the first 100 proteins that bind to CACUL1 were selected based on a search of the String website ( https://cn.string-db.org/ ) for KEGG analysis again. We searched for the target gene CACUL1 on GeneMANIA ( http://genemania.org/ ) and selected "Homo sapiens" to hits in order to obtain the network interactions map of CACUL1. CTRP drug sensitivity analyses were performed on CACUL1 using the GSCA website. On the basis of the GDSC transcriptomic database[ 7 ] ( https://www.cancerrxgene.org/ ), the relationship between different levels of gene expression and the IC50 of sorafenib has been analysed using the "IC50 module" of the ACLBI tool and the significance has been tested using the Kruskal-Wallis test. 7. Pathway correlated Analysis of CACUL1 Correlation analysis and various pathway scores[ 8 ] was performed using the "Pathway Correlation" subunit of the ACLBI tool, which is analysed in the background by The GSVA package of the R software (R version 4.0.3, p < 0.05) to get the link from CACUL1 to pathways. 8. Immunohistochemistry of CACUL1 CACUL1 expression was demonstrated by immunohistochemical staining. C10orf46 antibody (Ab 190799) was acquired from Abcam company, and tissue chip containing multiple tumour tissue samples was purchased from Shanghai OutDo Biotech Company. The slide was incubated overnight with a 1:200 dilution of CACUL1 antibody. 9. Statistical Analysis Different statistical methods were used depending on the nature of the data. Student's t-test is commonly applied to compare pairs of normatively distributed datasets with homogeneous variances, ANOVA is applied for comparing groups greater than or equal to three, followed up tests by Dunnett's post-hoc. However, if any of the above conditions were not met, the data were not normally distributed or did not satisfy homogenous variances, for variance, the Mann-Whitney test, followed by Tamhane's T2 test, was used to compare between two groups, and the Kruskal-Wallis test, followed by Dunnett's T3 test, to compare between three or more groups. R (version 4.0.3) was used for data analysis. Statistical significance was defined as p < 0.05. Results 1. Expression Information of CACUL1 Although CACUL1 protein expression was not found in HPA database[ 9 ], Fig. 1 a shows CACUL1 mRNA levels in different normal tissues, suggesting that CACUL1 is not highly tissue specific. Using TIMER, we assessed the expression characteristics of CACUL1 in many tumour types. CACUL1 expression showed higher levels in some types of tumours such as CHOL, ESCA, KIRC, HNSC, STAD, LUAD, and LIHC (Fig. 1 b). If matched normal tissue was not available in the TIMER database, the GEPIA database was used as an adjunct. CACUL1 mRNA levels were also found to be clearly higher in CHOL, PAAD, TGCT and UCS (Fig. 1 c). We chose to supplement the data from GTEX for non-tumour tissue samples that are missing from the TCGA database. Figure 1 d shows that CACUL1 was overexpressed in most cancers, including BRCA, GI (CHOL, COAD, LIHC, PAAD, READ, STAD), Kidney (KIRC, KIRP), Reproductive (OV, PRAD) and other types of cancer. We then analysed CACUL1 protein levels in tumours with the help of the CPTAC dataset. CACUL1 protein levels vary in different tumours (Fig. 1 e) and its expression varies in different subtypes of BRCA (p < 0.01) (Fig. 1 f). GEPIA2 analysis showed a clear relationship among expression levels and clinical stage in KIRC (p = 0.00407) and TGCT (p = 0.00656) (Fig. 1 g). 2. Prognostic Features of CACUL1 The "CANCER & CELL LINES" module of HPA database suggests that CACUL1 is a marker of poor prognosis in hepatocellular carcinoma (Fig. 2 a). To analyse the association between CACUL1 alterations and clinical outcome, different cancer types were divided into two subgroups according to the level of CACUL1 expression. OS results indicated that increased CACUL1 expression was linked to worse outcome in ACC, KIRC, LGG, LIHC, LUSC and PAAD (Fig. 2 b). Using DFS as an outcome measure, it is suggested that higher CACUL1 expression is linked to worse outcome in the following types: ACC, BRCA, KIRC, LIHC, PAAD and UCEC (Fig. 2 c). COX regression analyses and forest plots were performed on CACUL1, which was identified as being with worse outcome in LGG, LIHC and PAAD (Fig. 2 D). Analysis of 371 HCC cases in the TCGA using univariate-Cox regression suggests that CACUL1 is associated with poor survival; univariate-Cox regression and multifactorial-Cox regression suggest that CACUL1 may be an independent predictor of HCC (Fig. 2 E). 3. The Genetic Alteration of CACUL1 Using the cBioPortal website to analyse the genetic alterations in different tumours[ 10 ], the majority of gene alterations were found in UCEC, which was mainly characterised by 'mutations'. However, for pheochromocytomas and paragangliomas, the "amplified" type of CNA was the most common (Fig. 3 a). There is a summary of the mutation types of CACUL1, including structural variants, mutations, and copy number variants, as well as their distribution within different types of cancers (Fig. 3 b). Figure 3 c shows the types, loci and case numbers of CACUL1 mutations. The predicted 3D protein structure of CACUL1 based on the Alphafold ( https://alphafold.ebi.ac.uk/ ) is shown in Fig. 3 d[ 11 ]. Moreover, data from GSCA suggested that CACUL1 has different percentages of SNV and CNV (Fig. 3 e, f). Figure 3 g demonstrated the relationship between CNV and prognosis in different tumours. 4. Analysis of CACUL1 and Immune-related Data Using various algorithms such as EPIC, MCPCOUNTER, XCELL and TIDE[ 10 ], we found statistically positive correlations between estimated CAFs infiltration values and CACUL1 expression in LIHC, LUAD, LUSC, PAAD and THYM tumours (Fig. 4 a). Figure 4 b shows the link from CACUL1 to immune system infiltration using immune scores, microenvironment scores and stromal scores. Figure 4 c clearly shows that CACUL1 expression was tightly linked to immuno-cell infiltration, especially LIHC. This may indicate that CACUL1 might be a new target for this immune-infiltrated malignancy. Although CTLs are one of the most powerful cellular components in the host for direct tumour cell killing and defence against foreign factors[ 12 ], the negative correlation between CACUL1 and CD8 + T-cell expression in THYM suggests that CD8 + T-cell-mediated tumour killing may be weaker in hosts with high CACUL1 expression. TISDIB brings together multiple data types to predict tumour-immunity correlations based on gene expression and is an interoperability portal database for tumours and immune infiltrates. Figure 4 d, e, f, g shows the correlation analysis of CACUL1 with Immunoinhibitor, immunostimulators, chemokines, and receptors from TISDIB. TMB and MSI/dMMR are recently available biological markers for predicting response to immunotherapy[ 13 ]. CACUL1 expression has a positive connection with TMB in the following cancers: ACC, LAML, THYM, SKCM and PAAD. And MSI has a positive connection in GBM, READ, STAD, COAD and LAML and a negative connection in DLBC and UCS (Fig. 4 h、4i). In recent years, there has been a trend in cancer therapy research towards the use of specific antibodies that recognise and attach to immune checkpoint molecules in order to stimulate immune activity[ 14 ]. Analysis of the HCC data showed that CACUL1 expression was a positive co-regulator of a number of immune checkpoint molecules, which may indicate that CACUL1 is responsible for immunosuppression. Given that CACUL1 is positively associated with checkpoints, we next analysed an association of ICIs and CACUL1 in HCC. Higher TIDE scores, higher risk of immune escape and worse response to immunotherapy were observed with CACUL1 overexpression (G2)[ 15 ] (Fig. 4 k). These are a promising target point for inhibition or reversal of the immunosuppressive microenvironment created. 5. Single-cell level analysis of CACUL1 To examine the correlation among the tumour microenvironment cell types and CACUL1, we used the TISCH database [ 6 ]. First, a "malignancy" analysis was performed using the "Gene" module, including LIHC, CHOL, BRCA, and PAAD. The results showed that CACUL1 was mainly Fig. 5 Single cell analysis of CACUL1 expression using Tisch database distributed in immune cells and malignant cells (Fig. 5a). Further " major-lineage" analysis of LIHC, CHOL, and BRCA suggested that CACUL1 showed high enrichment in monocytes/macrophages (Fig. 5b, c, d, e). The "Dataset" module was used to up-regulate CACUL1. In the GSE140228 data set of LIHC, it was found that CACUL1 was mainly distributed in immune cells, and further analysis showed that it was more densely distributed in macrophages[ 16 ] (Fig. 5f). The correlation between CACUL1 and molecular markers (CD163 + CD68)[ 17 ] on the surface of M2 macrophages was subsequently verified in liver cancer (Fig. 5g). Consistent with single-cell analysis, CACUL1 was co-expressed with M2 macrophage surface markers. We further validated the GSE-28490 dataset from the GEO database. The peripheral blood of healthy people was divided into 6 immune cell lines for data analysis, in which the G1 group represented the monocyte group and the G2 group represented other cell types. We found that CACUL1 expression was markedly enriched in monocyte lines compared to other cells (Fig. 5h). 6.Enrichment and drug sensitivity analyses Gene enrichment is critical to our understanding of the molecular mechanisms underlying cancer genes. We first picked out the differential genes. The GSE36376 dataset of HCC showed 196 upregulation and 271 downregulation genes compared to normal tissue. These 467 genes were Fig. 6 Functional annotation and drug sensitivity analysis then analyzed by KM survival analysis, which identified 259 genes associated with prognosis. Then, the correlation between 259 genes and CACUL1 was analyzed, and P < 0.05 was considered significant. Finally, 239 differential genes were screened for GO and KEGG enrichment analysis. GO analysis revealed that these differential genes were grouped into 103 BP terms, 67 CC terms and 62 MF terms (Fig. 6a).The result of KEGG enrichment analysis comprised 27 paths and was significantly enriched in metabolic paths, including glucose, retinol, fatty acids, amino acids and drug metabolism (Fig. 6b).In addition, KEGG pathway analysis of the top 100 CACUL1 interacting proteins was performed on String and included " the cancer pathway", suggesting that CACUL1 plays a role in malignancy (Fig. 6c-d). GeneMANIA is a comprehensive site that brings together a large collection of genomics and proteomics used to predict the function of target genes[ 18 ]. A search for CACUL1 using GeneMANIA yielded genes with relevance in physical interactions, co-expression, co-localisation, genetic interactions, enrichment pathways, etc. (Fig. 6e). Figure 6f shows the relationship of CACUL1 expression to the susceptibility to CTRPs (data from GSCA). The sensitivity of sorafenib in HCC was also investigated, and CACUL1 was positively correlated with the IC50 of sorafenib (Fig. 6g). 7. Pathway correlated Analyses of CACUL1 in HCC Gene pathway studies have also been used in the research of the possible effects of CACUL1 in HCC.The enrichment of target genes in each pathway is analysed using the "correlative analysis" module of the ACLBI tool[ 19 ]. Gene pathway mapping revealed that the pathways associated Fig. 8 Immunohistochemical staining of CACUL1 with CACUL1 were angiogenesis, cellular response to hypoxia, DNA replication, EMT markers, TGF-β, tumour proliferation signature, P53 pathway, ether lipid metabolism and fatty acid degradation (Fig. 7 a-i). 8. Expression of CACUL1 in Some Tumors Although HPA predicts that CACUL1 is expressed in the nucleus[ 20 ], corresponding immunohistochemical staining maps are lacking. Based on differences in CACUL1 mRNA expression between tumours and controls, tumour types for IHC were identified. Scans showed that the stain of CACUL1 in THCA、BRCA、LUAD、LUSC、ESCA、LIHC、CHOL、STAD、COAD and KIRC (Fig. 8a-j) was more intense than that of the adjacent normal tissue. We show the staining results at 50 and 100 magnification. Discussion Currently, malignant tumours remain one of the world's leading killers and are the leading public health threat[ 21 ]. Therefore, to promote a good prognosis for patients with advanced tumors, it is very meaningful to understand the various stages of tumor occurrence and metastasis and important target molecules[ 22 ]. Precision medicine is currently facing a thorny problem in the tumor industry[ 23 ], targeted and immunotherapies are key treatment for patients. CACUL1's research is mainly focused on prostate cancer, gastric cancer and Alzheimer's disease[ 24 – 26 ], and its role in other diseases is not clear. This study was found that CACUL1 represents a poor prognosis for many tumours. We performed immunohistochemical staining of several tumour types, which coincided with bioinformatic results of CACUL1 expression. Most importantly, we performed a comprehensive and detailed analysis of CACUL1 and tumor immunity, suggesting that CACUL1 is closely related to anti-tumor immunity. The above immune-correlation analysis of CACUL1 also suggests that CACUL1 is associated with the immune environment and shows a good response to immunotherapy. Decoding the signature code of individual cells in the tumor microenvironment is extremely important. We performed single-cell analysis using the TISCH database and found that CACUL1 is expressed on immune cells of different tumor types, especially monocytes/macrophages. As the commonest defence cells in the surrounding tumour, TAMs exist in different phenotypes[ 27 – 29 ],and lipid droplets are considered to be an important factor in promoting the polarization of macrophages towards M2[ 30 ]. The correlation analysis between CACUL1 and M2 surface markers was consistent, suggesting the potential contribution of CACUL1 to tumor immunosuppression. Gene enrichment analyses suggest that CACUL1 is functionally focused on the "Metabolic pathways", "Fatty acid degradation" and “Pathways in cancer”. Similar results were shown for pathway correlation. Disturbed lipid metabolism is known as an important factor in inducing macrophage polarisation in the tumour microenvironment[ 31 ]. Combined with the distribution of CACUL1 in mononuclear/macrophages and its poor prognosis, we hypothesized that CACUL1 is related to the M2 phenotype of macrophages, which further experiments will have to verify. We still need to explore CACUL1 at the cellular level, hoping to provide a ray of light for targeted and immunotherapy of advanced cancer patients. Conclusions These results indicate that CACUL1 is tightly linked to tumour, in particular tumour immunity, as well as being a prognostic indicator of clinical prognosis and immune invasion. And we speculate that CACUL1 may inhibit anti-tumor immune responses and is more likely to be associated with macrophages. However, our study has its own inherent limitations. All data analyses are based on online databases, so further experiments are needed to complete the interpretation. Abbreviations TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus;TIMER2: the Tumor Immune Estimation Resource database; GEPIA: the Gene Expression Profiling Interactive Analysis database; GTEx: Genotype-Tissue Expression; CPTAC: Clinical Proteomic Tumor Analysis Consortium; TISH: Tumor Immune Single-cell Hub; TISDIB: an integrated repository portal for tumor-immune system interactions; GSCA: Gene Set Cancer Analysis; CTRP: The Cancer Therapeutics Response Portal; ACLBI: assistant for clinical bioinformatics; HPA the Human Protein Atlas database; GO: Gene Ontology; BP: Biological Process MF: Molecular Function; CC: Cell Component; KEGG: Kyoto Encyclopedia of Genes and Genomes; IHC: immunohistochemistry ACC: Adrenocortical carcinoma; BLCA: Bladder urothelial carcinoma; BRCA: Breast invasive carcinoma; CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL: Cholangiocarcinoma; COAD: Colon adenocarcinoma; DLBC: Lymphoid neoplasm diffuse large B-cell lymphoma; ESCA: Oesophageal carcinoma; GBM: Glioblastoma multiforme; HNSC: Head and neck squamous cell carcinoma; HCC: Hepatocellular carcinoma; KICH: Kidney chromophobe; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LAML: Acute myeloid leukaemia; 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; SKCM: Skin cutaneous melanoma; STAD: Stomach adenocarcinoma; STES: Stomach and oesophageal carcinoma; TGCT: Testicular germ cell tumours; THCA: Thyroid carcinoma; THYM: Thymoma; UCEC: Uterine corpus endometrial carcinoma; UCS: Uterine carcinosarcoma; UVM: Uveal melanoma; TIICs: Tumor infiltrating Leukocytes; OS: Overall Survival; DFS: Disease Free Surviva; ICB: Immune checkpoint blockade; CNA: Copy Number alterations; CNV: Copy Number variation; SNV: Stable Nuclear variation; CAFs: Cancer-Associated Fibroblasts; CTLs: Cytotoxic T Lymphocyte; TMB: Tumour mutation burden; MSI/dMMR: microsatellite instability/mismatch repair deficiency; ICIs: Immune Checkpoint Inhibitors; TME: Tumour Microenvironment; Declarations Author contributions Y.T. and J.W. designed and supervised the study; Y.T. analysed the data and wrote the first draft of the paper; Y.K. supervised and reviewed the paper. All authors contributed to the article and approved the submitted version. Ying Kong*: Corresponding author. Department of Oncology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China. Interest conflicts Authors involved declare no competing interests Declaration of consent Not applicable. Statement of Data Availability The data were obtained from publicly available online databases. Acknowledgments We thank to all the participants at all stages of the process. Thanks for mapping support to Shanghai Xinnuo Biotechnology Co., Ltd. (https://www.bioinformatics.com.cn, last visited on 2023.11.10). Ethical approval There is no ethical statement to be declared. Funding This work was supported by Natural Science Basic Research Plan in Shaanxi Province of China (2020JM-369) and Beijing Science and Technology Medical Development Foundation (KC2021-JX-0186-88). 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Environ Sci Technol 55:9926–9937 Gao F, Yu B, Rao B, Sun Y, Yu J, Wang D, Cui G, Ren Z (2022) The effect of the intratumoral microbiome on tumor occurrence, progression, prognosis and treatment. Front Immunol 13:1051987 König IR, Fuchs O, Hansen G, von Mutius E, Kopp MV (2017) What is precision medicine? Eur Respir J 50:1700391 Choi H, Lee SH, Um SJ, Kim EJ (2016) CACUL1 functions as a negative regulator of androgen receptor in prostate cancer cells. Cancer Lett 376:360–366 Kong Y, Ma LQ, Bai PS, Da R, Sun H, Qi XG, Ma JQ, Zhao RM, Chen NZ, Nan KJ (2013) Helicobacter pylori promotes invasion and metastasis of gastric cancer cells through activation of AP-1 and up-regulation of CACUL1. Int J Biochem Cell Biol 45:2666–2678 Kong Y, Bai PS, Sun H, Nan KJ (2012) Expression of the newly identified gene CAC1 in the hippocampus of Alzheimer's disease patients. J Mol Neurosci 47:207–218 Vinogradov S, Warren G, Wei X (2014) Macrophages associated with tumors as potential targets and therapeutic intermediates. Nanomed (London England) 9:695–707 Jin Y, Kang Y, Wang M, Wu B, Su B, Yin H, Tang Y, Li Q, Wei W, Mei Q, Hu G, Lukacs-Kornek V, Li J, Wu K, Yuan X, Wang W (2022) Targeting polarized phenotype of microglia via IL6/JAK2/STAT3 signaling to reduce NSCLC brain metastasis. Signal Transduct Target Therapy 7:52 Peng H, Wu X, Liu S, He M, Xie C, Zhong R, Liu J, Tang C, Li C, Xiong S, Zheng H, He J, Lu X, Liang W (2023) Multiplex immunofluorescence and single-cell transcriptomic profiling reveal the spatial cell interaction networks in the non-small cell lung cancer microenvironment. Clin Translational Med 13:e1155 Shapouri-Moghaddam A, Mohammadian S, Vazini H, Taghadosi M, Esmaeili SA, Mardani F, Seifi B, Mohammadi A, Afshari JT, Sahebkar A (2018) Macrophage plasticity, polarization, and function in health and disease. J Cell Physiol 233:6425–6440 Zhang Q, Wang H, Mao C, Sun M, Dominah G, Chen L, Zhuang Z (2018) Fatty acid oxidation contributes to IL-1β secretion in M2 macrophages and promotes macrophage-mediated tumor cell migration. Mol Immunol 94:27–35 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4015982","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276443921,"identity":"ae2f8c01-3e40-4b20-914a-e7fadada3bf9","order_by":0,"name":"Yuhan Tan","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yuhan","middleName":"","lastName":"Tan","suffix":""},{"id":276443922,"identity":"14147921-3919-409b-990e-9a0503f22590","order_by":1,"name":"Ju Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Ju","middleName":"","lastName":"Wang","suffix":""},{"id":276443923,"identity":"5544bea0-0a19-4811-9b33-510982116866","order_by":2,"name":"Ying Kong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACZgYGxgYgbX+8+QCDBFgogUgtDGeOJTBIJBCjhQGm5YaPAVQ1AS0Gx3kPv5xRcceucQbPNwnLH4cZ+NlzDBh+7sCj5TBfmuWGM8+Sm6V7NxtIJBxmkOx5Y8DYewafFh4zw4dth5PZZM5ufADSYnAjx4CZsY0ILTwSOQ8OgLTYE6HF+OHGtsN2EhI5jBBbJAhokQTawjjjzOEEA55jxgYSaek8EmeeFRzsxaOF7/wZ4489FYftDdibn0lL2FjL8bcnb3zwE48WhQMMbKA4T2wAEsxAFg9I9ABuDQwM8g0MzB+AtD2Iw/gBn9JRMApGwSgYsQAAK9lWFHyxSg8AAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Kong","suffix":""}],"badges":[],"createdAt":"2024-03-05 08:31:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4015982/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4015982/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52120841,"identity":"ddb04c0f-0064-4779-b284-f218c127b18a","added_by":"auto","created_at":"2024-03-07 04:34:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3410152,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of CACUL1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea: CACUL1 mRNA levels in human tissues from HPA RNA-seq data; b: The levels of CACUL1 mRNA expression by TIMER2; c: CACUL1 expression in tumour and non-tumour tissues (date from GEPIA); d: Differential expression of CACUL1 (data from TCGA and GTEx); e: Differential expression of CACUL1 protein (data from CPTAC); f: Expression status of the CACUL1 in BRCA subtypes (data from TISDIB) ; g: Pathological stages of CACUL1 (data from TCGA)(* p \u0026lt; 0.05; ** p \u0026lt; 0.01,*** p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4015982/v1/bd9056aa51ef593e1cee0808.png"},{"id":52121169,"identity":"31022f92-a973-48df-b50d-828ab66611ed","added_by":"auto","created_at":"2024-03-07 04:42:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2838362,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between CACUL1 Expression Level and Tumour Prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea: CACUL1 is a marker of poor prognosis in HCC (data from HPA)\u003cstrong\u003e; \u003c/strong\u003eb-c: Impact of CACUL1 on OS (a) and DFS (b) (data from GEPIA2); d: Forest plot of the effect of CACUL1 on tumour prognosis\u003cstrong\u003e; \u003c/strong\u003ee: CACUL1 may serve as an independent prognostic biomarker for HCC.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4015982/v1/a45c47150cd44d7e39e6c06b.png"},{"id":52120844,"identity":"44ad1b30-1348-4243-a594-d7a4ec02496c","added_by":"auto","created_at":"2024-03-07 04:34:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3038053,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMutational Features of CACUL1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea: Frequency of alterations in CACUL1 mutation types and mutation sites; b: General mutation counts for CACUL1 in human cancers; c: Mutation sites are displayed; d: The 3D prediction structure of CACUL1 was displayed; e-g: Frequency profile of SNV (e) and CNV (f) mutations in CACUL1; the relationship between CNV and prognosis (g) (figures by GSCA).\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4015982/v1/1741e54cba174a588f072b78.png"},{"id":52120847,"identity":"40235530-ca32-4ba8-b760-94193f4720b7","added_by":"auto","created_at":"2024-03-07 04:34:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5612749,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between immune infiltration and CACUL1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea: CACUL1 expression and CAF infiltration in human carcinoma was investigated using various algorithms; b: Association of CACUL1 expression with ImmuneScore and StromalScore; c: Heatmap of target gene expression associations with different immune cell types; d-g: Heatmap of immunosuppressive (d), immunostimulatory (e), chemotactic (f) and (g) receptor-associated factors with CACUL1 expression; h-i: Spearman correlation analysis ofTMB and MSI and CACUL1 expression; j: Correlation between CACUL1 expression and immunosuppression in liver cancer; k: TIDE algorithm for predicting CACUL1 response to predictive immune checkpoint inhibitors in liver cancer.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4015982/v1/784bb4443138772aa3d12bbe.png"},{"id":52120843,"identity":"fb24fed0-dbb3-4a9a-90ed-f95934bd530d","added_by":"auto","created_at":"2024-03-07 04:34:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2769602,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle cell analysis of CACUL1 expression using Tisch database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea: CACUL1 is widely expressed in immune cells;\u003cstrong\u003e \u003c/strong\u003eb-e: The highly expression of CACUL1 in macrophages was analyzed in LIHC, BRCA, CHOL and PAAD; f: Distribution of CACUL1 in different cell types in the LIHC-GSE140228 dataset;\u003cstrong\u003e \u003c/strong\u003eg: A positive correlation between CACUL1 and M2-like macrophage surface markers;\u003cstrong\u003e \u003c/strong\u003eh: The expression distribution of CACUL1 gene in different groups (G1: monocytes/G2: other cells) (* represents p\u0026lt;0.05, ** represents p \u0026lt; 0.01, *** represents p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-4015982/v1/5779ab4c5a021c488c936990.png"},{"id":52120848,"identity":"2821b6f0-82c9-4e3e-aa34-4e5f916aa5ca","added_by":"auto","created_at":"2024-03-07 04:34:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6835473,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional annotation and drug sensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea-b: GO (A) and KEGG (B) analysis entries for differential genes, selecting the top 10 for presentation;\u003cstrong\u003e \u003c/strong\u003ec: CACUL1-binding proteins using the STRING tool;\u003cstrong\u003e \u003c/strong\u003ed: KEGG analysis of the genes of the top 100 related proteins in string;\u003cstrong\u003e \u003c/strong\u003ee: Interaction network diagram for CACUL1 summarised by GeneMANIA;\u003cstrong\u003e \u003c/strong\u003ef: Correlation analysis between CACUL1 expression and CTRP drug sensitivity (data from GSCA);\u003cstrong\u003e \u003c/strong\u003eg: CACUL1 expression was positively correlated with IC50 of sorafenib.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-4015982/v1/449e134d19019f4ff1bd3b38.png"},{"id":52120846,"identity":"9b8e89fb-164d-4d84-83f8-476f24e00601","added_by":"auto","created_at":"2024-03-07 04:34:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2549596,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathway correlation of CACUL1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea-i Pathways significantly associated with CACUL1 expression.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-4015982/v1/d6e8c462405ae3c844f41455.png"},{"id":52121170,"identity":"e5ffba80-2a38-47de-b6af-e6cd47bbaa67","added_by":"auto","created_at":"2024-03-07 04:42:29","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":17672685,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunohistochemical staining of CACUL1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea1-j1: CACUL1 staining images in different tumor tissues (THCA, BRCA, LUAD, LUSC, ESCA, LIHC, CHOL, STAD, COAD and KIRC). Bar,500 um;\u003cstrong\u003e \u003c/strong\u003ea2-j2: Stained image of CACUL1 in the corresponding paracancerous tissue. Bar,500 um.\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-4015982/v1/de150e429c3a0ef8064239e4.png"},{"id":52193427,"identity":"6461220e-0d59-4fe5-8ee5-29aafd1bb67f","added_by":"auto","created_at":"2024-03-07 19:36:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4040757,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4015982/v1/448fdbea-94a8-4b4e-bc92-927dd4d7b057.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eUnlocking hidden potential: The Prognostic Value and Immunoinfiltration of CACUL1 in Malignant Tumours\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFrom the initial gene mutation to the complex correlation of various metabolic components under the influence of the TME, the mechanism of cancer occurrence and development is complexed. Immunotherapy can be ineffective in some patients due to immune resistance caused by dynamic cancer cell-immune cell evolution and interaction. Failure to respond to immune therapy has been strongly linked to increased expression of certain genes involved in energy metabolism[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. To better understand the mechanisms and provide new direction for diagnosis and treatment, it is essential to discover the new genes in multi-tumour species.\u003c/p\u003e \u003cp\u003eIn this study, data mining analysis was used to show CACUL1's prognostic value and analysed CACUL1's expression and its relationship with TIICs, immune checkpoints or response to immunotherapy. Our findings suggest that increased expression of the CACUL1 is detrimental to survival. Furthermore, CACUL1 has high affinity for immunity, including macrophages, and is co-expressed with M2-like macrophage surface markers. Taking these facts together, CACUL1 was shown to be a potential biomarker of poor survival and associated with tumour immunity.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1. Analysis of Gene Expression\u003c/h2\u003e \u003cp\u003eThe mRNA expression of CACUL1 in normal tissues was determined using the HPA website (\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). TIMER2 (\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) was a tool to determine mRNA levels between tumour and control samples. For tumour types that didn't contain the corresponding normal tissue, we use GEPIA data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as a supplement. The gene abundance was also complemented by data from the GTEX and TCGA databases. Different protein levels in the CPTAC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://proteomics.cancer.gov/programs/cptac\u003c/span\u003e\u003cspan address=\"https://proteomics.cancer.gov/programs/cptac\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were analysed with the UALCAN website (\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). CACUL1 expression in BRCA subtypes was obtained using the TISDIB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cis.hku.hk/TISIDB/index.php\u003c/span\u003e\u003cspan address=\"http://cis.hku.hk/TISIDB/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Using the GEPIA2 stageplot module (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we also investigated the relationship of CACUL1 levels with clinical stage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2. Analysis of Survival Prognosis\u003c/h2\u003e \u003cp\u003ePrognostic data for CACUL1 in different tumour types, including OS and DFS, were analysed using the GEPIA2 subunit's \"survival map\" module[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3. Analysis of Genetic Alterations\u003c/h2\u003e \u003cp\u003eThe cBioPortal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbioportal.org\u003c/span\u003e\u003cspan address=\"http://www.cbioportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to evaluate genetic alterations[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. 3D structure of CACUL1 as predicted according to the website of alphafold (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://alphafold.ebi.ac.uk/\u003c/span\u003e\u003cspan address=\"https://alphafold.ebi.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition, CACUL1 SNV and CNV percentages are provided by GSCA[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://guolab.wchscu.cn/GSCA/#/\u003c/span\u003e\u003cspan address=\"https://guolab.wchscu.cn/GSCA/#/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4. Analysis of Immune Infiltration\u003c/h2\u003e \u003cp\u003eThe association between CACUL1 levels and immune infiltration were collected with the TIMER2. The \"Immunity Score\" and \"Stroma Score\" integrated can be used to relate CACUL1 expression to immune infiltration. Using TISIDB dataset, we investigated the relationship between immunosuppressive and immunostimulatory factors and CACUL1 using the \"Immunomodulators\" module; and we assessed the correlation between chemokines and receptors and CACUL1 levels using the \"Chemokines\" module. The \"Pan-Cancer\" module is a component of ACLBI tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.aclbi.com\u003c/span\u003e\u003cspan address=\"https://www.aclbi.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which analysed the relationship between the target genes and TMB and MSI. Furthermore, the \"Immunity\" part of the tool was used to assess CACUL1 expression with immunosuppressive factors and ICB response. We also analysed HCC using the \"Immunity\" module of the ACLBI tool. The TIDE score was taken to assess tumour immuno-escape.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e5. Single-cell level Analysis\u003c/h2\u003e \u003cp\u003eTISCH database was used for single-cell analysis. The \"gene\" module was selected for pan-tumor cell \"malignance\" component analysis of CACUL1, and then the \"major-lineage\" component analysis was conducted for LIHC, CHOL, and BRCA. Finally, the distribution of CACUL1 cells in a single dataset was explored[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e6. Enrichment Analysis and Drug Sensitivity\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe GSE36376 dataset from LIHC was retrieved and differential genes were then chosen (fold change\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The 467 differential genes were analysed for KM (OS, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), leaving 259 genes that were subsequently correlated with CACUL1, with the final result suggesting 239 candidates. Using the DAVID database, GO and KEGG analysis was performed on 239 differential genes. Heatmap provided by an online platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.com.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for analysing and visualising data (last accessed on 10 Nov 2023). In addition, the genes corresponding to the first 100 proteins that bind to CACUL1 were selected based on a search of the String website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for KEGG analysis again. We searched for the target gene CACUL1 on GeneMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genemania.org/\u003c/span\u003e\u003cspan address=\"http://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and selected \"Homo sapiens\" to hits in order to obtain the network interactions map of CACUL1. CTRP drug sensitivity analyses were performed on CACUL1 using the GSCA website. On the basis of the GDSC transcriptomic database[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the relationship between different levels of gene expression and the IC50 of sorafenib has been analysed using the \"IC50 module\" of the ACLBI tool and the significance has been tested using the Kruskal-Wallis test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e7. Pathway correlated Analysis of CACUL1\u003c/h2\u003e \u003cp\u003eCorrelation analysis and various pathway scores[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] was performed using the \"Pathway Correlation\" subunit of the ACLBI tool, which is analysed in the background by The GSVA package of the R software (R version 4.0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) to get the link from CACUL1 to pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e8. Immunohistochemistry of CACUL1\u003c/h2\u003e \u003cp\u003eCACUL1 expression was demonstrated by immunohistochemical staining. C10orf46 antibody (Ab 190799) was acquired from Abcam company, and tissue chip containing multiple tumour tissue samples was purchased from Shanghai OutDo Biotech Company. The slide was incubated overnight with a 1:200 dilution of CACUL1 antibody.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e9. Statistical Analysis\u003c/h2\u003e \u003cp\u003eDifferent statistical methods were used depending on the nature of the data. Student's t-test is commonly applied to compare pairs of normatively distributed datasets with homogeneous variances, ANOVA is applied for comparing groups greater than or equal to three, followed up tests by Dunnett's post-hoc. However, if any of the above conditions were not met, the data were not normally distributed or did not satisfy homogenous variances, for variance, the Mann-Whitney test, followed by Tamhane's T2 test, was used to compare between two groups, and the Kruskal-Wallis test, followed by Dunnett's T3 test, to compare between three or more groups. R (version 4.0.3) was used for data analysis. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e1. Expression Information of CACUL1\u003c/h2\u003e\n\u003cp\u003eAlthough CACUL1 protein expression was not found in HPA database[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e], Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea shows CACUL1 mRNA levels in different normal tissues, suggesting that CACUL1 is not highly tissue specific. Using TIMER, we assessed the expression characteristics of CACUL1 in many tumour types. CACUL1 expression showed higher levels in some types of tumours such as CHOL, ESCA, KIRC, HNSC, STAD, LUAD, and LIHC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). If matched normal tissue was not\u0026nbsp;available in the TIMER database, the GEPIA database was used as an adjunct. CACUL1 mRNA levels were also found to be clearly higher in CHOL, PAAD, TGCT and UCS (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec). We chose to supplement the data from GTEX for non-tumour tissue samples that are missing from the TCGA database. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ed shows that CACUL1 was overexpressed in most cancers, including BRCA, GI (CHOL, COAD, LIHC, PAAD, READ, STAD), Kidney (KIRC, KIRP), Reproductive (OV, PRAD) and other types of cancer.\u003c/p\u003e\n\u003cp\u003eWe then analysed CACUL1 protein levels in tumours with the help of the CPTAC dataset. CACUL1 protein levels vary in different tumours (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ee) and its expression varies in different subtypes of BRCA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ef). GEPIA2 analysis showed a clear relationship among expression levels and clinical stage in KIRC (p\u0026thinsp;=\u0026thinsp;0.00407) and TGCT (p\u0026thinsp;=\u0026thinsp;0.00656) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eg).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e2. Prognostic Features of CACUL1\u003c/h2\u003e\n\u003cp\u003eThe \"CANCER \u0026amp; CELL LINES\" module of HPA database suggests that CACUL1 is a marker of poor prognosis in hepatocellular carcinoma (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). To analyse the association between CACUL1 alterations and clinical outcome, different cancer types were divided into two subgroups according to the level of CACUL1 expression. OS results indicated that increased CACUL1 expression was linked to worse outcome in ACC, KIRC, LGG, LIHC, LUSC and PAAD (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). Using DFS as an outcome measure, it is suggested that higher CACUL1 expression is linked\u0026nbsp;to worse outcome in the following types: ACC, BRCA, KIRC, LIHC, PAAD and UCEC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec). COX regression analyses and forest plots were performed on CACUL1, which was identified as being with worse outcome in LGG, LIHC and PAAD (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). Analysis of 371 HCC cases in the TCGA using univariate-Cox regression suggests that CACUL1 is associated with poor survival; univariate-Cox regression and multifactorial-Cox regression suggest that CACUL1 may be an independent predictor of HCC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e3. The Genetic Alteration of CACUL1\u003c/h2\u003e\n\u003cp\u003eUsing the cBioPortal website to analyse the genetic alterations in different tumours[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e], the majority of gene alterations were found in UCEC, which was mainly characterised by 'mutations'. However, for pheochromocytomas and paragangliomas, the \"amplified\" type of CNA was the most common (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). There is a summary of the mutation types of CACUL1, including structural variants, mutations, and copy number variants, as well as their distribution within different types of cancers (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec shows the types, loci and case numbers of CACUL1 mutations. The predicted 3D protein structure of CACUL1 based on the Alphafold (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://alphafold.ebi.ac.uk/\u003c/span\u003e\u003c/span\u003e) is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eMoreover, data from GSCA suggested that CACUL1 has different percentages of SNV and CNV (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ee, f). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eg demonstrated the relationship between CNV and prognosis in different tumours.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003e4. Analysis of CACUL1 and Immune-related Data\u003c/h2\u003e\n\u003cp\u003eUsing various algorithms such as EPIC, MCPCOUNTER, XCELL and TIDE[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e], we found statistically positive correlations between estimated CAFs infiltration values and CACUL1 expression in LIHC, LUAD, LUSC, PAAD and THYM tumours (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb shows the link from CACUL1 to immune system infiltration using immune scores, microenvironment scores and stromal scores.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec clearly shows that CACUL1 expression was tightly linked to immuno-cell infiltration, especially LIHC. This may indicate that CACUL1 might be a new target for this immune-infiltrated malignancy. Although CTLs are one of the most powerful cellular components in the host for direct tumour cell killing and defence against foreign factors[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], the negative correlation between CACUL1 and CD8\u0026thinsp;+\u0026thinsp;T-cell expression in THYM suggests that CD8\u0026thinsp;+\u0026thinsp;T-cell-mediated tumour killing may be weaker in hosts with high CACUL1 expression. TISDIB brings together multiple data types to predict tumour-immunity correlations based on gene expression and is an interoperability portal database for tumours and immune infiltrates. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ed, e, f, g shows the correlation analysis of CACUL1 with Immunoinhibitor, immunostimulators, chemokines, and receptors from TISDIB.\u003c/p\u003e\n\u003cp\u003eTMB and MSI/dMMR are recently available biological markers for predicting response to immunotherapy[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. CACUL1 expression has a positive connection with TMB in the following\u0026nbsp;cancers: ACC, LAML, THYM, SKCM and PAAD. And MSI has a positive connection in GBM, READ, STAD, COAD and LAML and a negative connection in DLBC and UCS (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eh、4i).\u003c/p\u003e\n\u003cp\u003eIn recent years, there has been a trend in cancer therapy research towards the use of specific antibodies that recognise and attach to immune checkpoint molecules in order to stimulate immune activity[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. Analysis of the HCC data showed that CACUL1 expression was a positive co-regulator of a number of immune checkpoint molecules, which may indicate that CACUL1 is responsible for immunosuppression.\u003c/p\u003e\n\u003cp\u003eGiven that CACUL1 is positively associated with checkpoints, we next analysed an association of ICIs and CACUL1 in HCC. Higher TIDE scores, higher risk of immune escape and worse response to immunotherapy were observed with CACUL1 overexpression (G2)[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ek). These are a promising target point for inhibition or reversal of the immunosuppressive microenvironment created.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003e5. Single-cell level analysis of CACUL1\u003c/h2\u003e\n\u003cp\u003eTo examine the correlation among the tumour microenvironment cell types and CACUL1, we used the TISCH database [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. First, a \"malignancy\" analysis was performed using the \"Gene\" module, including LIHC, CHOL, BRCA, and PAAD. The results showed that CACUL1 was mainly \u003cstrong\u003eFig.\u0026nbsp;5 Single cell analysis of CACUL1 expression using Tisch database\u0026nbsp;\u003c/strong\u003edistributed in immune cells and malignant cells (Fig.\u0026nbsp;5a). Further \" major-lineage\" analysis of LIHC, CHOL, and BRCA suggested that CACUL1 showed high enrichment in monocytes/macrophages (Fig.\u0026nbsp;5b, c, d, e). The \"Dataset\" module was used to up-regulate CACUL1. In the GSE140228 data set of LIHC, it was found that CACUL1 was mainly distributed in immune cells, and further analysis showed that it was more densely distributed in macrophages[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e] (Fig.\u0026nbsp;5f).\u003c/p\u003e\n\u003cp\u003eThe correlation between CACUL1 and molecular markers (CD163\u0026thinsp;+\u0026thinsp;CD68)[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] on the surface of M2 macrophages was subsequently verified in liver cancer (Fig.\u0026nbsp;5g). Consistent with single-cell analysis, CACUL1 was co-expressed with M2 macrophage surface markers. We further validated the GSE-28490 dataset from the GEO database. The peripheral blood of healthy people was divided into 6 immune cell lines for data analysis, in which the G1 group represented the monocyte group and the G2 group represented other cell types. We found that CACUL1 expression was markedly enriched in monocyte lines compared to other cells (Fig.\u0026nbsp;5h).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003e6.Enrichment and drug sensitivity analyses\u003c/h2\u003e\n\u003cp\u003eGene enrichment is critical to our understanding of the molecular mechanisms underlying cancer genes. We first picked out the differential genes. The GSE36376 dataset of HCC showed 196 upregulation and 271 downregulation genes compared to normal tissue. These 467 genes were \u003cstrong\u003eFig.\u0026nbsp;6 Functional annotation and drug sensitivity analysis\u0026nbsp;\u003c/strong\u003ethen analyzed by KM survival analysis, which identified 259 genes associated with prognosis. Then, the correlation between 259 genes and CACUL1 was analyzed, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. Finally, 239 differential genes were screened for GO and KEGG enrichment analysis. GO analysis revealed that these differential genes were grouped into 103 BP terms, 67 CC terms and 62 MF terms (Fig.\u0026nbsp;6a).The result of KEGG enrichment analysis comprised 27 paths and was significantly enriched in metabolic paths, including glucose, retinol, fatty acids, amino acids and drug metabolism (Fig.\u0026nbsp;6b).In addition, KEGG pathway analysis of the top 100 CACUL1 interacting proteins was performed on String and included \" the cancer pathway\", suggesting that CACUL1 plays a role in malignancy (Fig.\u0026nbsp;6c-d).\u003c/p\u003e\n\u003cp\u003eGeneMANIA is a comprehensive site that brings together a large collection of genomics and proteomics used to predict the function of target genes[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. A search for CACUL1 using GeneMANIA yielded genes with relevance in physical interactions, co-expression, co-localisation, genetic interactions, enrichment pathways, etc. (Fig.\u0026nbsp;6e).\u003c/p\u003e\n\u003cp\u003eFigure 6f shows the relationship of CACUL1 expression to the susceptibility to CTRPs (data from GSCA). The sensitivity of sorafenib in HCC was also investigated, and CACUL1 was positively correlated with the IC50 of sorafenib (Fig.\u0026nbsp;6g).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003e7. Pathway correlated Analyses of CACUL1 in HCC\u003c/h2\u003e\n\u003cp\u003eGene pathway studies have also been used in the research of the possible effects of CACUL1 in HCC.The enrichment of target genes in each pathway is analysed using the \"correlative analysis\" module of the ACLBI tool[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Gene pathway mapping revealed that the pathways associated \u003cstrong\u003eFig.\u0026nbsp;8 Immunohistochemical staining of CACUL1\u0026nbsp;\u003c/strong\u003ewith CACUL1 were angiogenesis, cellular response to hypoxia, DNA replication, EMT markers, TGF-\u0026beta;, tumour proliferation signature, P53 pathway, ether lipid metabolism and fatty acid degradation (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea-i).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003e8. Expression of CACUL1 in Some Tumors\u003c/h2\u003e\n\u003cp\u003eAlthough HPA predicts that CACUL1 is expressed in the nucleus[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e], corresponding immunohistochemical staining maps are lacking. Based on differences in CACUL1 mRNA expression between tumours and controls, tumour types for IHC were identified. Scans showed that the stain of CACUL1 in THCA、BRCA、LUAD、LUSC、ESCA、LIHC、CHOL、STAD、COAD and KIRC (Fig.\u0026nbsp;8a-j) was more intense than that of the adjacent normal tissue. We show the staining results at 50 and 100 magnification.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCurrently, malignant tumours remain one of the world's leading killers and are the leading public health threat[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, to promote a good prognosis for patients with advanced tumors, it is very meaningful to understand the various stages of tumor occurrence and metastasis and important target molecules[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Precision medicine is currently facing a thorny problem in the tumor industry[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], targeted and immunotherapies are key treatment for patients.\u003c/p\u003e \u003cp\u003eCACUL1's research is mainly focused on prostate cancer, gastric cancer and Alzheimer's disease[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and its role in other diseases is not clear. This study was found that CACUL1 represents a poor prognosis for many tumours. We performed immunohistochemical staining of several tumour types, which coincided with bioinformatic results of CACUL1 expression.\u003c/p\u003e \u003cp\u003eMost importantly, we performed a comprehensive and detailed analysis of CACUL1 and tumor immunity, suggesting that CACUL1 is closely related to anti-tumor immunity. The above immune-correlation analysis of CACUL1 also suggests that CACUL1 is associated with the immune environment and shows a good response to immunotherapy. Decoding the signature code of individual cells in the tumor microenvironment is extremely important. We performed single-cell analysis using the TISCH database and found that CACUL1 is expressed on immune cells of different tumor types, especially monocytes/macrophages.\u003c/p\u003e \u003cp\u003eAs the commonest defence cells in the surrounding tumour, TAMs exist in different phenotypes[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e],and lipid droplets are considered to be an important factor in promoting the polarization of macrophages towards M2[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe correlation analysis between CACUL1 and M2 surface markers was consistent, suggesting the potential contribution of CACUL1 to tumor immunosuppression. Gene enrichment analyses suggest that CACUL1 is functionally focused on the \"Metabolic pathways\", \"Fatty acid degradation\" and \u0026ldquo;Pathways in cancer\u0026rdquo;. Similar results were shown for pathway correlation. Disturbed lipid metabolism is known as an important factor in inducing macrophage polarisation in the tumour microenvironment[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Combined with the distribution of CACUL1 in mononuclear/macrophages and its poor prognosis, we hypothesized that CACUL1 is related to the M2 phenotype of macrophages, which further experiments will have to verify.\u003c/p\u003e \u003cp\u003eWe still need to explore CACUL1 at the cellular level, hoping to provide a ray of light for targeted and immunotherapy of advanced cancer patients.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThese results indicate that CACUL1 is tightly linked to tumour, in particular tumour immunity, as well as being a prognostic indicator of clinical prognosis and immune invasion. And we speculate that CACUL1 may inhibit anti-tumor immune responses and is more likely to be associated with macrophages. However, our study has its own inherent limitations. All data analyses are based on online databases, so further experiments are needed to complete the interpretation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus;TIMER2: the Tumor Immune Estimation Resource database; GEPIA: the Gene Expression Profiling Interactive Analysis database; GTEx: Genotype-Tissue Expression; CPTAC: Clinical Proteomic Tumor Analysis Consortium; TISH: Tumor Immune Single-cell Hub; TISDIB: an integrated repository portal for tumor-immune system interactions; GSCA: Gene Set Cancer Analysis; CTRP: The Cancer Therapeutics Response Portal; ACLBI: assistant for clinical bioinformatics; HPA the Human Protein Atlas database; GO: Gene Ontology; BP: Biological Process\u003c/p\u003e\n\u003cp\u003eMF: Molecular Function; CC: Cell Component; KEGG: Kyoto Encyclopedia of Genes and Genomes; IHC: immunohistochemistry\u003c/p\u003e\n\u003cp\u003eACC: Adrenocortical carcinoma; BLCA: Bladder urothelial carcinoma; BRCA: Breast invasive carcinoma; CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL: Cholangiocarcinoma; COAD: Colon adenocarcinoma; DLBC: Lymphoid neoplasm diffuse large B-cell lymphoma; ESCA: Oesophageal carcinoma; GBM: Glioblastoma multiforme; HNSC: Head and neck squamous cell carcinoma; HCC: Hepatocellular carcinoma; KICH: Kidney chromophobe; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LAML: Acute myeloid leukaemia; 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; SKCM: Skin cutaneous melanoma; STAD: Stomach adenocarcinoma; STES: Stomach and oesophageal carcinoma; TGCT: Testicular germ cell tumours; THCA: Thyroid carcinoma; THYM: Thymoma; UCEC: Uterine corpus endometrial carcinoma; UCS: Uterine carcinosarcoma; UVM: Uveal melanoma;\u003c/p\u003e\n\u003cp\u003eTIICs: Tumor infiltrating Leukocytes; OS: Overall Survival; DFS: Disease Free Surviva; ICB: Immune checkpoint blockade; CNA: Copy Number alterations; CNV: Copy Number variation; SNV: Stable Nuclear variation; CAFs: Cancer-Associated Fibroblasts; CTLs: Cytotoxic T Lymphocyte; TMB: Tumour mutation burden; MSI/dMMR: microsatellite instability/mismatch repair deficiency; ICIs: Immune Checkpoint Inhibitors; TME: Tumour Microenvironment;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.T. and J.W. designed and supervised the study; Y.T. analysed the data and wrote the first draft of the paper; Y.K. supervised and reviewed the paper. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003eYing Kong*: Corresponding author. Department of Oncology, The First Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University, Xi\u0026rsquo;an, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterest conflicts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors involved declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of Data Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were obtained from publicly available online databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank to all the participants at all stages of the process. Thanks for mapping support to Shanghai Xinnuo Biotechnology Co., Ltd. (https://www.bioinformatics.com.cn, last visited on 2023.11.10).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no ethical statement to be declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Natural Science Basic Research Plan in Shaanxi Province of China (2020JM-369) and Beijing Science and Technology Medical Development Foundation (KC2021-JX-0186-88).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAscierto ML, McMiller TL, Berger AE, Danilova L, Anders RA, Netto GJ, Xu H, Pritchard TS, Fan J, Cheadle C, Cope L, Drake CG, Pardoll DM, Taube JM, Topalian SL (2016) The Intratumoral Balance between Metabolic and Immunologic Gene Expression Is Associated with Anti-PD-1 Response in Patients with Renal Cell Carcinoma. 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Mol Immunol 94:27\u0026ndash;35\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":"bioinformatics, CACUL1, tumour-associated immunity, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-4015982/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4015982/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e CDK2 associated cullin domain 1 (CACUL1), also named C10ORF46, is a poorly understood gene. Growing evidence illustrates that CACUL1 plays a potential role in malignant tumors. However, the prognostic value of CACUL1 in malignant tumors didn’t significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e In this study, HPA, TCGA, GEO, TIMER2, GEPIA, GTEx, CPTAC, TISCH, and a variety of other bioinformatics tools were used. The expression was verified by immunohistochemistry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e CACUL1 was markedly overexpressed in tumours and correlated with poor prognosis. It will be a potiental biomarker for predicting HCC prognosis. The evidence of a variety of genetic and epigenetic signatures of CACUL1 in different types of cancer has been studied, and some of the results are also in relation to prognosis. Additionally, CACUL1 is associated with the expression of currently recognised immune checkpoints or infiltrates. Further analysis of CACUL1 and tumour-associated immune cells revealed a link between CACUL1 and macrophages in multiple tumour types. The promotion of poor prognosis by CACUL1 may be associated with a tumor-promoting phenotype of macrophages. Functional prediction of CACUL1 has focused on the molecular pathways of metabolism and the pathways in cancer. It is suggested that metabolic pathways may be the mechanism by which CACUL1 exerts its function to affect macrophage polarisation and thus promote poor prognosis. Finally, immunohistochemistry staining demonstrated that CACUL1 expression is markedly higher in tumour tissues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This first pan-cancer study of CACUL1 suggests a carcinogenic function in multiple tumors, and its closeness to immune cells hints at its potential application in anti-tumor immunotherapy.\u003c/p\u003e","manuscriptTitle":"Unlocking hidden potential: The Prognostic Value and Immunoinfiltration of CACUL1 in Malignant Tumours","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-07 04:34:24","doi":"10.21203/rs.3.rs-4015982/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":"3d65c8bf-9e92-4ec2-a1c2-feea9ec01b0f","owner":[],"postedDate":"March 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-07T19:35:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-07 04:34:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4015982","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4015982","identity":"rs-4015982","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}