RAD51AP1 as an immune-related prognostic biomarker and therapeutic response predictor in hepatocellular carcinoma

RAD51AP1 as an immune-related prognostic biomarker and therapeutic response predictor in hepatocellular carcinoma | 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 RAD51AP1 as an immune-related prognostic biomarker and therapeutic response predictor in hepatocellular carcinoma Chenlu Lan, Yongguang Wei, Xiangkun Wang, Xin Zhou, Xiwen Liao, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2638542/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 RAD51 associated protein 1 ( RAD51AP1 ) has been showed that regulated cell proliferation and cancer progression. However, the immune infiltrating correlation and therapeutics guidance of RAD51AP1 in hepatocellular carcinoma (HCC) still need further investigation. In this study, differential expression, clinicopathologic correlation, prognostic value, and function enrichment analysis of RAD51AP1 were performed in TCGA, GSE14520, GSE76427 and ICGC datasets and were validated using Guangxi cohort. We explored the predictive value of RAD51AP1 to therapeutics response comprehensively and probed the correlation between RAD51AP1 and HCC immunoinfiltration by CIBERSORT and ssGSEA. RAD51AP1 with a high diagnostic accuracy was significantly overexpressed in HCC tissues. The shorter survival time and poorer clinical features were showed when RAD51AP1 upregulated. A nomogram featuring RAD51AP1 and clinicopathologic factors was established to predict OS of HCCs. RAD51AP1 might be engaged in the carcinogenic and celluar cycle processes. In CIBERSORT analysis, higher T cells follicular helper but lower T cells CD4 + memory resting infiltrations were exhibited when RAD51AP1 upregulated. T demonstrated that High- RAD51AP1 expression subgroup had higher macrophages, Th2 and Treg cells infiltration, but lower type Ⅱ IFN response function in ssGSEA analysis, exhibited the upregulated immune-related checkpoint expression levels, lower IPS and TIDE scores, suggesting a better immunotherapy response, and may be more susceptible to Bexarotene, Doxorubicin, Gemcitabine and Tipifarnib. Taken together, RAD51AP1 mediating the immunosuppressive microenvironment is a potential diagnostic and prognostic biomarker and could be underlying HCC treatment strategy. Hepatocellular carcinoma RAD51AP1 Prognostic signature Bioinformatics Immune filtration Drug sensitivity 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 Liver cancer is the sixth most widespread cancer and the fourth primary cause of cancer-related death [ 1 ] . Its incidence and mortality may keep rising by 2030 [ 2 ] . Hepatocellular carcinoma (HCC) with a dismal prognosis is the most prominent type of primary liver cancer [ 3 ] . The important risk factors of HCC include chronic B or C viral hepatitis, heavy alcohol consumption, metabolic associated fatty liver disease, and aflatoxins [ 4 ] . Serum alpha fetoprotein (AFP) is a traditional index of diagnosis and follow-up. But the diagnosis sensitivity of AFP is only 40–60% [ 5 ] . For HCC treatment, development of systemic therapies of HCC has quickly accelerated [ 6 ] . Nevertheless, the treatment response for advanced HCC is still not optimistic due to tumor heterogeneity and drug resistance. Exploring and identifying new molecular biomarkers with satisfactory diagnostic and prognostic value and make sense to provide therapy target and improve the clinical outcome of HCC patients. The human RAD51AP1 gene, located on chromosomes 12p13.1 to 13.2 [ 7 ] , was identified in 1997 [ 8 ] . The DNA damage response plays an anti-cancer role in early human tumorigenesis, and mutations in DNA repair pathways may increase genomic instability and tumor progression [ 9 ] . RAD51AP1 functions in DNA homologous recombination repair by stimulating RAD51 activity [ 10 ] . Studies have shown that up-regulation of RAD51AP1 is related to poor cancer prognosis compared with down-regulation in ovarian cancer [ 11 ] and lung cancer [ 12 ] . Down-regulation of RAD51AP1 was shown to suppress the metastasis and proliferation of lung carcinoma cells [ 13 ] and retard the growth of intrahepatic cholangiocarcinoma cells [ 14 ] . RAD51AP1 has been reported to be overexpressed in HCC [ 15 ] , Zhuang et al (2020) demonstrated that HCC patients with overexpressed RAD51AP1 had the poor clinical features and dismal prognosis [ 16 ] . However, the molecular mechanism and detailed clinical significance of RAD51AP1 in HCC have not been comprehensive investigated, especially its immunoinfiltration correlation and therapeutics response guidance. In this project, we authenticated the diagnostic and prognostic value of RAD51AP1 to decipher its molecular mechanism and preliminarily discussed its impact on HCC immune infiltration and therapeutic sensitivity. 2. Materials And Methods 2.1 Data Source and HCC Samples Collection The mRNA expression arrays and corresponding clinicopathologic info of HCC patients were gained from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/repository ). The GSE14520, GSE76427 and ICGC datasets, including gene expression profiling and survival info of HCC patients, were retrieved from Gene Expression Omnibus (GEO) ( https://www.ncbi.nlm.nih.gov/geo/ ) and International Cancer Genome Consortium (ICGC; http://dcc.icgc.org ) databases. Besides, a total of 50 pairs HCC and corresponding adjacent non-cancerous tissue samples from First Affiliated Hospital of Guangxi Medical University were served as the Guangxi cohort. Then, the corresponding clinical info, including age, gender, serum AFP, tumor size, histologic grade, vascular invasion, China Liver Cancer Staging (CNLC), and Barcelona Clinic Liver Cancer (BCLC) staging were also collected. 2.2 Differentia expression and prognostics value analysis The differential RAD51AP1 expression analyses between HCC and non-cancerous liver tissues were carried out in the TCGA, GSE14520, GSE76427, ICGC and Guangxi cohorts. The diagnostic ability of RAD51AP1 was evaluated through plotting receiver operating characteristic (ROC) curves. The median expression of RAD51AP1 was used to categorize the subgroups as high- and low-expression. The prognostic value of RAD51AP1 was explored preliminarily by Kaplan-Meier survival analysis in TCGA-HCC cohort and was validated in the GSE14520 and ICGC cohorts. Using the “ggpubr” R package, the difference of RAD51AP1 expression level between different clinicopathological characteristics was investigated in the TCGA and Guangxi cohorts. 2.3 Nomogram Construction Univariate and multivariate Cox regression analyses were performed in TCGA cohort to analyze prognostic factors of HCC. Furthermore, nomogram is an effective approach to measure the specific risk by integrating multiple variables. Utilizing the “survival” and “rms” R package, a nomogram combining RAD51AP1 expression and easily accessible and widely accepted clinicopathological parameters (gender, age, BMI, AFP, histologic grade, TNM stage, and vascular invasion) was established based upon the TCGA-HCC data to determine the likelihood of 1-, 3-, and 5-year over survival (OS) for HCC patients. The analysis removed the patients with incomplete clinical data and follow-up periods of fewer than 30 days. The bootstrap method was applied to calculate concordance index (C-index) with 1000 resamples. The discrimination performance of nomogram was examined based on the consistency degree of calibration curves. 2.4 Functional enrichment analysis Gene Set Enrichment Analysis (GSEA) software program (v4.1.0) was employed to seek the putative regulatory mechanisms of high- and low- RAD51AP1 expression subgroups in virtue of the gene set data “c2.all.v7.0.symbols.gmt” and “c5.all.v7.0.symbols.gmt” [ 17 ] . The number of permutations was set at 1,000. Functional terms that met the criteria of a nominal P < 0.05 and a false discovery rate (FDR) < 0.05 were regarded as significant enrichment pathways. Additionally, based on the entire mRNA expression profile of the TCGA-HCC dataset, the genome-wide correlation analysis of RAD51AP1 was performed. Genes with correlation coefficient > 0.7 and P < 0.05 were incorporated to implement the gene ontology terms (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses utilizing the “clusterProfiler” R package. 2.5 Immune Cell Infiltration Analysis Based on a novel analytical methodology, namely cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), and TCGA-HCC expression profiling, the proportion of various immune cell in each sample were assessed. Then, the immune infiltration levels in different RAD51AP1 expression subgroups were uncovered. The correlation analyses of RAD51AP1 and multiple immune cells was also conducted. Tumor Immune Estimation Resource 2 (TIMER2) ( http://timer.cistrome.org/ ), a data repository for measuring immune cell infiltrations of distinct cancers. Spearman correlation analyses in TIMER2 were performed between RAD51AP1 and immune cell subsets to validate the results of CIBERSORT analysis. To further illustrate the relationship between RAD51AP1 expression and cancer immunity, the immune-associated cell infiltration levels and function pathways of the different RAD51AP1 expression subgroups analyzed in the TCGA and ICGC datasets using the "gsva" R package with an algorithm called single-sample Gene Set Enrichment Analysis (ssGSEA). Additionally, the stromal score, immune score and ESTIMATE score of each TCGA-HCC samples were calculated using the “estimate” R package and the relationships between RAD51AP1 expression and these scores were also excavated. 2.6 Drug sensitivity prediction analysis The association between RAD51AP1 expression and tumor mutation burden (TMB) and established immune checkpoint gene were explored using the Spearman correlation analysis in the TCGA-HCC cohort. The R package “pRRophetic” was applied to determine the half-maximal inhibitory concentration (IC50) of HCC treatment related chemotherapeutic and immunotherapeutic drugs by ridge regression method [ 18 ] . Additionally, a computationally based online program called Tumor Immune Dysfunction and Exclusion (TIDE, http://tide.dfci.harvard.edu/ ) stimulates the immune system of human body to further forecast the response to immune checkpoint inhibitor. The calculated score of TIDE, immune dysfunction, immune exclusion, and microsatellite instability (MSI) of each TCGA-HCC sample were acquired from TIDE website. In addition, is a dataset containing TCGA data of 20 solid cancers and more than 8,000 tumor samples. The immunophenotypic score (IPS) of tumor sample can be obtained from the Cancer Immunome Atlas (TCIA, https://tcia.at/ ) and contribute to estimate the therapeutic effect of immune-checkpoint inhibitors (ICIs), including anti-programmed cell death protein 1 (PD-1)and anti- cytotoxic T lymphocyte antigen-4 (CTLA-4) antibodies [ 19 ] . These immunotherapy-related indicators, including IC50, TIDE and IPS were compared between the different RAD51AP1 expression subgroups in the TCGA-HCC cohort. Besides that, IMvigor210 dataset, a sizable immunotherapy dataset containing the information of 298 urothelial carcinoma patients at advanced stage who were administered with anti-PD1 inhibitor, was used to grope the immunotherapy response predictability of RAD51AP1 in other cancer. 2.7 Reverse transcription quantitative PCR The method of specimen preservation, RNA extraction, and reverse transcription quantitative PCR were conducted as previously described [ 20 ] . Clinical sample collecting and handling procedures strictly adhered the standard protocol. The sequence of primers was as follows: GAPDH, forward: GTCAGCCGCATCTTCTTT, reverse: CGCCCAATACGACCAAAT. RAD51AP1 , forward: AGTGAAGGTAAAATCCCCAGTAGA, reverse: TGGCAAGGACTGAGATTCTGAT. Using the 2 ∆∆CT approach, the relative mRNA expressions of RAD51AP1 were measured [ 21 ] . 2.8 Statistical analysiss R (v4.1.1) was applied to complete data analysis and result visualization. The Wilcoxon rank-sum test was conducted between the different subgroups with continuous data, while the chi-square test was used for categorical data. Paired t-test was utilized to explore the discrepancy of RAD51AP1 expression levels between HCC samples and adjacent normal tissues of Guangxi cohort. The association of two variables was probed using the Spearman correlation analysis. Unless otherwise indicated, P < 0.05 was provided as the threshold cutoff value. 3. Results 3.1 The upregulated expression level of RAD51AP1 in HCC RAD51AP1 gene expression level was markedly over-expressed in HCC compared with normal liver tissues in various cohorts (Figure. 1A-E). The ROC curve indicated that RAD51AP1 had a high predictive accuracy of HCC diagnosis with the area under curve (AUC) > 0.900 in the TCGA, GSE14520 and ICGC cohorts (Figure. 1F). The AUC values of GSE76427 and Guangxi cohort were 0.821 and 0.721 respectively. These results showed that RAD51AP1 had a diagnostic significance for HCC. 3.2 RAD51AP1 correlated with dismal survival and poor clinical features The survival analysis indicated that the high RAD51AP1 -expression group showed the poorer OS and recurrence free survival (RFS) than that of the low expression group in the TCGA cohorts (Figure. 2A, B). Furthermore, the result was confirmed in the GSE14520 and ICGC cohorts (Figure. 2C, D). Moreover, a significantly higher RAD51AP1 gene expression was observed in patients with serum AFP > 400 ng/ml in the TCGA cohorts. Along with the advance of clinicopathologic features believed to get poorer prognosis, including histologic grade, TNM stage, T staging and vascular invasion degree, the expression level of RAD51AP1 presented an upward trend (Figure. 3). We further confirmed that RAD51AP1 was upregulated in patients of Guangxi cohort with AFP > 400 ng/mL, poor histologic grade, large tumor size and more advanced stage (CNLC Ia vs. Ib) (Figure. 4). 3.3 Construction of a nomogram Univariate Cox analysis discovered that RAD51AP1 expression (HR = 1.218; 95% CI = 1.055–1.407; P < 0.05), TNM stage, T staging, and vascular invasion were the high-risk factors for OS in TCGA-HCC patients (Figure. 5A). RAD51AP1 expression (HR = 1.159; 95% CI = 1.002–1.341; P < 0.05) remained statistically significant in multivariate Cox analysis. Therefore, RAD51AP1 could be identified as an independent prognostic factor (Figure. 5B). The established nomogram with the C-index of 0.725 contributed to predict the OS of specific HCC patients (Figure. 5C). The calibration curves exhibited a satisfactory consistency between the estimated and the actual OS rates at 1, 2, and 3 years (Figure. 5D). 3.4 RAD51AP1 was involved in cancer-promoting and cell cycle related pathways GO terms enrichment analysis discovered that up-regulation of RAD51AP1 was significantly related to “ATPASE_ACTIVITY, CELL_CYCLE_G1_S_PHASE_TRANSITION, CHROMOSOME_SEGREGATION, DNA_REPLICATION, NUCLEAR_INNER_MEMBRANE, REGULATION_OF_GENE_EXPRESSION_EPIGENETIC, SIGNAL_TRANSDUCTION_BY_P53_CLASS_MEDIATOR” pathways (Figure. 6A). Furthermore, KEGG pathway analysis highlighted that “REGULATION_OF_AUTOPHAGY, NOTCH_SIGNALING_PATHWAY, P53_SIGNALING_PATHWAY, PATHWAYS_IN_CANCER, ERBB_SIGNALING_PATHWAY” pathways were enriched in the up-regulated RAD51AP1 subgroup, while “COMPLEMENT_AND_COAGULATION_CASCADES, DRUG_METABOLISM_CYTOCHROME_P450, FATTY_ACID_METABOLISM, METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450” pathways were enriched in down-regulated RAD51AP1 subgroup (Figure. 6B). The results of genome-wide correlation analysis showed that 92 genes were relevant to RAD51AP1 with the absolute value of a correlation coefficient ≥ 0.7 in the TCGA-HCC cohort. All these genes were positively correlated with RAD51AP1 expression. (Figure. 7A). The RAD51AP1 -associated HCC hallmarks, including regulation of cell cycle, cell cycle phase transition, nuclear chromosome, tubulin binding, cellular senescence, FoxO signaling pathways, were identified by the GSEA analysis on these correlated genes. 3.5 Correlation between RAD51AP1 and the immune infiltration of tumor microenvironment in HCC CIBERSORT analysis revealed higher T cells follicular helper but lower T cells CD4 + memory resting significantly infiltrated in the high- RAD51AP1 expression subgroup ( P < 0.05; Figure. 8A). In line with the above results, a correlation heatmap displayed the positive and negative connections of RAD51AP1 expression and different immunizing cells (Figure. 8B). In TIMER2, RAD51AP1 expression negatively correlated to T cells CD4 + memory resting, but positively correlated to B cell memory, cell CD4 + T memory activated and Myeloid dendritic cell actived (Supplementary Figure. 1). The ssGSEA analysis showed that the up-regulated RAD51AP1 expression exhibited the higher macrophages, Th2 and Treg cells infiltration levels but lower immune function of type Ⅱ IFN response in both TCGA and ICGC cohort ( P < 0.05; Figure. 9A-D). 3.6 Predictive value of therapeutic efficacy in HCC The up-regulated expression levels of immune-related checkpoint genes, including CD44, HAVCR2, LGALS9, PDCD1, TNFRSF4, TNFRSF14, TNFRSF18, CD27, CD48, CD40, IDO1 and LAG3 , were uncovered in the high- RAD51AP1 expression subgroup (Figure. 10A). Moreover, RAD51AP1 expression was negatively related to stromal score ( R = -0.20, P < 0.001), ESTIMATE score ( R = -0.13, P < 0.05). However, the correlations of RAD51AP1 and immune score or TMB were statistically insignificant (Figure. 10B-C). The findings of specific drug sensitivity analysis showed that eight medications (Sorafenib, Nilotinib, Axitinib, Erlotinib, Dasatinib, Docetaxel, Pazopanib, Pyrimethamine, Rapamycin, Sunitinib and Temsirolimus) displayed higher IC50 in high- RAD51AP1 expression subgroup, whereas Bexarotene, Doxorubicin, Gemcitabine and Tipifarnib had lower IC50, which suggesting that the high-expression subgroup more susceptible to Bexarotene, Doxorubicin, Gemcitabine and Tipifarnib (all P < 0.05, Figure. 11). Violin plots showed the high- RAD51AP1 expression subgroup exhibited relatively lower IPSs, representing a preferable reaction to PD-1 and CTLA-4 inhibitors ( P < 0.05; Figure. 12A-D). The subgroup with higher TIDE score represented non-responders whose suppressive cells inhibit T cell infiltration. We observed that the high- RAD51AP1 expression subgroup had markedly lower TIDE and immune dysfunction scores but higher immune exclusion scores than those of low expression subgroup ( P < 0.001, Figure. 11F-I). Responders in the IMvigor210 had significantly higher RAD51AP1 expression, which demonstrating that RAD51AP1 could reliably predict the therapeutic response of immunotherapy not just for HCC but for other cancer types as well. 4. Discussion Genetic alterations is a hallmark characteristic of hominine solid tumors [ 22 ] and can greatly affect the formation and progression of HCC [ 23 ] . DNA damage can activate DNA damage repair systems, which increased the risk of DNA modifications and genome defection [ 24 ] . Homologous recombination DNA repair (HR), especially operating in double-strand DNA breaks [ 25 ] , is identified as an efficient repair mechanism [ 26 ] . RAD51 recombinase can catalyze HR [ 27 ] and form a nucleoprotein filament to promote HR DNA repair [ 28 ] . RAD51AP1 acts as a necessary protein to activate RAD51 recombinase [ 29 ] . Thus, RAD51AP1 functions to keep genomic integrity via RAD51 recombinase enhancement [ 30 ] . Furthermore, Gonzalez et al showed that RAD51AP1 functions to maintain telomere length and protect against proliferation of alternative lengthening of telomeres in cancer cells [ 31 ] . It is worth mentioning that Wu et al (2019) demonstrated a up-regulated RAD51AP1 in non-small cell lung cancer (NSCLC). In addition, the metastasis, proliferation, invasion, and migration of NSCLC cell line were inhibited under circumstance of RAD51AP1 silencing [ 13 ] . Obama et al (2019) showed that downregulation of RAD51AP1 retarded growth of intrahepatic cholangiocarcinoma cells [ 14 ] . Likewise, RAD51AP1 knockout experiments showed that RAD51AP1 knockout in human breast cancer cells and syngeneic mouse models reduced tumor growth and metastasis by increasing breast cancer stem cell self- renewal [ 32 ] . A mouse model experiment using U87MG cells revealed that knocking-down RAD51AP1 inhibited glioma [ 33 ] . In our study, the upregulated RAD51AP1 expression related to shorter OS and RFS, histologic grade, TNM stage, T staging and vascular invasion degree and higher serum AFP level and was involved in cancer-promoting pathways in HCC, indicating that RAD51AP1 represents increased malignancy and poor prognosis. Immune checkpoint inhibitors (ICIs) and molecularly targeted therapy have achieved remarkable survival benefit to the HCC managements [ 6 , 34 , 35 ] . HCC has a high tumor heterogeneity and different molecule characteristics [ 36 , 37 ] . Tumor associated macrophages (TAMs), contributing to tumor growth and progression, are the essential elements of tumor microenvironment [ 38 ] . RAD51AP1 was demonstrated to regulate colorectal cancer stem cell self-renewal and promote colorectal cancer growth and drug resistance [ 39 ] . We demonstrated that RAD51AP1 may be associated with an immunosuppressive microenvironment with higher TAMs infiltration in HCC. Interestingly, the high RAD51AP1 expression may suggest a better immunotherapy response and could be more susceptible to Bexarotene, Doxorubicin, Gemcitabine and Tipifarnib. Collectively, RAD51AP1 may be a potential molecular therapeutic target. The dominating analyses were bioinformatics research based on the public datasets. Therefore, immunohistochemistry is urgently needed to confirm the RAD51AP1 protein expression level in HCC tissue. The molecular mechanisms of RAD51AP1 for HCC still require further validated research in vitro and in vivo . 5. Conclusions Taken together, RAD51AP1 mediating the immunosuppressive microenvironment is a potential diagnostic and prognostic biomarker and could be underlying therapeutic target for HCC. However, this conclusion still requires further validations form additional cohorts. Declarations Supplementary Materials: The following supporting information can be downloaded at: www.mdpi.com/xxx/s1, Supplementary Figure S1 and Supplementary Table S1. Author Contributions: LCL and WYG analyzed data and wrote the manuscript. ZGZ and PT conceived and designed research and revised the manuscript. WZL, ZX and WXK conducted experiments. HHS, LTM and LXW collected and sorted data. All authors read and approved the manuscript. Funding: This work was supported by the National Natural Science Foundation of China No. 81902500, the Key Laboratory of High-Incidence-Tumor Prevention & Treatment (Guangxi Medical University), Ministry of Education (grant nos. GKE2018-01, GKE2019-11, GKE-ZZ202009 and GKE-ZZ202109), Guangxi Key Laboratory for the Prevention and Control of Viral Hepatitis (No. GXCDCKL201902), Key R&D Plan of Qingxiu District, Nanning (NO.2020056), Natural Science Foundation of Guangxi Province of China (grant no. 2020GXNSFAA159127) and Self-funded Scientific Research Project of Health Commission in Guangxi Zhuang Autonomous Region (Z20210977). Institutional Review Board Statement: This experiment was authorized by the Ethical Review Committee of the First Affiliated Hospital of Guangxi Medical University [Approval Number: 2021 (KY-E-032)]. Informed Consent Statement: Informed consent was agreed and signed by all HCC patients. Data Availability Statement: The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author. Acknowledgments: Firstly, the authors would like to thank TCGA, GEO and ICGC projects for data sharing. 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RAD51AP1 Loss Attenuates Colorectal Cancer Stem Cell Renewal and Sensitizes to Chemotherapy[J]. Mol Cancer Res, 2021, 19(9): 1486-1497. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableS1.docx SupplementaryFigure1.tif Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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-2638542","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":179781016,"identity":"16d22255-84e9-4705-b64b-875e7867c3b5","order_by":0,"name":"Chenlu Lan","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chenlu","middleName":"","lastName":"Lan","suffix":""},{"id":179781017,"identity":"7a814bf8-a9c8-49f2-b166-f6630fcc94b1","order_by":1,"name":"Yongguang Wei","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yongguang","middleName":"","lastName":"Wei","suffix":""},{"id":179781018,"identity":"440dadce-558a-4a72-897c-f1e42f200981","order_by":2,"name":"Xiangkun Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiangkun","middleName":"","lastName":"Wang","suffix":""},{"id":179781019,"identity":"77538d33-f869-4c82-90d9-ae8289b6c884","order_by":3,"name":"Xin Zhou","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhou","suffix":""},{"id":179781020,"identity":"ac0b06ee-d219-4487-8fe2-308c7201f322","order_by":4,"name":"Xiwen Liao","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiwen","middleName":"","lastName":"Liao","suffix":""},{"id":179781021,"identity":"7978ca42-2668-4c21-8d44-197f3c8384b2","order_by":5,"name":"Huangsheng Huang","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huangsheng","middleName":"","lastName":"Huang","suffix":""},{"id":179781022,"identity":"97a8fb2b-f555-4aff-8b16-f29bc0f01003","order_by":6,"name":"Zhongliu Wei","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhongliu","middleName":"","lastName":"Wei","suffix":""},{"id":179781023,"identity":"f50e8c01-56bc-4022-96f1-2685f012c006","order_by":7,"name":"Tianman Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tianman","middleName":"","lastName":"Li","suffix":""},{"id":179781024,"identity":"9ba4c562-0f40-438f-b763-fd27432b2976","order_by":8,"name":"Tao Peng","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Peng","suffix":""},{"id":179781025,"identity":"3f6852c0-09bf-4e4d-a5a8-70ec3d67c7df","order_by":9,"name":"Guangzhi Zhu","email":"data:image/png;base64,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","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Guangzhi","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2023-02-28 12:44:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2638542/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2638542/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":33866773,"identity":"a035f8a1-27f5-4ef2-a9b3-02fc78658d93","added_by":"auto","created_at":"2023-03-07 00:02:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":635363,"visible":true,"origin":"","legend":"\u003cp\u003e(A-E) Scatter plots of \u003cem\u003eRAD51AP1\u003c/em\u003e expression in HCC and normal liver tissues in TCGA, GSE14520, GSE76427, ICGC and Guangxi cohorts. (F) Diagnostic receiver operator curves of \u003cem\u003eRAD51AP1\u003c/em\u003e for HCC in various cohorts.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2638542/v1/92d9b529c2b3e1b45ef0ab8c.png"},{"id":33866094,"identity":"192f06e1-0b5d-4a8a-ab0e-65354b7563c9","added_by":"auto","created_at":"2023-03-06 23:54:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304718,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves with log-rank test between high- and low-\u003cem\u003eRAD51AP1\u003c/em\u003e expression subgroups in TCGA (A, B), GSE14520 (C) and ICGC (D) cohorts.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2638542/v1/81056ad58389cac284938fcc.png"},{"id":33866775,"identity":"657d4931-f928-4a9f-9cea-c94c12fc72f2","added_by":"auto","created_at":"2023-03-07 00:02:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":534996,"visible":true,"origin":"","legend":"\u003cp\u003e(A-H) The relationship between \u003cem\u003eRAD51AP1\u003c/em\u003e expression and the different clinicopathological characteristics in TCGA cohort, including age, gender, BMI, serum AFP level, histologic grade, TNM stage, T staging and vascular invasion degree. BMI, Body Mass Index; AFP, alpha-fetoprotein. TNM, Tumor Node Metastasis; Macro represents macrovascular invasion; Micro represents microvascular invasion.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2638542/v1/c02f5bb9613af3428ad85473.png"},{"id":33866772,"identity":"fe0382b8-e756-45a0-a21f-0e42311f6705","added_by":"auto","created_at":"2023-03-07 00:02:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":355104,"visible":true,"origin":"","legend":"\u003cp\u003e(A-H) The relationship between \u003cem\u003eRAD51AP1\u003c/em\u003e expression and the different clinicopathological characteristics in Guangxi cohort, including age, gender, serum AFP level, portal vein tumor thrombus, tumor size, histologic grade, CNLC and BCLC stage.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2638542/v1/711f290f84829b8dee1283e0.png"},{"id":33866098,"identity":"edd4d338-4268-4ce5-a7ee-0833f297ba18","added_by":"auto","created_at":"2023-03-06 23:54:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":459854,"visible":true,"origin":"","legend":"\u003cp\u003e(A, B) Univariate and multivariate Cox regression analyses of \u003cem\u003eRAD51AP1\u003c/em\u003eand clinicopathologic factors in TCGA datasets. (C, D) A nomogram combining \u003cem\u003eRAD51AP1\u003c/em\u003e and clinical characteristics and corresponding calibration curves.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-2638542/v1/da831070bfdd78c96316023e.png"},{"id":33867717,"identity":"83fd4cc9-f3c4-4688-8aa0-a4ae2b9e99ce","added_by":"auto","created_at":"2023-03-07 00:10:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":478936,"visible":true,"origin":"","legend":"\u003cp\u003e(A, B) The KEGG and GO function enrichment signaling pathways of high- and low-\u003cem\u003eRAD51AP1\u003c/em\u003e expression subgroups in GSEA analysis of TCGA-HCC cohort. KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology; GSEA, Gene Set Enrichment Analysis.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-2638542/v1/dd79b078e09d8e78a7021447.png"},{"id":33866101,"identity":"2f5fb828-df92-4bc6-ac20-c9d52c22e582","added_by":"auto","created_at":"2023-03-06 23:54:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":895338,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Regulatory network of \u003cem\u003eRAD51AP1\u003c/em\u003e and its co-expressed genes in the TCGA-HCC cohort. (B, C) The bubble diagrams displaying the significant GO and KEGG function enrichment signaling pathways of \u003cem\u003eRAD51AP1\u003c/em\u003e and its co-expressed genes. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-2638542/v1/88702968850ba2a978c3cf6d.png"},{"id":33866099,"identity":"2e3bacf5-d00b-4f5c-ade6-a4cb7baf8128","added_by":"auto","created_at":"2023-03-06 23:54:04","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":538169,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The percentages of 22 immune cell infiltration profiles between the high- and low-\u003cem\u003eRAD51AP1\u003c/em\u003e expression groups through CIBERSORT analysis. (B) The correlation heatmap displaying the correlation between \u003cem\u003eRAD51AP1\u003c/em\u003e expression and various immunocyte levels. *P \u0026lt; 0.05; **P \u0026lt; 0.01; and ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-2638542/v1/4dde12c7bbe1316fbbc018e8.png"},{"id":33866103,"identity":"bbdcae96-0752-4a19-8c50-cb67919c87ba","added_by":"auto","created_at":"2023-03-06 23:54:04","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":453198,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of ssGSEA analysis investigated the differences of immune cell infiltration between the high- and low-\u003cem\u003eRAD51AP1\u003c/em\u003e expression groups in TCGA (A, B) and ICGC (C, D) cohorts. *P \u0026lt; 0.05; **P \u0026lt; 0.01; and ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-2638542/v1/976d9aa963ab24429bc873c9.png"},{"id":33866776,"identity":"7659e781-9d5a-4010-8a6a-738a101c3033","added_by":"auto","created_at":"2023-03-07 00:02:04","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":431869,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The heatmap illustrating significantly differential expression immune-related checkpoint genes between the high- and low-\u003cem\u003eRAD51AP1\u003c/em\u003eexpression groups. (B-E) Spearman analysis of \u003cem\u003eRAD51AP1\u003c/em\u003e expression level and TMB (B), stromal score(C), immune score (D) and ESTIMATE score (E). TMB, tumor mutation burden.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-2638542/v1/b74a823d380e28eb72a44aa3.png"},{"id":33866777,"identity":"be857dfa-cfe4-43eb-8b19-f5077c05c080","added_by":"auto","created_at":"2023-03-07 00:02:04","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":497975,"visible":true,"origin":"","legend":"\u003cp\u003eResponse to anti-tumor therapies, including chemotherapeutic and molecular targeted drugs, of HCC patients in the high- and low-\u003cem\u003eRAD51AP1\u003c/em\u003eexpression groups.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-2638542/v1/b0d3952429e9658a76e8f603.png"},{"id":33866105,"identity":"5d76e302-7ba2-41ff-bef8-967e8f862e6c","added_by":"auto","created_at":"2023-03-06 23:54:04","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":793858,"visible":true,"origin":"","legend":"\u003cp\u003e(A-D) Violin plots showed the relationship between IPSs and different \u003cem\u003eRAD51AP1\u003c/em\u003eexpression groups. (E) Differential \u003cem\u003eRAD51AP1\u003c/em\u003eexpression level between non-response and response groups of IMvigor210 dataset. (F-I) Immune dysfunction, immune exclusion, TIDE, and MSI scores in the high- and low-\u003cem\u003eRAD51AP1\u003c/em\u003e expression groups. *P \u0026lt; 0.05; **P \u0026lt; 0.01; and ***P \u0026lt; 0.001. IPS, immunophenotypic score; TIDE, Tumor Immune Dysfunction and Exclusion; MSI, microsatellite instability.\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-2638542/v1/51f88de1f3c8993959d23dc6.png"},{"id":39273331,"identity":"5ff596ae-064a-40f8-9c6f-3226c4af5b12","added_by":"auto","created_at":"2023-06-29 05:14:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3392975,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2638542/v1/e9d4de69-4eaf-4c21-a461-6bbf5140565b.pdf"},{"id":33866093,"identity":"6ac7ae45-2c3c-4b91-a092-200a0c54602c","added_by":"auto","created_at":"2023-03-06 23:54:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18982,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-2638542/v1/02fd835d24550844865d10f5.docx"},{"id":33866778,"identity":"229374bc-ae46-44d5-914d-78d6e7eeb99b","added_by":"auto","created_at":"2023-03-07 00:02:04","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":6337656,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-2638542/v1/3f9e9473331e117499c80423.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"RAD51AP1 as an immune-related prognostic biomarker and therapeutic response predictor in hepatocellular carcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eLiver cancer is the sixth most widespread cancer and the fourth primary cause of cancer-related death \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Its incidence and mortality may keep rising by 2030\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Hepatocellular carcinoma (HCC) with a dismal prognosis is the most prominent type of primary liver cancer \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The important risk factors of HCC include chronic B or C viral hepatitis, heavy alcohol consumption, metabolic associated fatty liver disease, and aflatoxins\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Serum alpha fetoprotein (AFP) is a traditional index of diagnosis and follow-up. But the diagnosis sensitivity of AFP is only 40\u0026ndash;60%\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. For HCC treatment, development of systemic therapies of HCC has quickly accelerated\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, the treatment response for advanced HCC is still not optimistic due to tumor heterogeneity and drug resistance.\u003c/p\u003e \u003cp\u003eExploring and identifying new molecular biomarkers with satisfactory diagnostic and prognostic value and make sense to provide therapy target and improve the clinical outcome of HCC patients. The human \u003cem\u003eRAD51AP1\u003c/em\u003e gene, located on chromosomes 12p13.1 to 13.2\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, was identified in 1997\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The DNA damage response plays an anti-cancer role in early human tumorigenesis, and mutations in DNA repair pathways may increase genomic instability and tumor progression\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eRAD51AP1\u003c/em\u003e functions in DNA homologous recombination repair by stimulating \u003cem\u003eRAD51\u003c/em\u003e activity\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that up-regulation of \u003cem\u003eRAD51AP1\u003c/em\u003e is related to poor cancer prognosis compared with down-regulation in ovarian cancer\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e and lung cancer\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Down-regulation of \u003cem\u003eRAD51AP1\u003c/em\u003e was shown to suppress the metastasis and proliferation of lung carcinoma cells \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e and retard the growth of intrahepatic cholangiocarcinoma cells\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eRAD51AP1\u003c/em\u003e has been reported to be overexpressed in HCC\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, Zhuang et al (2020) demonstrated that HCC patients with overexpressed \u003cem\u003eRAD51AP1\u003c/em\u003e had the poor clinical features and dismal prognosis \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the molecular mechanism and detailed clinical significance of \u003cem\u003eRAD51AP1\u003c/em\u003e in HCC have not been comprehensive investigated, especially its immunoinfiltration correlation and therapeutics response guidance. In this project, we authenticated the diagnostic and prognostic value of \u003cem\u003eRAD51AP1\u003c/em\u003e to decipher its molecular mechanism and preliminarily discussed its impact on HCC immune infiltration and therapeutic sensitivity.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Source and HCC Samples Collection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe mRNA expression arrays and corresponding clinicopathologic info of HCC patients were gained from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/repository\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/repository\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GSE14520, GSE76427 and ICGC datasets, including gene expression profiling and survival info of HCC patients, were retrieved from Gene Expression Omnibus (GEO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and International Cancer Genome Consortium (ICGC; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dcc.icgc.org\u003c/span\u003e\u003cspan address=\"http://dcc.icgc.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases. Besides, a total of 50 pairs HCC and corresponding adjacent non-cancerous tissue samples from First Affiliated Hospital of Guangxi Medical University were served as the Guangxi cohort. Then, the corresponding clinical info, including age, gender, serum AFP, tumor size, histologic grade, vascular invasion, China Liver Cancer Staging (CNLC), and Barcelona Clinic Liver Cancer (BCLC) staging were also collected.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Differentia expression and prognostics value analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe differential \u003cem\u003eRAD51AP1\u003c/em\u003e expression analyses between HCC and non-cancerous liver tissues were carried out in the TCGA, GSE14520, GSE76427, ICGC and Guangxi cohorts. The diagnostic ability of \u003cem\u003eRAD51AP1\u003c/em\u003e was evaluated through plotting receiver operating characteristic (ROC) curves. The median expression of \u003cem\u003eRAD51AP1\u003c/em\u003e was used to categorize the subgroups as high- and low-expression. The prognostic value of \u003cem\u003eRAD51AP1\u003c/em\u003e was explored preliminarily by Kaplan-Meier survival analysis in TCGA-HCC cohort and was validated in the GSE14520 and ICGC cohorts. Using the \u0026ldquo;ggpubr\u0026rdquo; R package, the difference of \u003cem\u003eRAD51AP1\u003c/em\u003e expression level between different clinicopathological characteristics was investigated in the TCGA and Guangxi cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Nomogram Construction\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eUnivariate and multivariate Cox regression analyses were performed in TCGA cohort to analyze prognostic factors of HCC. Furthermore, nomogram is an effective approach to measure the specific risk by integrating multiple variables. Utilizing the \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;rms\u0026rdquo; R package, a nomogram combining \u003cem\u003eRAD51AP1\u003c/em\u003e expression and easily accessible and widely accepted clinicopathological parameters (gender, age, BMI, AFP, histologic grade, TNM stage, and vascular invasion) was established based upon the TCGA-HCC data to determine the likelihood of 1-, 3-, and 5-year over survival (OS) for HCC patients. The analysis removed the patients with incomplete clinical data and follow-up periods of fewer than 30 days. The bootstrap method was applied to calculate concordance index (C-index) with 1000 resamples. The discrimination performance of nomogram was examined based on the consistency degree of calibration curves.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Functional enrichment analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGene Set Enrichment Analysis (GSEA) software program (v4.1.0) was employed to seek the putative regulatory mechanisms of high- and low-\u003cem\u003eRAD51AP1\u003c/em\u003e expression subgroups in virtue of the gene set data \u0026ldquo;c2.all.v7.0.symbols.gmt\u0026rdquo; and \u0026ldquo;c5.all.v7.0.symbols.gmt\u0026rdquo; \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The number of permutations was set at 1,000. Functional terms that met the criteria of a nominal \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were regarded as significant enrichment pathways.\u003c/p\u003e \u003cp\u003eAdditionally, based on the entire mRNA expression profile of the TCGA-HCC dataset, the genome-wide correlation analysis of \u003cem\u003eRAD51AP1\u003c/em\u003e was performed. Genes with correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.7 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were incorporated to implement the gene ontology terms (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses utilizing the \u0026ldquo;clusterProfiler\u0026rdquo; R package.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Immune Cell Infiltration Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBased on a novel analytical methodology, namely cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), and TCGA-HCC expression profiling, the proportion of various immune cell in each sample were assessed. Then, the immune infiltration levels in different \u003cem\u003eRAD51AP1\u003c/em\u003e expression subgroups were uncovered. The correlation analyses of \u003cem\u003eRAD51AP1\u003c/em\u003e and multiple immune cells was also conducted. Tumor Immune Estimation Resource 2 (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), a data repository for measuring immune cell infiltrations of distinct cancers. Spearman correlation analyses in TIMER2 were performed between \u003cem\u003eRAD51AP1\u003c/em\u003e and immune cell subsets to validate the results of CIBERSORT analysis. To further illustrate the relationship between \u003cem\u003eRAD51AP1\u003c/em\u003e expression and cancer immunity, the immune-associated cell infiltration levels and function pathways of the different \u003cem\u003eRAD51AP1\u003c/em\u003e expression subgroups analyzed in the TCGA and ICGC datasets using the \"gsva\" R package with an algorithm called single-sample Gene Set Enrichment Analysis (ssGSEA). Additionally, the stromal score, immune score and ESTIMATE score of each TCGA-HCC samples were calculated using the \u0026ldquo;estimate\u0026rdquo; R package and the relationships between \u003cem\u003eRAD51AP1\u003c/em\u003e expression and these scores were also excavated.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Drug sensitivity prediction analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe association between \u003cem\u003eRAD51AP1\u003c/em\u003e expression and tumor mutation burden (TMB) and established immune checkpoint gene were explored using the Spearman correlation analysis in the TCGA-HCC cohort. The R package \u0026ldquo;pRRophetic\u0026rdquo; was applied to determine the half-maximal inhibitory concentration (IC50) of HCC treatment related chemotherapeutic and immunotherapeutic drugs by ridge regression method \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Additionally, a computationally based online program called Tumor Immune Dysfunction and Exclusion (TIDE, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) stimulates the immune system of human body to further forecast the response to immune checkpoint inhibitor. The calculated score of TIDE, immune dysfunction, immune exclusion, and microsatellite instability (MSI) of each TCGA-HCC sample were acquired from TIDE website. In addition, is a dataset containing TCGA data of 20 solid cancers and more than 8,000 tumor samples. The immunophenotypic score (IPS) of tumor sample can be obtained from the Cancer Immunome Atlas (TCIA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcia.at/\u003c/span\u003e\u003cspan address=\"https://tcia.at/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and contribute to estimate the therapeutic effect of immune-checkpoint inhibitors (ICIs), including anti-programmed cell death protein 1 (PD-1)and anti- cytotoxic T lymphocyte antigen-4 (CTLA-4) antibodies \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. These immunotherapy-related indicators, including IC50, TIDE and IPS were compared between the different \u003cem\u003eRAD51AP1\u003c/em\u003e expression subgroups in the TCGA-HCC cohort. Besides that, IMvigor210 dataset, a sizable immunotherapy dataset containing the information of 298 urothelial carcinoma patients at advanced stage who were administered with anti-PD1 inhibitor, was used to grope the immunotherapy response predictability of \u003cem\u003eRAD51AP1\u003c/em\u003e in other cancer.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Reverse transcription quantitative PCR\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe method of specimen preservation, RNA extraction, and reverse transcription quantitative PCR were conducted as previously described \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Clinical sample collecting and handling procedures strictly adhered the standard protocol. The sequence of primers was as follows: GAPDH, forward: GTCAGCCGCATCTTCTTT, reverse: CGCCCAATACGACCAAAT. \u003cem\u003eRAD51AP1\u003c/em\u003e, forward: AGTGAAGGTAAAATCCCCAGTAGA, reverse: TGGCAAGGACTGAGATTCTGAT. Using the 2 ∆∆CT approach, the relative mRNA expressions of \u003cem\u003eRAD51AP1\u003c/em\u003e were measured \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical analysiss\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eR (v4.1.1) was applied to complete data analysis and result visualization. The Wilcoxon rank-sum test was conducted between the different subgroups with continuous data, while the chi-square test was used for categorical data. Paired t-test was utilized to explore the discrepancy of \u003cem\u003eRAD51AP1\u003c/em\u003e expression levels between HCC samples and adjacent normal tissues of Guangxi cohort. The association of two variables was probed using the Spearman correlation analysis. Unless otherwise indicated, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was provided as the threshold cutoff value.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv\u003e\n \u003ch2\u003e3.1 The upregulated expression level of \u003cem\u003eRAD51AP1\u003c/em\u003e in HCC\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cem\u003eRAD51AP1\u003c/em\u003e gene expression level was markedly over-expressed in HCC compared with normal liver tissues in various cohorts (Figure. 1A-E). The ROC curve indicated that \u003cem\u003eRAD51AP1\u003c/em\u003e had a high predictive accuracy of HCC diagnosis with the area under curve (AUC) \u0026gt; 0.900 in the TCGA, GSE14520 and ICGC cohorts (Figure. 1F). The AUC values of GSE76427 and Guangxi cohort were 0.821 and 0.721 respectively. These results showed that \u003cem\u003eRAD51AP1\u003c/em\u003e had a diagnostic significance for HCC.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003e3.2 \u003cem\u003eRAD51AP1\u003c/em\u003e correlated with dismal survival and poor clinical features\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe survival analysis indicated that the high \u003cem\u003eRAD51AP1\u003c/em\u003e-expression group showed the poorer OS and recurrence free survival (RFS) than that of the low expression group in the TCGA cohorts (Figure. 2A, B). Furthermore, the result was confirmed in the GSE14520 and ICGC cohorts (Figure. 2C, D). Moreover, a significantly higher \u003cem\u003eRAD51AP1\u003c/em\u003e gene expression was observed in patients with serum AFP \u0026gt; 400 ng/ml in the TCGA cohorts. Along with the advance of clinicopathologic features believed to get poorer prognosis, including histologic grade, TNM stage, T staging and vascular invasion degree, the expression level of \u003cem\u003eRAD51AP1\u003c/em\u003e presented an upward trend (Figure. 3). We further confirmed that \u003cem\u003eRAD51AP1\u003c/em\u003e was upregulated in patients of Guangxi cohort with AFP \u0026gt; 400 ng/mL, poor histologic grade, large tumor size and more advanced stage (CNLC Ia \u003cem\u003evs.\u003c/em\u003e Ib) (Figure. 4).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003e3.3 Construction of a nomogram\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eUnivariate Cox analysis discovered that \u003cem\u003eRAD51AP1\u003c/em\u003e expression (HR = 1.218; 95% CI = 1.055–1.407; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), TNM stage, T staging, and vascular invasion were the high-risk factors for OS in TCGA-HCC patients (Figure. 5A). \u003cem\u003eRAD51AP1\u003c/em\u003e expression (HR = 1.159; 95% CI = 1.002–1.341; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) remained statistically significant in multivariate Cox analysis. Therefore, \u003cem\u003eRAD51AP1\u003c/em\u003e could be identified as an independent prognostic factor (Figure. 5B). The established nomogram with the C-index of 0.725 contributed to predict the OS of specific HCC patients (Figure. 5C). The calibration curves exhibited a satisfactory consistency between the estimated and the actual OS rates at 1, 2, and 3 years (Figure. 5D).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003e3.4 \u003cem\u003eRAD51AP1\u003c/em\u003e was involved in cancer-promoting and cell cycle related pathways\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eGO terms enrichment analysis discovered that up-regulation of \u003cem\u003eRAD51AP1\u003c/em\u003e was significantly related to “ATPASE_ACTIVITY, CELL_CYCLE_G1_S_PHASE_TRANSITION, CHROMOSOME_SEGREGATION, DNA_REPLICATION, NUCLEAR_INNER_MEMBRANE, REGULATION_OF_GENE_EXPRESSION_EPIGENETIC, SIGNAL_TRANSDUCTION_BY_P53_CLASS_MEDIATOR” pathways (Figure. 6A). Furthermore, KEGG pathway analysis highlighted that “REGULATION_OF_AUTOPHAGY, NOTCH_SIGNALING_PATHWAY, P53_SIGNALING_PATHWAY, PATHWAYS_IN_CANCER, ERBB_SIGNALING_PATHWAY” pathways were enriched in the up-regulated \u003cem\u003eRAD51AP1\u003c/em\u003e subgroup, while “COMPLEMENT_AND_COAGULATION_CASCADES, DRUG_METABOLISM_CYTOCHROME_P450, FATTY_ACID_METABOLISM, METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450” pathways were enriched in down-regulated \u003cem\u003eRAD51AP1\u003c/em\u003e subgroup (Figure. 6B).\u003c/p\u003e\n \u003cp\u003eThe results of genome-wide correlation analysis showed that 92 genes were relevant to \u003cem\u003eRAD51AP1\u003c/em\u003e with the absolute value of a correlation coefficient ≥ 0.7 in the TCGA-HCC cohort. All these genes were positively correlated with \u003cem\u003eRAD51AP1\u003c/em\u003e expression. (Figure. 7A). The \u003cem\u003eRAD51AP1\u003c/em\u003e-associated HCC hallmarks, including regulation of cell cycle, cell cycle phase transition, nuclear chromosome, tubulin binding, cellular senescence, FoxO signaling pathways, were identified by the GSEA analysis on these correlated genes.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003e3.5 Correlation between \u003cem\u003eRAD51AP1\u003c/em\u003e and the immune infiltration of tumor microenvironment in HCC\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eCIBERSORT analysis revealed higher T cells follicular helper but lower T cells CD4\u003csup\u003e+\u003c/sup\u003e memory resting significantly infiltrated in the high-\u003cem\u003eRAD51AP1\u003c/em\u003e expression subgroup (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Figure. 8A). In line with the above results, a correlation heatmap displayed the positive and negative connections of \u003cem\u003eRAD51AP1\u003c/em\u003e expression and different immunizing cells (Figure. 8B). In TIMER2, \u003cem\u003eRAD51AP1\u003c/em\u003e expression negatively correlated to T cells CD4\u003csup\u003e+\u003c/sup\u003e memory resting, but positively correlated to B cell memory, cell CD4\u003csup\u003e+\u003c/sup\u003e T memory activated and Myeloid dendritic cell actived (Supplementary Figure. 1). The ssGSEA analysis showed that the up-regulated \u003cem\u003eRAD51AP1\u003c/em\u003e expression exhibited the higher macrophages, Th2 and Treg cells infiltration levels but lower immune function of type Ⅱ IFN response in both TCGA and ICGC cohort (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Figure. 9A-D).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003e3.6 Predictive value of therapeutic efficacy in HCC\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe up-regulated expression levels of immune-related checkpoint genes, including \u003cem\u003eCD44, HAVCR2, LGALS9, PDCD1, TNFRSF4, TNFRSF14, TNFRSF18, CD27, CD48, CD40, IDO1\u003c/em\u003e and \u003cem\u003eLAG3\u003c/em\u003e, were uncovered in the high-\u003cem\u003eRAD51AP1\u003c/em\u003e expression subgroup (Figure. 10A). Moreover, \u003cem\u003eRAD51AP1\u003c/em\u003e expression was negatively related to stromal score (\u003cem\u003eR\u003c/em\u003e = -0.20, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), ESTIMATE score (\u003cem\u003eR\u003c/em\u003e = -0.13, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). However, the correlations of \u003cem\u003eRAD51AP1\u003c/em\u003e and immune score or TMB were statistically insignificant (Figure. 10B-C). The findings of specific drug sensitivity analysis showed that eight medications (Sorafenib, Nilotinib, Axitinib, Erlotinib, Dasatinib, Docetaxel, Pazopanib, Pyrimethamine, Rapamycin, Sunitinib and Temsirolimus) displayed higher IC50 in high-\u003cem\u003eRAD51AP1\u003c/em\u003e expression subgroup, whereas Bexarotene, Doxorubicin, Gemcitabine and Tipifarnib had lower IC50, which suggesting that the high-expression subgroup more susceptible to Bexarotene, Doxorubicin, Gemcitabine and Tipifarnib (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, Figure. 11).\u003c/p\u003e\n \u003cp\u003eViolin plots showed the high-\u003cem\u003eRAD51AP1\u003c/em\u003e expression subgroup exhibited relatively lower IPSs, representing a preferable reaction to \u003cem\u003ePD-1\u003c/em\u003e and \u003cem\u003eCTLA-4\u003c/em\u003e inhibitors (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Figure. 12A-D). The subgroup with higher TIDE score represented non-responders whose suppressive cells inhibit T cell infiltration. We observed that the high-\u003cem\u003eRAD51AP1\u003c/em\u003e expression subgroup had markedly lower TIDE and immune dysfunction scores but higher immune exclusion scores than those of low expression subgroup (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, Figure. 11F-I). Responders in the IMvigor210 had significantly higher \u003cem\u003eRAD51AP1\u003c/em\u003e expression, which demonstrating that \u003cem\u003eRAD51AP1\u003c/em\u003e could reliably predict the therapeutic response of immunotherapy not just for HCC but for other cancer types as well.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGenetic alterations is a hallmark characteristic of hominine solid tumors\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e and can greatly affect the formation and progression of HCC\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. DNA damage can activate DNA damage repair systems, which increased the risk of DNA modifications and genome defection \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Homologous recombination DNA repair (HR), especially operating in double-strand DNA breaks\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, is identified as an efficient repair mechanism\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eRAD51\u003c/em\u003e recombinase can catalyze HR\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e and form a nucleoprotein filament to promote HR DNA repair\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eRAD51AP1\u003c/em\u003e acts as a necessary protein to activate \u003cem\u003eRAD51\u003c/em\u003e recombinase\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Thus, \u003cem\u003eRAD51AP1\u003c/em\u003e functions to keep genomic integrity via \u003cem\u003eRAD51\u003c/em\u003e recombinase enhancement\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Furthermore, Gonzalez et al showed that \u003cem\u003eRAD51AP1\u003c/em\u003e functions to maintain telomere length and protect against proliferation of alternative lengthening of telomeres in cancer cells\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. It is worth mentioning that Wu et al (2019) demonstrated a up-regulated \u003cem\u003eRAD51AP1\u003c/em\u003e in non-small cell lung cancer (NSCLC). In addition, the metastasis, proliferation, invasion, and migration of NSCLC cell line were inhibited under circumstance of \u003cem\u003eRAD51AP1\u003c/em\u003e silencing\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Obama et al (2019) showed that downregulation of \u003cem\u003eRAD51AP1\u003c/em\u003e retarded growth of intrahepatic cholangiocarcinoma cells\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Likewise, \u003cem\u003eRAD51AP1\u003c/em\u003e knockout experiments showed that \u003cem\u003eRAD51AP1\u003c/em\u003e knockout in human breast cancer cells and syngeneic mouse models reduced tumor growth and metastasis by increasing breast cancer stem cell self- renewal\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. A mouse model experiment using U87MG cells revealed that knocking-down \u003cem\u003eRAD51AP1\u003c/em\u003e inhibited glioma\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our study, the upregulated \u003cem\u003eRAD51AP1\u003c/em\u003e expression related to shorter OS and RFS, histologic grade, TNM stage, T staging and vascular invasion degree and higher serum AFP level and was involved in cancer-promoting pathways in HCC, indicating that \u003cem\u003eRAD51AP1\u003c/em\u003e represents increased malignancy and poor prognosis. Immune checkpoint inhibitors (ICIs) and molecularly targeted therapy have achieved remarkable survival benefit to the HCC managements\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. HCC has a high tumor heterogeneity and different molecule characteristics\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Tumor associated macrophages (TAMs), contributing to tumor growth and progression, are the essential elements of tumor microenvironment\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eRAD51AP1\u003c/em\u003e was demonstrated to regulate colorectal cancer stem cell self-renewal and promote colorectal cancer growth and drug resistance\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. We demonstrated that \u003cem\u003eRAD51AP1\u003c/em\u003e may be associated with an immunosuppressive microenvironment with higher TAMs infiltration in HCC. Interestingly, the high \u003cem\u003eRAD51AP1\u003c/em\u003e expression may suggest a better immunotherapy response and could be more susceptible to Bexarotene, Doxorubicin, Gemcitabine and Tipifarnib. Collectively, \u003cem\u003eRAD51AP1\u003c/em\u003e may be a potential molecular therapeutic target. The dominating analyses were bioinformatics research based on the public datasets. Therefore, immunohistochemistry is urgently needed to confirm the \u003cem\u003eRAD51AP1\u003c/em\u003e protein expression level in HCC tissue. The molecular mechanisms of \u003cem\u003eRAD51AP1\u003c/em\u003e for HCC still require further validated research \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTaken together, \u003cem\u003eRAD51AP1\u003c/em\u003e mediating the immunosuppressive microenvironment is a potential diagnostic and prognostic biomarker and could be underlying therapeutic target for HCC. However, this conclusion still requires further validations form additional cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Materials:\u0026nbsp;\u003c/strong\u003eThe following supporting information can be downloaded at: www.mdpi.com/xxx/s1, Supplementary Figure S1 and Supplementary Table S1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e LCL and WYG analyzed data and wrote the manuscript. ZGZ and PT conceived and designed research and revised the manuscript. WZL, ZX and WXK conducted experiments. HHS, LTM and LXW collected and sorted data. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by the National Natural Science Foundation of China No. 81902500, the Key Laboratory of High-Incidence-Tumor Prevention \u0026amp; Treatment (Guangxi Medical University), Ministry of Education (grant nos. GKE2018-01, GKE2019-11, GKE-ZZ202009 and GKE-ZZ202109), Guangxi Key Laboratory for the Prevention and Control of Viral Hepatitis (No. GXCDCKL201902), Key R\u0026amp;D Plan of Qingxiu District, Nanning (NO.2020056), Natural Science Foundation of Guangxi Province of China (grant no. 2020GXNSFAA159127) and Self-funded Scientific Research Project of Health Commission in Guangxi Zhuang Autonomous Region (Z20210977).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eThis experiment was authorized by the Ethical Review Committee of the First Affiliated Hospital of Guangxi Medical University [Approval Number: 2021 (KY-E-032)].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eInformed consent was agreed and signed by all HCC patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e Firstly, the authors would like to thank TCGA, GEO and ICGC projects for data sharing. Second, I would like to express my gratitude to the authors of this article and thank them for their help and dedication from the end. Finally, the authors would like to thank all the staff in the editorial department, and thanks for all your valuable comments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2018, 68(6): 394-424.\u003c/li\u003e\n\u003cli\u003eRahib L, Smith B D, Aizenberg R, et al. 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J Immunother Cancer, 2021, 9(1).\u003c/li\u003e\n\u003cli\u003eBridges A E, Ramachandran S, Tamizhmani K, et al. \u003cem\u003eRAD51AP1\u003c/em\u003e Loss Attenuates Colorectal Cancer Stem Cell Renewal and Sensitizes to Chemotherapy[J]. Mol Cancer Res, 2021, 19(9): 1486-1497.\u003c/li\u003e\n\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":"Hepatocellular carcinoma, RAD51AP1, Prognostic signature, Bioinformatics, Immune filtration, Drug sensitivity","lastPublishedDoi":"10.21203/rs.3.rs-2638542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2638542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eRAD51\u003c/em\u003e associated protein 1 (\u003cem\u003eRAD51AP1\u003c/em\u003e) has been showed that regulated cell proliferation and cancer progression. However, the immune infiltrating correlation and therapeutics guidance of \u003cem\u003eRAD51AP1\u003c/em\u003e in hepatocellular carcinoma (HCC) still need further investigation. In this study, differential expression, clinicopathologic correlation, prognostic value, and function enrichment analysis of \u003cem\u003eRAD51AP1\u003c/em\u003e were performed in TCGA, GSE14520, GSE76427 and ICGC datasets and were validated using Guangxi cohort. We explored the predictive value of \u003cem\u003eRAD51AP1\u003c/em\u003e to therapeutics response comprehensively and probed the correlation between \u003cem\u003eRAD51AP1\u003c/em\u003e and HCC immunoinfiltration by CIBERSORT and ssGSEA. \u003cem\u003eRAD51AP1\u003c/em\u003e with a high diagnostic accuracy was significantly overexpressed in HCC tissues. The shorter survival time and poorer clinical features were showed when \u003cem\u003eRAD51AP1\u003c/em\u003e upregulated. A nomogram featuring \u003cem\u003eRAD51AP1\u003c/em\u003e and clinicopathologic factors was established to predict OS of HCCs. \u003cem\u003eRAD51AP1\u003c/em\u003e might be engaged in the carcinogenic and celluar cycle processes. In CIBERSORT analysis, higher T cells follicular helper but lower T cells CD4\u0026thinsp;+\u0026thinsp;memory resting infiltrations were exhibited when \u003cem\u003eRAD51AP1\u003c/em\u003e upregulated. T demonstrated that High-\u003cem\u003eRAD51AP1\u003c/em\u003e expression subgroup had higher macrophages, Th2 and Treg cells infiltration, but lower type Ⅱ IFN response function in ssGSEA analysis, exhibited the upregulated immune-related checkpoint expression levels, lower IPS and TIDE scores, suggesting a better immunotherapy response, and may be more susceptible to Bexarotene, Doxorubicin, Gemcitabine and Tipifarnib. Taken together, \u003cem\u003eRAD51AP1\u003c/em\u003e mediating the immunosuppressive microenvironment is a potential diagnostic and prognostic biomarker and could be underlying HCC treatment strategy.\u003c/p\u003e","manuscriptTitle":"RAD51AP1 as an immune-related prognostic biomarker and therapeutic response predictor in hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-06 23:53:58","doi":"10.21203/rs.3.rs-2638542/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":"2ac541a0-5ce6-4571-90fb-b230e001ebbe","owner":[],"postedDate":"March 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-06-29T05:14:31+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-06 23:53:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2638542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2638542","identity":"rs-2638542","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}