C5orf46: a Promising Prognosis Risk Indicator with Implication in the Remodeling of KIRC included Pan-cancer Tumor Microenvironment

C5orf46: a Promising Prognosis Risk Indicator with Implication in the Remodeling of KIRC included Pan-cancer Tumor Microenvironment | 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 C5orf46: a Promising Prognosis Risk Indicator with Implication in the Remodeling of KIRC included Pan-cancer Tumor Microenvironment Xuzhi Wang, Jiayao Li, Huijun Yang, Fei Wang, Lei Miao, Siying Liu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6650741/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 C5orf46 is a recently discovered tumor progression related gene whose function in most cancers are still unknown, especially its potential regulation on tumor microenvironment (TME). The aim of the study is to explore C5orf46 gene function in kidney renal clear cell carcinoma (KIRC) included human pan-cancer for potential clinical application. Methods The study started with the physicochemical property of C5orf46, and then the gene expression as well as alteration patterns in diverse cancers, followed by post transcription modulation of the gene and then survival analysis. Moreover, the correlation between C5orf46 and multiple cancer TME related parameters including angiogenesis, extracellular matrix (ECM) degradation and immune infiltration were in succession explored. Further, C5orf46 association with others critical cancer features for instance cancer stemness, tumor epithelial mesenchymal transition (EMT) and DNA repair were also investigated. Results Firstly, physicochemical properties including the aminoacid composition, estimated molecular weight and protein half life of C5orf46 gene were in succession computed. Then, based on gene expression as well as survival analysis result, C5orf46 was shown to be up-regulated in various human cancers, of which KIRC was the top cancer with highest C5orf46 expression difference between cancer and corresponding normal tissues. And the changed expression was partly due to DNA methylation modulation. Meanwhile, of more clinical significance, the up-regulated C5orf46 expression was correlated with both worse patients overall survival and shorter recurrence free survival. Moreover, the association between C5orf46 and multiple critical cancer traits including microenvironment angiogenesis, immune infiltration, ECM degradation and cancer EMT were validated. Further, C5orf46 gene was indicated to correlate with the sensitivity of several chemotherapy related drugs. Conclusions Based on TCGA pan-cancer data and local hospital samples validation, C5orf46 was indicated to potentially works as an oncogene in diverse cancers, and the gene was associated with multiple critical cancers traits. Pan-cancer analysis C5orf46 tumor microenvironment prognosis risk immune infiltration drug target Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Background Cancer has been a worldwide health threat for people, and the initiation of cancer involves not only the accumulation of genetic mutations, epigenetic alterations or changed expression of oncogenes and tumor inhibiting genes, but also cellular communication and interaction between cancer cells and surrounding microenvironment (TME)[ 1 ]. Cancer TME has been gradually recognized to be a complex yet important ecosystem which involves not only cancer cells, but also a wide range of non-cancerous cells including various immune cells, cancer-associated fibroblasts (CAFs), endothelial cells and other tissue specific cell types[ 2 ]. Increasing number of studies have been showing that the interaction between cancer cells and TME could exert great effect on tumor clinical traits including cell proliferation, cancer metastasis, microenvironment angiogenesis and significant immune surveillance escape[ 3 , 4 ]. Depending on the specific organ that different tumors arise, intrinsic features of the cancer cells, tumor grade, stage, patient characteristics, cellular composition as well as the functional state of the TME will differ, and various cells in the TME can play either tumor suppressive or tumor supporting functions[ 1 , 5 , 6 ]. Exosomes are extracellular vesicles with a diameter of 30-100nm that could be released by various TME cells, containing various substances such as nucleic acids, proteins and lipids[ 7 , 8 ]. Exosomes have been an important way for cellular communication and serving as carriers to form a signaling network system, mediating the interactions between tumor cells and surrounding TME cells. Tumor cells derived exosomes (TDEs) have been reported to play vital functions in the formation and development of different tumor processes, including promoting tumor epithelial mesenchymal transition (EMT), mediating immune escape, TME remodeling, inducing angiogenesis, and regulating macrophage polarization[ 9 – 11 ]. The diversified function of exosome has been attracting widespread attention, and it’s of clinical significance to keep identifying the role of exosome as well as its containing proteins or nucleic acids in cancers, the results shall provide promising insight for further understanding of the collaborative working network of cancer TME and identifying potential cancer biomarkers. In the study, we mainly focused on a specific gene C5orf46 which is a recently discovered tumor relating gene, and the gene has been predicted by multiple online database including GeneCards and UniProt to be locating in extracellular exosomes. As for the gene function, C5orf46 is short for chromosome 5 open reading frame 46, and it has been reported to be associated with the patients survival in gastrointestinal tumor[ 12 ], some renal and gastric cancers[ 13 , 14 ], but the full spectrum of C5orf46’s role in human cancers, especially its potential regulation on cancer TME remodeling, for instance tumor angiogenesis, immune regulation and extracellular matrix (ECM) degradation as a predicted exosome containing gene has yet to be explained. Based on TCGA pan-cancer data and local hospital cancer samples, we comprehensively analyzed the expression as well as clinical traits of C5orf46 in human cancers. Considering kidney renal clear cell carcinoma (KIRC) was indicated to be the top cancer with highest C5orf46 expression difference between cancer and corresponding normal tissues, and also although KIRC is able to benefit from PD-1/PD-L1 blockade immunotherapy, the function of the commonly used immune biomarkers including PD-L1 expression, MSI and tumor mutation burden (TMB) in KIRC are still in dispute, so a comprehensive analysis of the cancer immune microenvironment might benefit further tumor immunotherapy. Thus, KIRC was highly focused during our validation of C5orf46 association with pan-cancer clinical traits. The results shall provide promising insights for understanding the role of exosomal C5orf46 in human cancers and aid the unearthing of potential new prognostic indicators. Materials and Methods Data source: TCGA pan-cancer gene expression profiles The study was started with TCGA pan-cancer data analysis and then local hospital samples validation, and the TCGA data was accessed using UCSC Xena[15]. Based on the downloaded TCGA data, the basic information of C5orf46 especially its expression levels in pan-cancers, and its association with cancers clinical parameters for instance cancer stages, grades, subtypes, patients age and gender were in succession explored. Basic physicochemical property analysis of C5orf46 gene The basic genetic information and physicochemical properties of C5orf46 gene was investigated based on a combine of five analysis databases including ProtParam[16], ProtScale[17], Uniprot[18], GeneCards[19] and Human Protein Atlas[20]. ProtParam and ProtScale were together applied to investigate the physicochemical properties of the protein including its computed aminoacid composition, encoded protein molecular weight, its theoretical isoelectric point, estimated protein half life, instability index as well as hydrophobicity and hydrophilicity. Meanwhile, Uniprot, GeneCards and Human Protein Atlas were in succession accessed to investigate the cellular location and expression level of C5orf46 in human cancers. Expression in pan-cancer and association with cancers clinical parameters After understanding the basic genetic and encoding protein property of C5orf46, UALCAN[21] which has been an effective and open access online service for interpreting the association between certain genes and cancers clinical parameters were applied to investigate C5orf46 expression in cancers comparing to normal control samples. At first, based on UALCAN, C5orf46 expression patterns in human pan-cancer including but not limited to the top morbidity and mortality cancers for instance breast invasive carcinoma (BRCA), pancreatic adenocarcinoma (PAAD), rectum adenocarcinoma (READ), hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), kidney clear cell carcinoma (KIRC) and stomach adenocarcinoma (STAD) comparing to corresponding normal control tissues were investigated. Besides, the association between C5orf46 expression level and important clinical parameters including cancer stages, grades and other cancers features in these cancers were also observed. Then, besides UALCAN online database analysis, local hospital samples were also used to explore the association between C5orf46 expression and cancer clinical features. Considering KIRC was the top cancer with highest C5orf46 gene expression difference between cancer and corresponding normal samples, the analysis was mainly focused in KIRC. A total of 39 KIRC samples were applied for further tissue microarray production and Immunohistochemistry (IHC) experiments. Tissue microarray production In the study, KIRC and Giant cell tumor of bone (GCTB) samples were used to make tissue microarrays for further IHC experiment. The cancer tissues were all obtained from local hospital Biobank. Scientific use of the Biobank samples in this study was approved by both the Biobank Comittee and Hospital Institutional Board (Second Hospital of ShanXi Medical University, China). In the study, 39 cases of KIRC and 20 cases of GCTB samples were obtained from biobank after HE staining confirmation of the disease diagnosis and evaluation of cancer percentage by local hospital registered pathologists. And to eliminate tumor heterogeneity, in each sample case, three to four areas were circled, followed by preparing the receptor wax block with 1.5mm needle according to operating instructions (Chloe, BeiJin, China). Further, the tissue microarray was serially sliced and stored at 4°C refrigerator for next step experiment. Immunohistochemistry (IHC) experiments Tissue samples and regents IHC experiment was conducted using above cancer tissue microarrays to detect the gene’s expression pattern in different cancer samples. And it was performed on VENTANA platform (Roche) in local hospital Pathology Department. The primary antibody of C5orf46 gene was purchased from Invitrogen (PA5-140360), and the secondary antibody (Envision /HRP kit) and DAB detection kit were from ZSBG-Bio. Other reagents including but not limited to H2O2, antigen retrieval citrate solution, phosphate-buffered saline (PBS) and hematoxylin stain were all supplied by our hospital Supply Department. IHC experimental protocol The tissue microarray slides were processed in the order of deparaffin, rehydration with gradient ethanol followed by antigen retrieval according to the antibody’s manual instruction. Meanwhile, to inhibit the activity of endogenous peroxidase, the slides were maintained in 0.3% H2O2 containing methanol for 10min. Further, the slides were soaked in bovine serum albumin for 30min and then incubated with primary C5orf46 antibody (dilution 1:200) overnight at 4°C followed by a 40 minutes secondary antibody incubation at 37°C. Finally, the slides were processed with horseradish peroxidase (HRP) and visualized in DAB for results evaluation. IHC results evaluation C5orf46 was observed to distribute mostly in cell cytoplasm, cell membrane and extra cellular regions. The evaluation of the IHC result was based on both staining intensity and staining area. In the study, IHC result was evaluated by two local hospital registered pathologists with the staining intensity scored as: None (0), mild (1), moderate (2) and strong (3), meanwhile, the staining area was classified as: 10% (0), 11–25% (1), 26–50% (2), 51–75% (3) and >75% (4) (Example in Figure 1A-1D). The section’s final score equals the multiplication of staining intensity and staining area, and the final result of each patient’s tissue was recorded as the average of three independent microarray cores’ scores, and if the final score<6, the result was defined as low expression, meanwhile, if final score≥6, the result was classified as high expression. Survival analysis of C5orf46 gene in pan-cancer Survival analysis is conducted based on Kaplan-Meier plotter[22] which has been an effective used database for investigating the overall survival (OS) as well as recurrence free survival (RFS) correlation of human genes, in the database, over 10,000 cases covering broad spectrum human cancers were contained. The prognosis indicating value of C5orf46 in pan-cancers were analyzed based on the platform. Methylation regulation and genetic alterations analysis To preliminary explore the potential reason of altered expression of C5orf46 in cancers comparing to corresponding normal tissues, the common gene expression modulation especially methylation and phosphorylation regulations of the gene was preliminary explored based on UALCAN platform which has been above used for analyzing the clinical parameters association of the gene. Based on UALCAN, the relative methylation level of C5orf46 in cancers as well as the relative phosphorylated protein comparing to total C5orf46 protein levels in cancers were observed. Meanwhile, besides altered mRNA expression, other types of genetic variations for instance gene mutation, copy number variation, amplification and deletion are also important genetic changes that may effect on cancers biological functions. cBioPortal[23] has been a rich data resource for world wide researches to investigate certain genes variations, and in the study, it was applied to firstly uncover the genetic alterations of C5orf46 in human cancers using “cancer types summary” module of “quick search” section in the database, and then display the mutated site information of C5orf46 gene in 3D protein structures using the “mutation” module of the database. C5orf46 centering Protein-protein interaction (PPI) network construction and related genes analysis After understanding the expression patterns as well as genetic alterations of C5orf46 gene in cancers, the next step was to investigate the detailed functions of C5orf46 in cancers and to preliminary explore the potential mechanism. Firstly, STRING, which is short for Search Tool for the Retrieval of Interacting Genes[24] was used to construct the PPI network centering on C5orf46 gene for investigating its surrounding interacting gene partners. And then genes enrichment analysis was applied to annotate the basic biological attributes of PPI network containing genes including their main cellular location, involved biological processes, molecular functions and the signaling pathways the genes mainly enriched in. Thus, C5orf46 gene and its potential interacting partner genes as well as their biological features were preliminary understood. TME angiogenesis association of C5orf46 gene in pan-cancer To validate the association between C5orf46 gene and tumor angiogenesis, 32 well known genes that were previously reported to be related with TME angiogenesis (listed in Supplementary Table 1) including vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF) and platelet derived growth factor (PDGF) were selected. Further, based on TCGA pan-cancer data, the expression of the 32 genes expression in cancers were observed, followed by being input into GEPIA2.0 for calculating their correlations with C5orf46. Then, IHC experiment was additionally conducted to validate the association between C5orf46 expression and KIRC cancer angiogenesis, CD31 and CD34 which have been two commonly used markers for immature and mature tissue blood vessels were used for analyze. Primary antibodies to both CD31 and CD34 were from hospital Pathological Department, and other IHC experiment regents and protocols were same as described before. Meanwhile, the evaluation of CD31 and CD34 marked blood vessels were scored as the average number of 5 pictures under 200x magnification microscope field. Extra cellular matrix (ECM) degradation association analysis Besides TME angiogenesis, ECM degradation has been another major aspect of TME remodeling which has been known to involve in various cancer processes, most significantly cancer invasion and metastasis. For evaluating the association between C5orf46 and cancers ECM degradation, 23 genes that have been supported to be related with ECM degradation were selected (listed in Supplementary Table 2), and based on TCGA pan-cancer expression data, the correlation between C5orf46 and the 23 genes signature was evaluated in various cancers for investigating the potential effect C5orf46 gene has on TME remodeling. Association between C5orf46 and cancers EMT process Epithelial Mesenchymal Transition (EMT) refers to the transformation of epithelial cells to mesenchymal cells, which endows cells the ability to transfer and invade, thus promoting multiple critical cancer processes, for instance inducing stem cell characteristics, reducing apoptosis and increasing immune suppression. For evaluating the effect C5orf46 gene has on cancer EMT process, 14 characteristic genes (listed in Supplementary Table 3) that were indicated by previous reports to be EMT related were identified, and their expressed were accessed based on TCGA pan-cancer data. Further, the correlation between C5orf46 and the 14 genes signature was evaluated. C5orf46 correlation with cancers stemness index Cancer stem cells are a specific group of cells with similar biological traits as the regular stem cells, which posses high self-renewal ability and the ability to differentiate into various populations of cancer cells. In the past decade, scientists engaged in TCGA projects analyzed thousands of cells at different stages of cell differentiation, thereby identifying the typical molecular characteristics possessed by stem cells. Based on these data, they developed the “cancer stemness index” which was calculated based on genes expression and DNA methylation characteristics[25]. The cancer stemness index ranges from 0 to 1, with 0 indicating low similarity to stem cells and 1 indicating high similarity to stem cells, and a higher index seems to be directly related with the progression of cancer. In the study, using the reported “cancer stemness index” calculation algorithm, we calculated the index in human cancers and compared the difference between C5orf46-high and low expression groups of cases. C5orf46 correlation with cancers HRR signature and MRR proteins expression Considering Homologous Recombination Deficiency (HRD) has been an increasing known important clinical trait which correlates not only with disease procession but also PARPi related drugs therapies efficiency[26], besides above calculation of cancer stemness index, we further evaluated the association between C5orf46 and cancer DNA repair ability which was based on TCCA pan-cancer data of 27 selected Homologous Recombination Repair (HRR) signaling participated genes (listed in Supplementary Table 4). Moreover, besides HRD, the potential correlation between C5orf46 expression and another well known DNA deficiency repair system, namely mismatch repair (MMR) proteins expression were also analyzed. Four commonly used MMR proteins namely MLH1, PMS2, MSH2 and MSH6 were detected with local hospital COAD cancer samples which has been a cancer type with high dMMR probability. The the primary antibody to all the four MMR proteins were purchased from ZS-BIO (China), and IHC regents and experiment procedures of the four MMR proteins detection were the same as previously described. Further, C5orf46 expression in COAD samples was detected using qRT-PCR experiment as previously described, and the association between relative C5orf46 expression and MMR status would then be evaluated based on the qRT-PCR and IHC experiment results. Immune infiltration cells and CTL dysfunction correlation Besides previously analyzed tumor EMT process, angiogenesis and ECM degradation, exosomes have been known to participate in immune microenvironment regulation. For characterizing the immune landscape between C5orf46-hign and low expression cancer samples, CIBERSORT algorithm was performed to calculate the relative contents of 22 tumor infiltrating immune cells (TICs) based on TCGA profiles data, followed by investigating the correlation between C5orf46 expression and the TICs infiltration level. Especially, cytotoxic T lymphocytes (CTLs) have been a key component of adaptive immune system and play important roles in human anti-tumor immunity, and CTLs are also one of the main effector cells of tumor immunotherapy. In the study, the association between C5orf46 expression and CTLs’ function on patients survival rates were especially presented based on Tumor Immune Dysfunction and Exclusion (TIDE) platform[27]. Moreover, considering CD8+T cell has been an important cancer killing immune cell type, and for validating the association between C5orf46 expression and CD8+T cells infiltration, KIRC tissue microarray which has been previously used to evaluate the association with CD31 and CD34 stained blood vessels were again applied to explore the CD8+T cells distribution. The primary antibody to CD8 were purchased from ZS-BIO (China), meanwhile, other IHC regents, equipment and experiment procedure were the same as previously described. The evaluation of CD8+ T cells infiltration were scored as the average number of positive stained cell numbers in 5 pictures that were taken under 200x magnification microscope field. Tumor mutation burden (TMB), microsatellite instability (MSI) and immune therapy sensitivity analysis MSI and TMB are currently clinical effectively used biomarkers for predicting the potential benefit of patients from immune therapies. Therefore, after analyzing the association with immune cells infiltration and CTL functions, C5orf46 gene expression correlation with cancers TMB and MSI scores were next step evaluated based on ACLBI online database[28]. Moreover, based on ROC plotter analysis platform, C5orf46 gene expression differences between the responders and non-responders to specific immune therapies drugs for instance anti PD-1, PD-L1 and CTLA4 inhibitors were displayed, together with the receiver operating characteristic curves (ROC) of therapy-related cancer patients survival. Chemotherapy related drugs sensitivity analysis Besides immune therapy, for preliminary investigating the effects of C5orf46 has on the routine chemotherapeutic response for human cancers, based on ROC plotter analysis platform, C5orf46 gene expression between the responders and non-responders to commonly used chemotherapy drugs in different types of human cancers, as well as the ROC curve of therapy-related patients survival were additionally displayed[29]. Statistical Analysis Most of the bioinformatics analyses were performed on corresponding online databases. As for the processing of downloaded TCGA pan-cancer data, statistical analysis were performed using SPSS 26.0. For enumeration data for instance when comparing genes expression difference between cancers and corresponding normal samples, the data were analyzed using t test. As for the measurement data including the association between gene expression and cancers pathological parameters, the data were analyzed by χ2 test. And for correlation analysis, for instance the correlation between C5orf46 gene expression and angiogenesis signature, the data were analyzed by Spearman analysis. p<0.05 was considered statistically significant. (For all analysis results, * represents p<0.05, ** represents p<0.01, *** represents p<0.001). Results C5orf46 genetic information and physicochemical property C5orf46 which is short for chromosome 5 open reading frame 46 is also known as AP-46 and SSSP1. Based on combine analysis of different platforms, the basic genetic information and physiochemical property of the gene was discovered. The results revealed that C5orf46 gene locates in 5q32, comprises of 5 exons and encodes a 87 amino acids containing protein. The protein formula is C 433 H 686 N 108 O 135 S 4 with estimated weight as 9.7KD, and the computed theoretical isoelectric point of the protein is 4.67, meanwhile, the instability index of the protein is estimated to be 39.96 and the grand average of hydrophobic value is -0.254 indicating that C5orf46 works as a cellular stable and hydrophilic protein. Meanwhile, as for the cellular location, C5orf46 was supported by multiple databases including Uniprot, GeneCards and Human Protein Atlas to be cellular secreted and locating in extracellular exosomes (Figure 1A), which is consistent with its computed protein stability and hydrophilic property (Figure 1B). C5orf46 was up-regulated in KIRC included multiple cancers and associated with advanced cancer grades and stages C5orf46 gene expression in broad spectrum cancers and corresponding normal control samples were investigated, and the results indicated that C5orf46 was statistical significantly up-regulated in various cancer types expect for PAAD (Figure 1C). In PAAD, the gene seemed to express lower in the cancer comparing to normal samples even though the difference was not statistical significant (Figure 1D). And of the up-regulated cancers, KIRC was tend to be the top cancer with highest expression difference between cancer and corresponding normal tissues, and the tendency was also validated using local hospital cancer samples based on IHC experiment (Figure 2A-2D). Although the gene expression was also aberrant up-regulated in other cancers, the difference was not as high as in KIRC, including in other kidney cancers for instance KIRP and KICH (Figure 2E, 2F). Meanwhile, as for the association with KIRC clinical pathological features, C5orf46 was indicated by TCGA data to tend to express higher in male patients than female ones (Figure 2G), and the up-regulated expression was associated with more advanced cancer grades and stages (Figure 2H, 2I). Moreover, the gene expression was also higher in cases with nodal metastasis, although the difference was not statistical significant which might be due to the limited case number in the metastatic group (Figure 2J). The association between C5orf46 expression and KIRC clinical features were also validated using local hospital samples, which also revealed the similar tread between C5orf46 expression and patients gender, ISUP Grade as well as bone metastasis (the main metastasis site for local KIRC samples). Meanwhile, no statistical significant correlation was observed between C5orf46 and patients age, tumor size, membrane and renal sinus invasion and other clinical features (Table 1). As for the other cancers, similar association has also been discovered between C5orf46 gene and the clinical features in multiple cancers, for instance, higher gene expression was related with more advanced cancer stages in READ, BLCA and KICH (Figure 1E, 1G, 1H), and also it expressed higher in cases with nodal metastasis in BLCA (Figure 1F). Interestingly, similar trends were observed between C5orf46 and PAAD clinical features which cancer was revealed by previous analysis to be the only cancer with down regulated C5orf46 expression. C5orf46 expression was also higher in more advanced PAAD stages and grades. Even though the expression in Grades 4 and stage 4 seemed to be lower than Grade 3 and stage 3 (Figure 1I-1K), the difference was not statistical significant and it might be the result from the limited samples cases in the 4 th group. The similar trend with cancer stages and grades in PAAD as in the KIRC included other cancers indicating C5orf46 works as an oncogene in these cancers. Higher expression of C5orf46 was correlated with worse patients survival in cancers Besides the association with advanced cancers grades and stages, the direct prognosis indicating value of C5orf46 gene was next step analyzed. The results indicated that higher expression of the gene was associated with statistical significantly worse overall survival (OS) and shorter recurrence free survival (RFS) in nearly all cancers including KIRC (Figure 2K, 2L) and others for instance CESC, STAD, PAAD and KIRP (Figure 3A-3C). The consistent results of the association with both advancing cancer stages and worse patients survival including in PAAD assisted previous speculation that C5orf46 works as a potential oncogene in broad spectrum human cancers. C5orf46 was correlated with the cancer metastasis status Cancer metastasis has been a major reason for poor patients prognosis, and for evaluating the association between C5orf46 and cancer metastasis, IHC experiment was applied on KIRC included cancer samples. And the results revealed that C5orf46 expression was not only higher in KIRC cancer cells comparing to corresponding normal kidney cells (Figure 4A), the gene expression was higher in bone (Figure 4B) and lung metastatic (Figure 4C) KIRC cancer cells comparing to surrounding remained normal bone and lung tissues. Meanwhile, besides epithelium originated cancers, for preliminary detecting C5orf46 expression in mesenchymal tissues originated tumors, tissue microarray made of GCTB were also applied to detect C5orf46 expression (Figure 4D), and the results revealed that the gene expression was higher in tumor lesions (Figure 4E, 4F) comparing to surrounding normal soft tissues (Figure 4G, 4H), although the gene was seemed to diffusely distributed with no difference in tumor or immune cells. Altered C5orf46 expression in cancers was partly attributed to DNA methylation regulation For exploring the potential reason for the altered C5orf46 gene expression in KIRC included human cancers, the methylation level of the gene was investigated based on UALCAN platform. And the results revealed that expect for PAAD which showed that C5orf46 gene methylation was higher in cancer comparing to normal tissues which trend was actually in consistent with the lower gene expression in cancer, as for KIRC included other cancers for instance BRAC, LUAD and LUSC, the gene methylation was widely lower in cancers comparing with corresponding normal samples (Figure 5A-5N). The results indicated gene methylation accounts for at least part of the up-regulated expression of C5orf46 in human cancers. Meanwhile, as for mesenchymal tissue originated sarcomas, the gene methylation level was not statistical significantly different in tumor comparing to normal tissues (Figure 5O) indicating a much more complex regulation mechanism in this kind of tumors. Other genetic alterations of C5orf46 gene in cancers Besides mRNA expression, for comprehensively understanding C5orf46 gene status in cancers, other genetic alterations including gene mutation, protein structure variant and copy number variation were investigated based on cBioPortal database. And the results revealed that gene amplification was one of the main types of C5orf46 alterations in cancers, especially in KIRC that gene amplification has been the only type of alteration being detected so far (Figure 5P). Meanwhile, as for the other cancers, a certain percent of deletion and genetic mutations which were mostly single nucleotide variations were also discovered in several types of cancers including skin cutaneous melanoma, diffuse large B-cell lymphoma, uterine corpus endometrioid carcinoma, cervical squamous cell carcinoma, head and neck squamous cell carcinoma and esophageal adenocarcinoma (Figure 5Q). C5orf46 centering Protein-protein interaction (PPI) network construction and functional enrichment analysis To preliminary explore the potential mechanism of C5orf46 regulation on KIRC included cancers development, STRING was used to construct the C5orf46 gene centering PPI network followed by genes enrichment analysis, thus uncovering the probable signaling pathways C5orf46 and its interacting genes involved in. The results actually showed consistent direction as current understanding of C5orf46 as an exosome containing gene, that it mainly participates in biological processes such as cell communication, secretion and chemical synaptic transmission (Figure 6A-6D). The results supported further deeper and in detail exploration of the correlation between C5orf46 and currently acknowledged exosomes related biological cellular effects. C5orf46 related with TME angiogenesis TME angiogenesis has been a major aspect during tumor development and metastasis, to validate the association between C5orf46 gene and tumor angiogenesis, we selected 32 angiogenesis related genes and analyzed their association with C5orf46 expression in various cancers, and the results showed a statistical significant positive correlation between the gene and KIRC included multiple cancers for instance OV, BLCA and COAD (Figure 7A-7G). Meanwhile, IHC experiment that was conducted on KIRC tissue microarray also revealed that although C5orf46, CD31 and CD34 expressions vary in different cancer samples, the C5orf46 expression was indeed associated with the number of both CD31 and CD34 stained blood vessels. We took a case with obvious heterogeneous C5orf46 expression as example (Figure 7H), in the area with high C5orf46 expression, both the number of CD31 stained immature blood vessels (Figure 7I) and CD34 marked mature blood vessels (Figure 7J) were higher that in the area with low C5orf46 expression, supporting the potential effect C5orf46 has on TME angiogenesis. The colocalization of C5orf46 with CD31/CD34 haven’t been included in the study yet due to the regent and equipment limitation in our laboratory, and also KIRC has been well known as a typical cancer abundant with blood sinuses, not only the number of blood vessels, but also the vessel lumen structure, size and morphology shall effect on cancer development, more detailed experiments and analysis are still needed for further investigating the effect C5orf46 has on KIRC blood vessels remodeling. C5orf46 correlated with ECM degradation and EMT transition in metastasis process of cancers Besides angiogenesis, ECM degradation has also been an important aspect of TME remodeling which was not only a critical step for cancer metastasis, but also a major biological process that reacts on oncogenes containing exosomes regulation. For investigating the association between C5orf46 and ECM degradation, 23 ECM degradation related genes were selected, and the analysis result revealed significant positive correlation in various cancers including OV, BLCA, LUSC and COAD (Figure 8B-8G), however, the association in KIRC was revealed to be not statistical significant (Figure 8A). Meanwhile, similar correlation trends was also observed between C5orf46 and cancer EMT transition. Although positive correlation were revealed in various cancers between C5orf46 gene and the signature composed of 14 EMT related genes, for instance in OV, BLCA, PAAD and COAD (Figure 8L-8Q), the correlation in KIRC was not statistical significant neither (Figure 8K). The significant correlation between the gene and multiple important cancer traits shall provide promising insights for further detailed investigation of the gene role on specific cancer progresses. Association between C5orf46 and cancer stemness index Cancer stemness index is a recently developed indicator which is calculated based on genes methylation and expression data for evaluating the similarity between tumor cells and stem cells. In the study, we calculated the mRNAsi in different cancers based on TCGA pan-cancer genes expression data (Figure 9A), followed by analyzing its correlation with C5orf46 gene expression, and the results revealed that mRNAsi index score differed significantly between high C5orf46 expression and low expression groups in KIRC included various cancers (Figure 9B-9L). Interestingly, similar trend was observed among various cancers that C5orf46 gene was negatively correlated with the mRNAsi index, namely the index was higher in low C5orf46 expression groups, indicating a uniform effect C5orf46 has on the stemness of the cancers. Association between C5orf46 and HRR signature as well as MRR status analysis Considering DNA repair has been a major mechanism for maintaining cancer genome stability, which also contributes to the cancers stemness maintenance, we further analyzed the association between C5orf46 and 27 commonly known HRR genes, and the results revealed that only mild interaction has been observed (Figure 10A-10H), indicating C5orf46 might not be a main regulator for cancers DNA deficiency nor HRR DNA repair. Besides HRD, given MRR has also been another well known DNA deficiency repair system, the association between C5orf46 expression and cancer MMR status were also preliminary explored. In the study, we included in 5 cases of COAD cancer samples which has been a common cancer type with high dMMR ratio, and based on the IHC experiment of four MMR proteins MLH1, PMS2, MSH2 and MSH6, two cases were revealed to be MSI, meanwhile, the other three cases were MSS (Figure 10I). We compared the relative C5orf46 expression between the two MSI samples and three MSS cases, and non statistical significance was discovered (Figure 10J). And even between the two cases with MSI status, the MMR proteins expression were different, one with MLH1, PMS2 loss of expression (Figure 10K, 10L), meanwhile, the other with MSH2 and MSH6 loss of expression (Figure 10M, 10N). Based on current results, none specific correlation was supported between C5orf46 and cellular DNA deficiency repair system, although further deeper analysis with bigger sample number covering more cancer types would help validating the conclusion. The positive correlation with specific cancer traits including EMT, ECM degradation and TME angiogenesis, meanwhile negative correlation with cancer stemness index and none specific correlation with DNA repair might reflects the diversified functions of exosomal genes in cellular biological processes. C5orf46 correlates with cancers immune landscape especially cytotoxic T lymphocytes infiltration To investigate the effect C5orf46 gene has on cancers immune infiltration, the 22 tumor infiltration immunocytes (TIC) landscape in different types of cancers were calculated based on CIBERSORT database, followed by analyzing its relationship with C5orf46 expression. And as indicted in the distribution heatmap, strong positive correlations between C5orf46 and macrophages were observed in multiple cancers including BRCA, COAD, KICH, LUSC, OV, PAAD, READ and so on. Meanwhile, negative correlations were displayed with CD4+ T cell and CD8+T cells in above cancers. As for the association with endothelial cells, the distribution various in different cancers, indicating a cancer-specific function mode (Figure 11A). Considering CD8+T cells have been an important immune cell type playing significant cancer killing function, the association between the gene and CD8+T cells distribution was mainly validated using KIRC tissue microarray (the same as previous described, Figure 11B, 11C). And what is controversial is that the association between C5orf46 gene and CD8+T cells distribution in KIRC was only stochastic. Although the level of CD8+T cells infiltration vary in different samples, it seems inconsistent with C5orf46 expression level (Figure 11D, 11E), especially in the same case with heterogeneous C5orf46 expression that was previously applied to observe angiogenesis, the CD8+T cells infiltration was non statistical different in the areas with higher or lower C5orf46 expression (Figure 11F). Meanwhile, given the dysfunction of cytotoxic T lymphocyte (CTL) has been a major aspect during tumors immunosuppression, we also investigated C5orf46 association with CTL functions based on TIDE analysis. And the analysis result revealed that CTL dysfunction level were different between high-C5orf46 expression and low expression samples in multiple cancers including lung cancers, melanoma, colorectal cancer, invasive breast cancer and pancrestic cancers, but not in KIRC (Figure 12A-12F), indicating C5orf46 indeed has potential value in predicting CTL functions in cancers, but it works a cancer-specific mode, which means it might only functions in certain types of cancers. Further deeper analysis with bigger sample number covering more cancer types would help validating the conclusion. Tumor mutation burden (TMB), microsatellite instability (MSI) and immune therapy sensitivity analysis TMB and MSI have been not only well acknowledged clinical evaluation indicators of cancers immune microenvironment, but also widely used biomarkers for predicting patients sensitivity for immune therapies. Despite the significant correlation between C5orf46 and immune cells distribution, the analysis of association with TMB and MSI revealed that C5orf46 affects only mildly of the MSI status in CHOL, as for the other cancers, none significant correlation were observed in neither TMB nor MSI analysis (Figure 12G, 12H). Meanwhile, consistent results have been revealed in the immune therapy drugs sensitivity analysis which showed that none statistical significant expression difference of C5orf46 has been observed between the responders and non-responders of patients after receiving immune therapies drugs including anti PD-1, PD-L1 and CTLA4 inhibitors (data not shown). The results indicated that although relates with the immune infiltration landscape in cancers, C5orf46 might not be a potential drug target for immune therapy. C5orf46 affects drugs sensitivity in certain chemotherapy Drug sensitivity has been a critical and also insurmountable problem in clinic cancer treatment, despite the none specific correlation with immune therapy drugs, to explore whether C5orf46 would be a potential indicator to predict chemotherapeutic responses of cancers, ROCplotter database was accessed to evaluate the association between C5orf46 and the therapeutic outcomes in certain cancer types. The results revealed that in BRCA, C5orf46 expression was significantly higher in the non-responders comparing to responders after endocrine therapy using aromatase inhibitors as well as after anti-HER2 therapy using trastuzumab (Figure 13A-13D). Similar trends were observed in ovary and colorectal cancer patients that C5orf46 expression was higher in non-responders comparing to responders after chemotherapy using platin, taxane, 5-fluorouracil, oxaliplatin and fluoropyrimidines (Figure 13E-13L). Additionally, based on RNAactDrug platform, the top 8 anti-C5orf46 small molecular compounds with FDR < 0.05 were also displayed (Table 2). Although deeper experiments validation and clinical trials were needed before clinical application of these drugs, the results shall provide promising insights for further researches. Discussion Cancer has been a major health threat for people, and the development of cancer involves not only the genetic alterations for instance oncogenes activation and tumor inhibiting genes mutations, but also involving intensive interaction between cancer cells and resident non-cancerous cells in TME[ 30 ]. And the interaction modes among cancers and surrounding TME cells includes direct cell-cell contact and paracrine signaling, during which process the release of extracellular vesicles (EVs), for instance exosomes has been an important paracrine mechanism for cell communicating. Exosomes have been gradually revealed to play diversified functions in cancer development, for instance inducing normal cells malignant transformation, participating in TME regulation, influencing the drug resistance of tumor cells, as well as regulating the premetastatic niche of tumors[ 31 , 32 ]. In the study, we mainly focused on a recently discovered tumor regulation related gene C5orf46, the gene firstly exposed to us from previous studies that when we analyzing the different expressed genes in primary cancers comparing to responding normal control samples, the gene was generated repeatedly in multiple types of cancers for instance lung cancer[ 33 ], pancreatic cancer[ 34 ] and even osteosarcoma[ 35 ]. It certainly provoked our interest, although we soon discovered that up to now, only very limited functions have been reported about the gene. A pan-cancer analysis of the gene shall provide meaningful direction for further detailed researches of the gene’s role in the initiation and development of different types of cancers. The study was started with the investigation of the basic physiochemical property of C5orf46, the computed molecular weight, hydrophobicity/hydrophilicity feature as well as instability index all supported C5orf46 working as a cellular stable and hydrophilic protein, and the gene was supported by multiple databases to be mainly being secreted and locating in extracellular exosomes. However, the main limitation of the current validation is that as a potential exosome containing gene, the exosomes hasn’t been detected using electron microscopy because of the equipment limitation in our laboratory, this part of experimental validation hasn’t been included in the study. After basic understanding of the physiochemical property of C5orf46, the gene expression patterns as well as prognosis correlation were comprehensively investigated based on TCGA pan-cancer data. We encouragingly discovered that C5orf46 expression was widely up-regulated in broad spectrum human cancers especially in KIRC which was indicated to be the top cancer with highest C5orf46 expression difference between cancer and normal tissues. And IHC experiment using local hospital KIRC cancer samples supported the gene not only expressed high in cancers comparing to corresponding normal tissues, but also the gene associated with multiple cancer clinical features including more advanced cancer stages, grades and metastatic status. Meanwhile, the higher gene expression was indicated to statistical significantly correlates with worse patients OS and RFS in various cancers. For preliminary exploring the potential reason of the up-regulated expression of C5orf46 in cancers, DNA methylation level was then evaluated, and we discovered that Co5orf46 gene methylation level was lower in various cancers comparing to corresponding normal control samples expect for in PAAD, the results supported that gene methylation be a main regulation of C5orf46 gene and accounted for at least part of gene’s altered expression in human cancers. Besides mRNA expression, other types of genetic alterations including mutation ratio, protein structure variant and copy number variations that commonly affect gene functions were also analyzed, and the result indicated that although a certain percent of deletion and single nucleotide mutations were discovered, the gene amplification and proteion gain of expression shall be one of the the major alteration types of C5orf46 in human cancers. Further, for investigating the potential role of C5orf46 in cancers, the PPI network centering on the gene was constructed followed by preliminary analyzing the main enrichment of the related surrounding interacting genes. Afterwards, the association between C5orf46 and multiple important clinical cancers traits including TME angiogenesis, ECM structures, tumor transition EMT and immune modulation were in succession analyzed. Firstly, considering tumor microenvironment angiogenesis has been a well acknowledged important process for tumor growth and metastasis[ 36 , 37 ], multiple critical elements including the known vascular endothelial growth factor (VEGF), VEGF receptor (VEGFR), fibroblast growth factor (FGF), platelet derived growth factor (PDGF), transforming growth factor β (TGF-β) have been acknowledged to play roles in the process[ 38 – 40 ], thus the association between C5orf46 and angiogenesis was mainly focused. As a matter of fact, exosomes from cell lines or plasma sources of various human tumors for instance glioblastoma, pancreatic cancer and nasopharyngeal carcinoma have been reported to be able to effectively induce angiogenesis in vitro and in vivo[ 41 , 42 ]. In the study, we discovered that exosomal C5orf46 also related with TME angiogenesis, statistical significant positive correlation have been observed in KIRC included various cancers, for instance COAD, CESC, KIRC, LUSC, LUAD and READ. However, as KIRC has been known as a typical cancer abundant with blood sinuses, not only the number of blood vessels, but also the vessel lumen structure, size and morphology shall effect on cancer development, more detailed experiments are still needed for validating the association between C5orf46 and KIRC angiogenesis. Besides angiogenesis, ECM degradation has also been an important aspect of TME remodeling[ 43 , 44 ]. The association analysis between C5orf46 and ECM degradation revealed statistical significant positive correlations in KIRC included multiple cancers for instance BLCA, BRCA, KIRP, PAAD, HNSC, CHOL, SKCM, THCA and LIHC. Additionally, considering ECM degradation is not only a type of TME structures remodeling, but also a critical step for cancer metastasis, to be more comprehensively understanding the potential effect C5orf46 has on cancer metastasis, we also analyzed the association between the gene and 29 cytoskeleton dynamics related genes that were related with actin filaments stabilization, F-actin polymerization and actin-myosin contractile force generation, but only barely mild correlation was detected (data not shown). Moreover, based on the fact that EMT is also an important step in cancer formation which is characterized by the loss of E-cadherin expression ,gain of N-cadherin as well as regulators for instance snail, proteases, and some totipotent transcription factors expression[ 45 – 47 ]. In the study, we selected 14 genes that were well known to be EMT related and analyzed their correlation with C5orf46, and discovered statistical significant positive correlation in multiple cancers including BLCA, BRCA, CESC, CHOL, HNSC, COAD, KIRP, and LUSC, however, the correlation was not significant in KIRC. Meanwhile, as for the DNA deficiency and repair system, none specific correlation was observed between C5orf46 gene expression and cancers HRR related gene signatures. Increasing studies have been revealing that exosomes participates in tumor immune escape by delivering its contained molecules, such as proteins, mRNA, and miRNA to certain receptor TME cells[ 48 ]. For instance, exosomes from lung cancer cells have been reported to induce immune escape by reducing T cell activity, expressing PD-L1, and promoting tumor growth. In the study, the potential effect of C5orf46 has on cancers immune infiltration landscape was explored. And the result revealed that C5orf46 strongly correlated with the distribution of multiples TICs, most significantly macrophages, CD4 + T cell and CD8 + T cells. Although the results are still in dispute in KIRC, the observed CTL dysfunction level difference between high-C5orf46 expression and low expression samples in multiple cancers indicating the potential regulation F5orf46 gene has on certain types of cancers immune environment modulation. Further, for preliminary evaluating the potential of C5orf46 as a probable drug target, we explored the gene expression association with cancer patients response to certain therapies, and discovered that although C5orf46 correlates with the TMB and MSI status in some cancers, none statistical significant expression difference of gene has been observed between the responders and non-responders of patients after receiving immune therapies drugs including anti PD-1, PD-L1 and CTLA4 inhibitors. Meanwhile, as for the chemotherapy drugs, C5orf46 expression was significantly different in the non-responders comparing to responders after receiving certain drugs in BRCA, colorectal cancer and GBM. Although deeper in vitro experiments validation and clinical trials were needed before clinical application of these drugs, the results shall provide promising insights for further clinical researches of C5orf46 gene functions in cancers. Conclusion In conclusion, based on TCGA pan-cancer data and certain local hospital patients samples, the complex and comprehensive roles of C5orf46 gene in cancers were preliminary explored. C5orf46 expression was aberrant up-regulated in various cancers which correlates directly with worse patients OS and shorter RFS. Meanwhile, the gene was also supported to participate in the regulation of TME angiogenesis, ECM degradation, EMT transition as well as cancer stemness. Significantly, C5orf46 was also involved in cancer immunity and might work as a potential biomarker for certain chemotherapy drugs sensitivity, the results shall provide meaningful insights for better understanding the molecular mechanism behind C5orf46 regulation on cancers development. Abbreviations Abbreviations for TCGA cancer types: Bladder urothelial carcinoma (BLCA), Breast invasive carcinoma (BRCA), Colon adenocarcinoma (COAD), Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), Cholangio carcinoma (CHOL), Esophageal carcinoma (ESCA), Glioblastoma multiforme (GBM), Head and neck squamous cell carcinome (HNSC), Kidney renal papillary cell carcinoma (KIRP), Kidney clear cell carcinoma (KIRC), Pancreatic adenocarcinoma (PAAD), Liver hepatocellular carcinoma (LIHC), Lung squamous cell carcinoma (LUSC), Lung adenocarcinoma (LUAD), Thymoma (THYM), Ovarian serous cystadenocarcinoma (OV), Mesothelioma (MESO), Pheochromocytoma and paraganglioma (PCPG), Prostate adenocarcinoma (PRAD), Rectum adenocarcinoma (READ), Stomach adenocarcinoma (STAD), Thyroid carcinoma (THCA), Uterine corpus endometrial carcinoma (UCEC), Uterine carcinosarcoma (UCS). Other abbreviations: Protein-protein interaction network (PPI), Overall survival rate (OS), Recurrence free survival rate (RFS), Extra cellular matrix (ECM), Tumor microenvironment (TME), Epithelial Mesenchymal Transition (EMT), Homologous Recombination Repair (HRR), Tumor infiltrating cell (TIC), Cytotoxic T lymphocytes (CTLs), Tumor mutation burden (TMB), Microsatellite instability (MSI). Declarations Ethnic approval and consent to participate All of the local hospital patients samples that were used for IHC experiments were obtained from hospital BioBank (Second Hospital of ShanXi Medical University, ShanXi Province, China). Informed consent with signed signatures of the potential scientific application of the samples have been obtained from patients at the same time they made the donation to BioBank. The certain number of Biobank samples that were used in this study was approval by Hospital Institutional Board (Second Hospital of ShanXi Medical University, ShanXi Province, China). All methods were carried out in accordance with relevant guidelines and regulations or declaration of Helsinki. Clinical Trial Number Not applicable Consent for publication Not applicable Availability of data and materials TCGA pan-cancer profiles were analyzed in the study, which was downloaded from UCSC Xena (https://www.cancer.gov/ccg/research/genome-sequencing/tcga). All data generated or analyzed during this study are included in the article and supplementary files. Competing interests All of the authors agreed the publication of the paper and declare no conflicts of interests. Funding The work was supported by China central government funds for guiding local scientific and technological development (YDZJSX2021A042), the Science project from Health Commission of ShanXi Province (2023103) and grants of Natural Science Foundation of ShanXi Province in China (202203021222393, 202303021222333, 202403021211135). Authors' contributions XW and JL designed the study and drafted the manuscript, contributed equally to the whole study. HY, FW, LM, SL and NS performed the data collecting and analysis. ZY and LG participated in data interpretation and study design, WM and CW were involved in the drafting and critical revision of manuscript. As the corresponding authors, both WM and CW have full access to all data of the manuscript, CW made the eventual decision to submit the article for publication. All authors read and approved the final manuscript. Acknowledgements We sincerely acknowledge TCGA database for providing the platform and the rich data and information resources for cancers analysis, we also appreciate the science projects from worldwide for uploading their meaningful data on the platform. References de Visser KE, Joyce JA: The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth . Cancer Cell 2023, 41 (3):374-403. Wang JJ, Lei KF, Han F: Tumor microenvironment: recent advances in various cancer treatments . Eur Rev Med Pharmacol Sci 2018, 22 (12):3855-3864. Zhang Y, Han X, Nie G: Responsive and activable nanomedicines for remodeling the tumor microenvironment . Nat Protoc 2021, 16 (1):405-430. Sarode P, Schaefer MB, Grimminger F, Seeger W, Savai R: Macrophage and Tumor Cell Cross-Talk Is Fundamental for Lung Tumor Progression: We Need to Talk . Front Oncol 2020, 10 :324. Carnevalli LS, Ghadially H, Barry ST: Therapeutic Approaches Targeting the Natural Killer-Myeloid Cell Axis in the Tumor Microenvironment . Front Immunol 2021, 12 :633685. Bejarano L, Jordao MJC, Joyce JA: Therapeutic Targeting of the Tumor Microenvironment . Cancer Discov 2021, 11 (4):933-959. Zhou L, Lv T, Zhang Q, Zhu Q, Zhan P, Zhu S, Zhang J, Song Y: The biology, function and clinical implications of exosomes in lung cancer . Cancer Lett 2017, 407 :84-92. Aghabozorgi AS, Ahangari N, Eftekhaari TE, Torbati PN, Bahiraee A, Ebrahimi R, Pasdar A: Circulating exosomal miRNAs in cardiovascular disease pathogenesis: New emerging hopes . J Cell Physiol 2019, 234 (12):21796-21809. Deep G, Panigrahi GK: Hypoxia-Induced Signaling Promotes Prostate Cancer Progression: Exosomes Role as Messenger of Hypoxic Response in Tumor Microenvironment . Crit Rev Oncog 2015, 20 (5-6):419-434. Zhang HG, Grizzle WE: Exosomes and cancer: a newly described pathway of immune suppression . Clin Cancer Res 2011, 17 (5):959-964. de Freitas RCC, Hirata RDC, Hirata MH, Aikawa E: Circulating Extracellular Vesicles As Biomarkers and Drug Delivery Vehicles in Cardiovascular Diseases . Biomolecules 2021, 11 (3). Jiang Y, Wang X, Li L, He J, Jin Q, Long D, Liu C, Zhou W, Liu K: A systematic analysis of C5ORF46 in gastrointestinal tumors as a potential prognostic and immunological biomarker . Front Genet 2022, 13 :926943. Ma M, Zhang Z, Liu Y, Li Z, Fu S, Chen Q, Wang S: Preliminary study on the role of the C5orf46 gene in renal cancer . Transl Oncol 2022, 21 :101442. Zhou YJ, Liu JM, Liu B, Wang ZX, Fan XY, Huang PZ, Huang YX, Sun JN, Chen QQ, Shen HM: [Significance of high expression of C5orf46 in gastric cancer and potential intervention of tarditional Chinese medicine based on bioinformatics, molecular docking, and cell experiments] . Zhongguo Zhong Yao Za Zhi 2023, 48 (9):2368-2378. TCGA-UCSC software . In: https://xenabrowsernet/. Accessed 07 Apr 2022. ProtParam software . In: https://webexpasyorg/protparam/. Accessed 21 Apr 2023. ProtScale software . In: https://webexpasyorg/protscale/. Accessed 27 Apr 2023. Uniprot software . In: https://wwwuniprotorg/. Accessed 25 Apr 2023. GeneCards software . In: https://wwwgenecardsorg/. Accessed 09 May 2023. HumanProteinAtlas software . In: https://wwwproteinatlasorg/. Accessed 23 May 2023. UALCAN software . In: http://ualcanpathuabedu/analysishtml. Accessed 24 May 2023. Kalpan-Meier software . In: http://kmplotcom/analysis/. Accessed 11 Jun 2023. cBioPortal software . In: https://wwwcbioportalorg/. Accessed 13 Jun 2023. STRING software . In: https://string-dborg/. Accessed 27 Jun 2023. Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN, Kaminska B, Huelsken J, Omberg L, Gevaert O et al : Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation . Cell 2018, 173 (2):338-354 e315. Liang X, Zhang H, Wang Z, Zhang X, Dai Z, Zhang J, Luo P, Zhang L, Hu J, Liu Z et al : JMJD8 Is an M2 Macrophage Biomarker, and It Associates With DNA Damage Repair to Facilitate Stemness Maintenance, Chemoresistance, and Immunosuppression in Pan-Cancer . Front Immunol 2022, 13 :875786. TIDE software . In: http://tidedfciharvardedu/login/. Accessed 02 Aug 2023. ACLBI software . In: https://wwwaclbicom/static/indexhtml#/. Accessed 09 Aug 2023. ROC-Plotter software . In: https://wwwrocplotorg/. Accessed 17 Aug 2023. Hanahan D, Weinberg RA: Hallmarks of cancer: the next generation . Cell 2011, 144 (5):646-674. Dominiak A, Chelstowska B, Olejarz W, Nowicka G: Communication in the Cancer Microenvironment as a Target for Therapeutic Interventions . Cancers (Basel) 2020, 12 (5). van Niel G, Carter DRF, Clayton A, Lambert DW, Raposo G, Vader P: Challenges and directions in studying cell-cell communication by extracellular vesicles . Nat Rev Mol Cell Biol 2022, 23 (5):369-382. Wei R, Wang Z, Zhang Y, Wang B, Shen N, E L, Li X, Shang L, Shang Y, Yan W et al : Bioinformatic analysis revealing mitotic spindle assembly regulated NDC80 and MAD2L1 as prognostic biomarkers in non-small cell lung cancer development . BMC Med Genomics 2020, 13 (1):112. Wei R, Qi G, Zeng Z, Shen N, Wang Z, Shen H, Gao L, Song C, Ma W, Wang C: IMUP and GPRC5A: two newly identified risk score indicators in pancreatic ductal adenocarcinoma . Cancer Cell Int 2021, 21 (1):620. Wang Z, Zeng Z, Gao F, Gui Z, Du J, Shen N, Shang Y, Yang Z, Shang L, Wei R et al : Osteosarcoma transcriptome data exploration reveals STC2 as a novel risk indicator in disease progression . BMC Med Genomics 2023, 16 (1):30. Lee SH, Jeong D, Han YS, Baek MJ: Pivotal role of vascular endothelial growth factor pathway in tumor angiogenesis . Ann Surg Treat Res 2015, 89 (1):1-8. Hicklin DJ, Ellis LM: Role of the vascular endothelial growth factor pathway in tumor growth and angiogenesis . J Clin Oncol 2005, 23 (5):1011-1027. Momeny M, Sabourinejad Z, Zarrinrad G, Moghaddaskho F, Eyvani H, Yousefi H, Mirshahvaladi S, Poursani EM, Barghi F, Poursheikhani A et al : Anti-tumour activity of tivozanib, a pan-inhibitor of VEGF receptors, in therapy-resistant ovarian carcinoma cells . Sci Rep 2017, 7 :45954. Lorenc P, Sikorska A, Molenda S, Guzniczak N, Dams-Kozlowska H, Florczak A: Physiological and tumor-associated angiogenesis: Key factors and therapy targeting VEGF/VEGFR pathway . Biomed Pharmacother 2024, 180 :117585. Monaci S, Coppola F, Filippi I, Falsini A, Carraro F, Naldini A: Targeting hypoxia signaling pathways in angiogenesis . Front Physiol 2024, 15 :1408750. Skog J, Wurdinger T, van Rijn S, Meijer DH, Gainche L, Sena-Esteves M, Curry WT, Jr., Carter BS, Krichevsky AM, Breakefield XO: Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers . Nat Cell Biol 2008, 10 (12):1470-1476. Gesierich S, Berezovskiy I, Ryschich E, Zoller M: Systemic induction of the angiogenesis switch by the tetraspanin D6.1A/CO-029 . Cancer Res 2006, 66 (14):7083-7094. Najafi M, Farhood B, Mortezaee K: Extracellular matrix (ECM) stiffness and degradation as cancer drivers . J Cell Biochem 2019, 120 (3):2782-2790. Jablonska-Trypuc A, Matejczyk M, Rosochacki S: Matrix metalloproteinases (MMPs), the main extracellular matrix (ECM) enzymes in collagen degradation, as a target for anticancer drugs . J Enzyme Inhib Med Chem 2016, 31 (sup1):177-183. Jihu Y, Leng R, Liu M, Ren H, Xie D, Yao C, Yan H: Angiotensin (1-7) Inhibits Transforming Growth Factor-Beta1-Induced Epithelial-Mesenchymal Transition of Human Keratinocyte Hacat Cells in vitro . Clin Cosmet Investig Dermatol 2024, 17 :1049-1058. Gao X, Lin X, Lin M, Lan Y, Wang Y, Wu R, Li J, Huang C, Zhong D: Silencing Rac1 and Prex1 Inhibit Epithelial-Mesenchymal Transition in Human Gastric Cancer Cells Induced by Transforming Growth Factor-beta1 . Turk J Gastroenterol 2023, 34 (9):975-981. Shrestha R, Bridle KR, Crawford DHG, Jayachandran A: Immune checkpoint molecules are regulated by transforming growth factor (TGF)-beta1-induced epithelial-to-mesenchymal transition in hepatocellular carcinoma . Int J Med Sci 2021, 18 (12):2466-2479. Romeo E, Caserta CA, Rumio C, Marcucci F: The Vicious Cross-Talk between Tumor Cells with an EMT Phenotype and Cells of the Immune System . Cells 2019, 8 (5). Tables Table 1 A ssociation between C5orf46 expression and KIRC clinical features Parameters C5orf46 expression P Value Low expression High expression Gender male 8 (33.3%) 16 (66.7%) 0.042* female 10 (66.7%) 5 (33.3%) Age ≤40 3 (60.0%) 2 (40.0%) 0.647 >40 15 (44.1%) 19 (55.9%) WHO/ISUP grade G1/G2 14 (60.9%) 9 (39.1%) 0.027* G3/G4 4 (25.0%) 12 (75.0%) Tumor diameter ≤4cm 6 (54.5%) 5 (45.5%) 0.883 4~7cm 10 (41.7%) 14 (58.3%) >7cm 2 (50.0%) 2 (50.0%) Membrane invasion Yes 14 (48.3%) 15 (51.7%) 0.932 No 4 (40.0%) 6 (60.0%) Neurovascular invasion Yes 1 (33.3%) 2 (66.7%) 0.643 No 17 (47.2%) 19 (52.8) Renal sinus invasion Yes 2 (33.3%) 4 (66.7%) 0.497 No 16 (48.5%) 17 (51.5%) Renal pelvis invasion Yes 4 (80.0%) 1 (20.0%) 0.162 No 14 (41.2%) 20 (58.8%) T stage Ia 5 (50.0%) 5 (50.0%) 0.883 Ib 10 (63.6%) 14 (36.4%) II 2 (50.0%) 2 (50.0%) Bone metastasis Yes 4 (23.5%) 13 (76.5%) 0.013* No 14 (63.6%) 8 (36.4%) * Represents p<0.05. Table 2. The 8 compounds correlated with C5orf46 based on RNAact Drug platform Compounds Omics Source Spearman.stat Spearman.fdr 3-bromo-4-n,n-bis-2'-cyanoethylaminobenzylidene rhodanine Expression CellMiner 0.453 0.030 Panobinostat Expression CCLE 0.400 7.643e-11 sulfonaphtholazoresorcinol Methylation CellMiner 0.394 0.035 1,4-dimethoxy-7-azaisoindole[2,1-a]quinoxalin-6(5h)-one Methylation CellMiner 0.374 0.042 1,3-diphenyl-4-(3-phenyl-4,5-dihydro-1H-pyrazol-5-yl)-1 Expression CellMiner 0.373 0.039 mercury(acetyloxy)(pentamethylphenyl) Methylation CellMiner -0.390 0.018 varacin trifluoroacetate salt Expression CellMiner -0.398 0.045 8-Chloro-adenosine Expression CellMiner -0.378 0.048 Additional Declarations No competing interests reported. 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University","correspondingAuthor":false,"prefix":"","firstName":"Zhiqing","middleName":"","lastName":"Yang","suffix":""},{"id":509699972,"identity":"49ed6dee-024b-4822-b868-69cb154854ff","order_by":8,"name":"Lifang Gao","email":"","orcid":"","institution":"Second Hospital of ShanXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lifang","middleName":"","lastName":"Gao","suffix":""},{"id":509699974,"identity":"cf1c84c4-37a1-4565-a2c0-cffdbc851fad","order_by":9,"name":"Wenxia Ma","email":"","orcid":"","institution":"Second Hospital of ShanXi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenxia","middleName":"","lastName":"Ma","suffix":""},{"id":509699975,"identity":"7fe9abdc-12a3-430a-ab25-163607d5b61a","order_by":10,"name":"Chen Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYLACxgYGBn4kNpFaJBtI1mJwgFgtBsfPHn7xc4dNnvH5M6abeRhsZDccYH72AK+WM3lplr1n0orNbqSl3eZhSDPecIDN3ACfFrMDOWbGjG2HE7fdYD4G1HI4ccMBHjYJvFrOvwFp+Z+4uf9gG1DLfyK03MgxfszYdiBxA0MyyJYDhLXY33hjxtjblpw4A+iXm3MMko1nHmYzw6tFsj/H+MPPNrvE/v4zZjfeVNjJ9h1vfoZXCxAgOwMUVMwE1IOUfCCsZhSMglEwCkY0AABzwE9dc3zLowAAAABJRU5ErkJggg==","orcid":"","institution":"Second Hospital of ShanXi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Chen","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-05-13 02:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6650741/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6650741/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90790463,"identity":"24f3c3a1-659c-4493-a248-5d3d3e0991eb","added_by":"auto","created_at":"2025-09-08 08:13:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":287664,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eC5orf46 gene characteristics and association with human cancer pathological features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The prediction model of the cellular location of C5orf46 gene based on Uniprot platform. (B) The computed hydrophilcity / hydrophobicity analysis of C5orf46 encoding protein based on ProtParam and ProtScale database. (C) UALCAN prediction of C5orf46 expression in human pan-cancers comparing to corresponding normal control samples based on TCGA profiles. (D) GEPIA analysis of the C5orf46 gene expression in PAAD comparing to normal pancreatic tissues. (E)Expression of C5orf46 based on cancer stages in READ. (F) Expression of C5orf46 in BLCA based on lymph nodes metastasis status. Expression of C5orf46 based on cancer stages in (G) BLCA and (H) KICH. C5orf46 gene expression association with PAAD (I) stages, (J) patients age and (K) tumor grade. (*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001. The first layer* which is right above the error bar represents comparison to normal group, and the above layers* which were above a secondary line represent the comparison between corresponding groups that were covered by the line).\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6650741/v1/49f4adac8db3036aa6a28a84.png"},{"id":90790465,"identity":"621adb93-3f41-454a-92ca-31b7d8d31a73","added_by":"auto","created_at":"2025-09-08 08:13:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2422252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eC5orf46 gene expression in KIRC and its association with cancer clinical pathological features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIHC experiment staining of C5orf46 expression in KIRC samples which intensity was scored as (A) “none”, (B) “mild”, (C) “moderate” and (D) “strong” (all figures magnification: 20X, error bar represents 100um). GEPIA analysis of C5orf46 gene expression in (E) KIRC, (F) KIRP and KICH comparing to corresponding normal tissues. Expression of C5orf46 in KIRC based on (G) patients gender, (H) tumor grade, (I) cancer stages and (J) nodal metastasis status (*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001. The first layer* which is above the error bar represents comparison to normal group, and the above layers *which were above a secondary line represent the comparison between corresponding groups that were covered by the line). (K) The overall survival and (L) recurrence free survival analysis of C5orf46 in KIRC.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6650741/v1/5eb74333d638949db3b5cd5c.png"},{"id":90790464,"identity":"0947a841-d410-4862-bba2-f56bc645b8fd","added_by":"auto","created_at":"2025-09-08 08:13:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":165807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between C5orf46 gene expression and pan-cancer patients survival\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) C5orf46 gene association with patients survival risk in pan-cancer.\u003c/p\u003e\n\u003cp\u003e(B) The association between C5orf46 and patients overall survival in different types of cancers. (C) The association between C5orf46 and patients recurrence free survival in different types of cancers. (p\u0026lt;0.05 was considered statistical significant.)\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6650741/v1/81138b4df889d96000e4311b.png"},{"id":90791242,"identity":"62e904f7-ea8b-4290-aa79-cc024b63d743","added_by":"auto","created_at":"2025-09-08 08:21:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4442294,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIHC detection of C5orf46 expression in primary and metastatic KIRC samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Expression of C5orf46 in KIRC cancers comparing to corresponding normal kidney cells (The middle graph in the panel contains cancer and surrounding normal tissues, magnification: 40X, error bar represents 300um. The nearer left and right graph represents normal kidney and cancer lesion respectively, magnification: 100X, error bar represents 100um. The further left and right graph represents the magnification of the nearer graph, magnification: 200X, error bar represents 60um.\u003c/p\u003e\n\u003cp\u003e(B) Expression of C5orf46 in bone metastatic KIRC cancers (The middle graph contains cancer and surrounding remained bone tissues, magnification: 40X, error bar represents 300um. The nearer and further left and right graph represents cancer and surrounding remained bone tissues, magnification: 100X and 200X, error bar represents 100um and 60um respectively.\u003c/p\u003e\n\u003cp\u003e(C) Expression of C5orf46 in lung metastatic renal cancers (The middle graph contains cancer and surrounding remained lung tissues, magnification: 40X, error bar represents 300um. The nearer and further left and right graph represents cancer and surrounding remained lung tissues, magnification: 100X and 200X, error bar represents 100um and 60um respectively.\u003c/p\u003e\n\u003cp\u003e(D) A tissue microarray made of giant cell tumor of bone samples for preliminary detecting C5orf46 expression in soft tissue originated tumors.\u003c/p\u003e\n\u003cp\u003e(E) Magnification of a tumor tissue based on (D) microarray, and IHC experiment was conducted on it.\u003c/p\u003e\n\u003cp\u003e(F) IHC experiment detection of C5orf46 expression in a case of giant cell tumor of bone (magnification: 100X and 200X, error bar represents 100um and 60um in the left and right graph respectively).\u003c/p\u003e\n\u003cp\u003e(G) Magnification of a surrounding normal soft tissue based on (D) microarray, and IHC experiment was conducted on it.\u003c/p\u003e\n\u003cp\u003e(H) IHC experiment detection of C5orf46 expression in surrounding normal soft tissues (magnification: 100X and 200X, error bar represents 100um and 60um in the left and right graph respectively).\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6650741/v1/3d514c83bc2eb658eaece9c6.png"},{"id":90790469,"identity":"fdec2568-0c38-4f6e-aded-33394c42800e","added_by":"auto","created_at":"2025-09-08 08:13:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116856,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePromoter methylation of C5orf46 and the gene variations in cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe promoter methylation level C5orf46 gene in different types of cancers, including in (A) PAAD, (B) BRCA, (C) TGCT, (D) UCEC, (E) HNSC, (F) BLCA, (G) KIRC, (H) KIRP, (I) LIHC, (J) LUAD, (K) LUSC, (L) PCPG, (M) COAD, (N) CHOL, (O) SARC. (P) Mutation detection results pf C5orf46 gene in human cancers as well as (Q) the detailed detected mutation spots revealed by cBioPortal dataset. (**p\u0026lt;0.01, ***p\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6650741/v1/711a3ff9c863f640d799a08f.png"},{"id":90792607,"identity":"732d9fac-c283-44d1-adfe-3deb5e91612b","added_by":"auto","created_at":"2025-09-08 08:29:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":707903,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI network centering on C5orf46 gene and enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The PPI network which is centered on C5orf46 gene for analyzing (B) the main biological functions C5orf46 and its connected genes mainly participated in.\u003c/p\u003e\n\u003cp\u003e(C)The PPI network centered on C5orf46 for analyzing (D) the main signaling pathways C5orf46 and its interacting genes involved in.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6650741/v1/677dc8e072a3e26ae6a1fafa.png"},{"id":90790473,"identity":"9567adac-ecec-418d-90a3-0d5f6a457172","added_by":"auto","created_at":"2025-09-08 08:13:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2461274,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between C5orf46 gene and pan-cancer TME angiogenesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) C5orf46 association with TME angiogenesis related genes signature which was calculated based on TCGA genes expression data in human cancers. C5orf46 association with TME angiogenesis related genes signature in individual cancers, including in (B) OV, (C) BLCA, (D) COAD, (E) READ, (F) LUSC and (G) KIRC. (R\u0026gt;0.30 was considered correlated, R between 0.30~0.49 was considered preliminary correlated, and 0.50~0.79 was moderate correlated, meanwhile, R\u0026gt;0.80 was thought as strongly correlated). (H) A tissue microarray made of KIRC samples for IHC experiments detecting C5orf46 expression, the middle panel represents a tumor tissue from left panel tissue microarray, the upper graphs(Red box) in right panel represents KIRC cancer lesion with higher C5orf46 expression, and the lower graphs (Blue box) were tissue lesion with relatively lower expression (magnification: 100X and 200X, error bar represents 100um and 60um in the left and right graph respectively).\u003c/p\u003e\n\u003cp\u003e(I) The tissue microarray same as above in (F) graph, and IHC experiment detecting CD31 which is a classic gene marker for tissue angiogenesis. The middle panel represents same tumor tissue as above C5orf46 gene expression detection tissue with same magnification and error bars representation.\u003c/p\u003e\n\u003cp\u003e(J) The tissue microarray same as above in (F) graph, and IHC experiment detecting CD34 which is another classic gene marker for tissue angiogenesis. The middle panel represents same tumor tissue as above C5orf46 gene expression detection tissue with same magnification and error bars representation.\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-6650741/v1/c6e33725c5e11d709c9093b7.png"},{"id":90790468,"identity":"44933108-cbff-46cd-8c3e-8e71d28491ed","added_by":"auto","created_at":"2025-09-08 08:13:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":234439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between C5orf46 and ECM degradation as well as EMT transition related gene signature in cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) C5orf46 association with ECM degradation which was calculated based on TCGA genes expression data in different human cancers. C5orf46 association with ECM degradation related gene signature in individual cancers, including in (B) DLBC, (C) OV, (D) BLCA, (E) LUSC, (F) CHOL, (G) GBM, (H) PRAD, (I) LIHC, (J) COAD. (K) C5orf46 association with EMT transition related gene signature that was calculated based on TCGA genes expression data in different human cancers. C5orf46 association with EMT transition related gene signature in (L) OV, (M) BLCA, (N) PAAD, (O) ESCA, (P) CHOL and (Q) COAD. (R\u0026gt;0.30 was considered correlated, R between 0.30~0.49 was considered preliminary correlated, and 0.50~0.79 was moderate correlated, meanwhile, R\u0026gt;0.80 was thought as strongly correlated).\u003c/p\u003e","description":"","filename":"fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-6650741/v1/8e81e61fb4f34748c8d9f5be.png"},{"id":90790475,"identity":"7cbf4721-461c-4e47-a786-7028c07f053f","added_by":"auto","created_at":"2025-09-08 08:13:43","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":119001,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCancers mRNAsi stemness score distribution in different C5orf46 gene expression samples\u003c/strong\u003e\u003c/p\u003e\n\u003ch4\u003e(A) C5orf46 association with mRNAsi score in human pan-cancers. Comparison of mRNAsi score in high-C5orf46 and low expression groups in (B) BLCA, (C) CESC, (D) COAD, (E) ESCA, (F) GBM, (G) KIRC, (H) BRCA, (I) LUAD, (J) HNSC, (K) PAAD and (L) LUSC. (*p\u0026lt;0.05 was considered statistical significant.)\u003c/h4\u003e","description":"","filename":"fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-6650741/v1/5bef8daa2c787871d89a27da.png"},{"id":90790471,"identity":"49dd6c89-a8d8-491b-8bc5-d441c00354d1","added_by":"auto","created_at":"2025-09-08 08:13:43","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1530147,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between C5orf46 and HRR related gene signature as well as MRR proteins expression in pan-cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association between C5orf46 gene and HRR related gene signature in (A) HNSC, (B) BLCA, (C) BRCA, (D) CESC, (E) LIHC, (F) COAD, (G) KIRC and (H) KIRP. (R\u0026gt;0.30 was considered correlated, R between 0.30~0.49 was considered preliminary correlated, and 0.50~0.79 was moderate correlated, meanwhile, R\u0026gt;0.80 was thought as strongly correlated). (I) Real time qRT-PCR detection of C5orf46 expression in 5 cases of COAD. (J) The 5 COAD cases were divided into MSS and MSI groups based on IHC experiment of four MMR proteins including MLH1, PMS2, MSH2 and MSH6 expression, and relative C5orf46 expression was compared between the two groups of samples (p\u0026lt;0.05 was considered as statistical significant). (K) The HE staining and (L) IHC experiment of MMR proteins in case 3 which is one of the COAD sample that was detected as MSI. (M) The HE staining and (N) IHC experiment of MMR proteins in case 4 which is the other COAD sample that was detected as MSI.\u003c/p\u003e","description":"","filename":"fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-6650741/v1/4e1b54054c04e993045e3689.png"},{"id":90790478,"identity":"b4d13302-ab87-45fa-859f-74ee659c4ac1","added_by":"auto","created_at":"2025-09-08 08:13:43","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1511252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eC5orf46 association with CD8+T cells included TICs distribution in cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Association between C5orf46 expression and various TICs distribution in pan-cancer. The same tissue microarray made of KIRC samples as in Figure 6H for IHC experiment showing the correlation between (B) relative C5orf46 gene expression and (C) CD8+T cells infiltration, the red, blue and green box in thee graph represents three cases that were displayed in D, E and F graph. (D) The relative C5orf46 expression (upper graph) and CD8+T cells infiltration (lower graph) in the red boxed case. (E) The relative C5orf46 expression (upper graph) and CD8+T cells infiltration (lower graph) in the blue boxed case. (F) The relative CD8+T cells infiltration (lower graph) in the green boxed case, whose C5orf46 expression result was displayed in Figure 6H (magnification: 100X and 200X, error bar represents 100um and 60um in the middle and right graph respectively) .\u003c/p\u003e","description":"","filename":"fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-6650741/v1/c0d2a624afc9f14cb840bb21.png"},{"id":90790483,"identity":"eff54b39-de12-47e9-9a22-948daae761ce","added_by":"auto","created_at":"2025-09-08 08:13:44","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":220933,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eC5orf46 association with CTL dysfunction and MSI as well as TMB in human cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssociation between C5orf46 gene and CTL function status in (A) SKCM, (B) LUAD, (C) BRCA, (D) PAAD, (E) COAD and (F) DLBC. (p\u0026lt;0.05 was considered statistical significant.) C5orf46 association with tumor (G) TMB and (H) MSI status in cancers.\u003c/p\u003e","description":"","filename":"fig12.png","url":"https://assets-eu.researchsquare.com/files/rs-6650741/v1/e69d4b24e1a11cdde2c3c281.png"},{"id":90790477,"identity":"cb848fd8-a0cb-4131-9cbf-9c212069c8f3","added_by":"auto","created_at":"2025-09-08 08:13:43","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":157117,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eC5orf46 association with certain chemotherapy drugs sensitivity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC5orf46 gene expression difference between breast cancer responders and non-responders patients as well as the generated AUC curve after using (A) endocrine therapy aromatase, (C) anti Her-2 trastuzumab. C5orf46 gene expression difference between colorectal cancer responders and non-responders patients as well as the generated ROC curve after using (B) chemotherapy oxaliplatin drugs. C5orf46 gene expression difference between ovary cancer responders and non-responders patients as well as the generated ROC curve after using (D) any chemotherapy drugs at 12 months, (E) platin at 12 months, (F) platin\u0026amp;taxane, (G) taxane, (H) any chemotherapy drugs at 6 months and (I) platin at 6 months. C5orf46 gene expression difference between colorectal cancer responders and non-responders patients as well as the generated ROC curve after using (J) 5-fluorouracil and (K) fluoropyrimidines. C5orf46 gene expression difference between GBM cancer responders and non-responders patients as well as the generated ROC curve after using (L) camustine. (*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"fig13.png","url":"https://assets-eu.researchsquare.com/files/rs-6650741/v1/3e6c7ceb93b208db37f3de49.png"},{"id":90793097,"identity":"c599fcd2-2513-4f11-9474-5965676b2a8a","added_by":"auto","created_at":"2025-09-08 08:37:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18588453,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6650741/v1/8b7751ae-a817-42e7-a2f0-ec58e23cb04c.pdf"},{"id":90790466,"identity":"f671226e-da4d-49d3-a111-83f373f31431","added_by":"auto","created_at":"2025-09-08 08:13:43","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":47104,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.doc","url":"https://assets-eu.researchsquare.com/files/rs-6650741/v1/075ed86a6752b993ba531d83.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"C5orf46: a Promising Prognosis Risk Indicator with Implication in the Remodeling of KIRC included Pan-cancer Tumor Microenvironment","fulltext":[{"header":"Background","content":"\u003cp\u003eCancer has been a worldwide health threat for people, and the initiation of cancer involves not only the accumulation of genetic mutations, epigenetic alterations or changed expression of oncogenes and tumor inhibiting genes, but also cellular communication and interaction between cancer cells and surrounding microenvironment (TME)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Cancer TME has been gradually recognized to be a complex yet important ecosystem which involves not only cancer cells, but also a wide range of non-cancerous cells including various immune cells, cancer-associated fibroblasts (CAFs), endothelial cells and other tissue specific cell types[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIncreasing number of studies have been showing that the interaction between cancer cells and TME could exert great effect on tumor clinical traits including cell proliferation, cancer metastasis, microenvironment angiogenesis and significant immune surveillance escape[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Depending on the specific organ that different tumors arise, intrinsic features of the cancer cells, tumor grade, stage, patient characteristics, cellular composition as well as the functional state of the TME will differ, and various cells in the TME can play either tumor suppressive or tumor supporting functions[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExosomes are extracellular vesicles with a diameter of 30-100nm that could be released by various TME cells, containing various substances such as nucleic acids, proteins and lipids[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Exosomes have been an important way for cellular communication and serving as carriers to form a signaling network system, mediating the interactions between tumor cells and surrounding TME cells. Tumor cells derived exosomes (TDEs) have been reported to play vital functions in the formation and development of different tumor processes, including promoting tumor epithelial mesenchymal transition (EMT), mediating immune escape, TME remodeling, inducing angiogenesis, and regulating macrophage polarization[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The diversified function of exosome has been attracting widespread attention, and it\u0026rsquo;s of clinical significance to keep identifying the role of exosome as well as its containing proteins or nucleic acids in cancers, the results shall provide promising insight for further understanding of the collaborative working network of cancer TME and identifying potential cancer biomarkers.\u003c/p\u003e \u003cp\u003eIn the study, we mainly focused on a specific gene C5orf46 which is a recently discovered tumor relating gene, and the gene has been predicted by multiple online database including GeneCards and UniProt to be locating in extracellular exosomes. As for the gene function, C5orf46 is short for chromosome 5 open reading frame 46, and it has been reported to be associated with the patients survival in gastrointestinal tumor[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], some renal and gastric cancers[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], but the full spectrum of C5orf46\u0026rsquo;s role in human cancers, especially its potential regulation on cancer TME remodeling, for instance tumor angiogenesis, immune regulation and extracellular matrix (ECM) degradation as a predicted exosome containing gene has yet to be explained.\u003c/p\u003e \u003cp\u003eBased on TCGA pan-cancer data and local hospital cancer samples, we comprehensively analyzed the expression as well as clinical traits of C5orf46 in human cancers. Considering kidney renal clear cell carcinoma (KIRC) was indicated to be the top cancer with highest C5orf46 expression difference between cancer and corresponding normal tissues, and also although KIRC is able to benefit from PD-1/PD-L1 blockade immunotherapy, the function of the commonly used immune biomarkers including PD-L1 expression, MSI and tumor mutation burden (TMB) in KIRC are still in dispute, so a comprehensive analysis of the cancer immune microenvironment might benefit further tumor immunotherapy. Thus, KIRC was highly focused during our validation of C5orf46 association with pan-cancer clinical traits. The results shall provide promising insights for understanding the role of exosomal C5orf46 in human cancers and aid the unearthing of potential new prognostic indicators.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eData source: TCGA pan-cancer gene expression profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was started with TCGA pan-cancer data analysis and then local hospital samples validation, and the TCGA data was accessed using UCSC Xena[15]. Based on the downloaded TCGA data, the basic information of C5orf46 especially its expression levels in pan-cancers, and its association with cancers clinical parameters for instance cancer stages, grades, subtypes, patients age and gender were in succession explored. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBasic physicochemical property analysis of C5orf46 gene\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe basic genetic information and physicochemical properties of C5orf46 gene was investigated based on a combine of five analysis databases including ProtParam[16], ProtScale[17], Uniprot[18], GeneCards[19] and Human Protein Atlas[20]. ProtParam and ProtScale were together applied to investigate the physicochemical properties of the protein including its computed aminoacid composition, encoded protein molecular weight, its theoretical isoelectric point, estimated protein half life, instability index as well as hydrophobicity and hydrophilicity. Meanwhile, Uniprot, GeneCards and Human Protein Atlas were in succession accessed to investigate the cellular location and expression level of C5orf46 in human cancers. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpression in pan-cancer and association with cancers clinical parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter understanding the basic genetic and encoding protein property of C5orf46, UALCAN[21] which has been an effective and open access online service for interpreting the association between certain genes and cancers clinical parameters were applied to investigate C5orf46 expression in cancers comparing to normal control samples. \u003c/p\u003e\n\u003cp\u003eAt first, based on UALCAN, C5orf46 expression patterns in human pan-cancer including but not limited to the top morbidity and mortality cancers for instance breast invasive carcinoma (BRCA), pancreatic adenocarcinoma (PAAD), rectum adenocarcinoma (READ), hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), kidney clear cell carcinoma (KIRC) and stomach adenocarcinoma (STAD) comparing to corresponding normal control tissues were investigated. Besides, the association between C5orf46 expression level and important clinical parameters including cancer stages, grades and other cancers features in these cancers were also observed. \u003c/p\u003e\n\u003cp\u003eThen, besides UALCAN online database analysis, local hospital samples were also used to explore the association between C5orf46 expression and cancer clinical features. Considering KIRC was the top cancer with highest C5orf46 gene expression difference between cancer and corresponding normal samples, the analysis was mainly focused in KIRC. A total of 39 KIRC samples were applied for further tissue microarray production and Immunohistochemistry (IHC) experiments. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue microarray production\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the study, KIRC and Giant cell tumor of bone (GCTB) samples were used to make tissue microarrays for further IHC experiment. The cancer tissues were all obtained from local hospital Biobank. Scientific use of the Biobank samples in this study was approved by both the Biobank Comittee and Hospital Institutional Board (Second Hospital of ShanXi Medical University, China). \u003c/p\u003e\n\u003cp\u003eIn the study, 39 cases of KIRC and 20 cases of GCTB samples were obtained from biobank after HE staining confirmation of the disease diagnosis and evaluation of cancer percentage by local hospital registered pathologists. And to eliminate tumor heterogeneity, in each sample case, three to four areas were circled, followed by preparing the receptor wax block with 1.5mm needle according to operating instructions (Chloe, BeiJin, China). Further, the tissue microarray was serially sliced and stored at 4\u0026deg;C refrigerator for next step experiment. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemistry (IHC) experiments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue samples and regents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIHC experiment was conducted using above cancer tissue microarrays to detect the gene\u0026rsquo;s expression pattern in different cancer samples. And it was performed on VENTANA platform (Roche) in local hospital Pathology Department. The primary antibody of C5orf46 gene was purchased from Invitrogen (PA5-140360), and the secondary antibody (Envision /HRP kit) and DAB detection kit were from ZSBG-Bio. Other reagents including but not limited to H2O2, antigen retrieval citrate solution, phosphate-buffered saline (PBS) and hematoxylin stain were all supplied by our hospital Supply Department. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIHC experimental protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe tissue microarray slides were processed in the order of deparaffin, rehydration with gradient ethanol followed by antigen retrieval according to the antibody\u0026rsquo;s manual instruction. Meanwhile, to inhibit the activity of endogenous peroxidase, the slides were maintained in 0.3% H2O2 containing methanol for 10min. Further, the slides were soaked in bovine serum albumin for 30min and then incubated with primary C5orf46 antibody (dilution 1:200) overnight at 4\u0026deg;C followed by a 40 minutes secondary antibody incubation at 37\u0026deg;C. Finally, the slides were processed with horseradish peroxidase (HRP) and visualized in DAB for results evaluation. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIHC results evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC5orf46 was observed to distribute mostly in cell cytoplasm, cell membrane and extra cellular regions. The evaluation of the IHC result was based on both staining intensity and staining area. In the study, IHC result was evaluated by two local hospital registered pathologists with the staining intensity scored as: None (0), mild (1), moderate (2) and strong (3), meanwhile, the staining area was classified as: 10% (0), 11\u0026ndash;25% (1), 26\u0026ndash;50% (2), 51\u0026ndash;75% (3) and \u0026gt;75% (4) (Example in Figure 1A-1D). \u003c/p\u003e\n\u003cp\u003eThe section\u0026rsquo;s final score equals the multiplication of staining intensity and staining area, and the final result of each patient\u0026rsquo;s tissue was recorded as the average of three independent microarray cores\u0026rsquo; scores, and if the final score\u0026lt;6, the result was defined as low expression, meanwhile, if final score\u0026ge;6, the result was classified as high expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival analysis of C5orf46 gene in pan-cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvival analysis is conducted based on Kaplan-Meier plotter[22] which has been an effective used database for investigating the overall survival (OS) as well as recurrence free survival (RFS) correlation of human genes, in the database, over 10,000 cases covering broad spectrum human cancers were contained. The prognosis indicating value of C5orf46 in pan-cancers were analyzed based on the platform. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethylation regulation and genetic alterations analysis \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo preliminary explore the potential reason of altered expression of C5orf46 in cancers comparing to corresponding normal tissues, the common gene expression modulation especially methylation and phosphorylation regulations of the gene was preliminary explored based on UALCAN platform which has been above used for analyzing the clinical parameters association of the gene. Based on UALCAN, the relative methylation level of C5orf46 in cancers as well as the relative phosphorylated protein comparing to total C5orf46 protein levels in cancers were observed. \u003c/p\u003e\n\u003cp\u003eMeanwhile, besides altered mRNA expression, other types of genetic variations for instance gene mutation, copy number variation, amplification and deletion are also important genetic changes that may effect on cancers biological functions. cBioPortal[23] has been a rich data resource for world wide researches to investigate certain genes variations, and in the study, it was applied to firstly uncover the genetic alterations of C5orf46 in human cancers using \u0026ldquo;cancer types summary\u0026rdquo; module of \u0026ldquo;quick search\u0026rdquo; section in the database, and then display the mutated site information of C5orf46 gene in 3D protein structures using the \u0026ldquo;mutation\u0026rdquo; module of the database. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC5orf46 centering Protein-protein interaction (PPI) network construction and related genes analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter understanding the expression patterns as well as genetic alterations of C5orf46 gene in cancers, the next step was to investigate the detailed functions of C5orf46 in cancers and to preliminary explore the potential mechanism. \u003c/p\u003e\n\u003cp\u003eFirstly, STRING, which is short for Search Tool for the Retrieval of Interacting Genes[24] was used to construct the PPI network centering on C5orf46 gene for investigating its surrounding interacting gene partners. And then genes enrichment analysis was applied to annotate the basic biological attributes of PPI network containing genes including their main cellular location, involved biological processes, molecular functions and the signaling pathways the genes mainly enriched in. Thus, C5orf46 gene and its potential interacting partner genes as well as their biological features were preliminary understood. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTME angiogenesis association of C5orf46 gene in pan-cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the association between C5orf46 gene and tumor angiogenesis, 32 well known genes that were previously reported to be related with TME angiogenesis (listed in Supplementary Table 1) including vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF) and platelet derived growth factor (PDGF) were selected. Further, based on TCGA pan-cancer data, the expression of the 32 genes expression in cancers were observed, followed by being input into GEPIA2.0 for calculating their correlations with C5orf46. \u003c/p\u003e\n\u003cp\u003eThen, IHC experiment was additionally conducted to validate the association between C5orf46 expression and KIRC cancer angiogenesis, CD31 and CD34 which have been two commonly used markers for immature and mature tissue blood vessels were used for analyze. Primary antibodies to both CD31 and CD34 were from hospital Pathological Department, and other IHC experiment regents and protocols were same as described before. Meanwhile, the evaluation of CD31 and CD34 marked blood vessels were scored as the average number of 5 pictures under 200x magnification microscope field. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtra cellular matrix (ECM) degradation association analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBesides TME angiogenesis, ECM degradation has been another major aspect of TME remodeling which has been known to involve in various cancer processes, most significantly cancer invasion and metastasis. For evaluating the association between C5orf46 and cancers ECM degradation, 23 genes that have been supported to be related with ECM degradation were selected (listed in Supplementary Table 2), and based on TCGA pan-cancer expression data, the correlation between C5orf46 and the 23 genes signature was evaluated in various cancers for investigating the potential effect C5orf46 gene has on TME remodeling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between C5orf46 and cancers EMT process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEpithelial Mesenchymal Transition (EMT) refers to the transformation of epithelial cells to mesenchymal cells, which endows cells the ability to transfer and invade, thus promoting multiple critical cancer processes, for instance inducing stem cell characteristics, reducing apoptosis and increasing immune suppression. For evaluating the effect C5orf46 gene has on cancer EMT process, 14 characteristic genes (listed in Supplementary Table 3) that were indicated by previous reports to be EMT related were identified, and their expressed were accessed based on TCGA pan-cancer data. Further, the correlation between C5orf46 and the 14 genes signature was evaluated. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC5orf46 correlation with cancers stemness index \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCancer stem cells are a specific group of cells with similar biological traits as the regular stem cells, which posses high self-renewal ability and the ability to differentiate into various populations of cancer cells. In the past decade, scientists engaged in TCGA projects analyzed thousands of cells at different stages of cell differentiation, thereby identifying the typical molecular characteristics possessed by stem cells. Based on these data, they developed the \u0026ldquo;cancer stemness index\u0026rdquo; which was calculated based on genes expression and DNA methylation characteristics[25]. \u003c/p\u003e\n\u003cp\u003eThe cancer stemness index ranges from 0 to 1, with 0 indicating low similarity to stem cells and 1 indicating high similarity to stem cells, and a higher index seems to be directly related with the progression of cancer. In the study, using the reported \u0026ldquo;cancer stemness index\u0026rdquo; calculation algorithm, we calculated the index in human cancers and compared the difference between C5orf46-high and low expression groups of cases. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC5orf46 correlation with cancers HRR signature and MRR proteins expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering Homologous Recombination Deficiency (HRD) has been an increasing known important clinical trait which correlates not only with disease procession but also PARPi related drugs therapies efficiency[26], besides above calculation of cancer stemness index, we further evaluated the association between C5orf46 and cancer DNA repair ability which was based on TCCA pan-cancer data of 27 selected Homologous Recombination Repair (HRR) signaling participated genes (listed in Supplementary Table 4). \u003c/p\u003e\n\u003cp\u003eMoreover, besides HRD, the potential correlation between C5orf46 expression and another well known DNA deficiency repair system, namely mismatch repair (MMR) proteins expression were also analyzed. Four commonly used MMR proteins namely MLH1, PMS2, MSH2 and MSH6 were detected with local hospital COAD cancer samples which has been a cancer type with high dMMR probability. The the primary antibody to all the four MMR proteins were purchased from ZS-BIO (China), and IHC regents and experiment procedures of the four MMR proteins detection were the same as previously described. Further, C5orf46 expression in COAD samples was detected using qRT-PCR experiment as previously described, and the association between relative C5orf46 expression and MMR status would then be evaluated based on the qRT-PCR and IHC experiment results. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune infiltration cells and CTL dysfunction correlation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBesides previously analyzed tumor EMT process, angiogenesis and ECM degradation, exosomes have been known to participate in immune microenvironment regulation. For characterizing the immune landscape between C5orf46-hign and low expression cancer samples, CIBERSORT algorithm was performed to calculate the relative contents of 22 tumor infiltrating immune cells (TICs) based on TCGA profiles data, followed by investigating the correlation between C5orf46 expression and the TICs infiltration level. \u003c/p\u003e\n\u003cp\u003eEspecially, cytotoxic T lymphocytes (CTLs) have been a key component of adaptive immune system and play important roles in human anti-tumor immunity, and CTLs are also one of the main effector cells of tumor immunotherapy. In the study, the association between C5orf46 expression and CTLs\u0026rsquo; function on patients survival rates were especially presented based on Tumor Immune Dysfunction and Exclusion (TIDE) platform[27].\u003c/p\u003e\n\u003cp\u003eMoreover, considering CD8+T cell has been an important cancer killing immune cell type, and for validating the association between C5orf46 expression and CD8+T cells infiltration, KIRC tissue microarray which has been previously used to evaluate the association with CD31 and CD34 stained blood vessels were again applied to explore the CD8+T cells distribution. The primary antibody to CD8 were purchased from ZS-BIO (China), meanwhile, other IHC regents, equipment and experiment procedure were the same as previously described. The evaluation of CD8+ T cells infiltration were scored as the average number of positive stained cell numbers in 5 pictures that were taken under 200x magnification microscope field. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor mutation burden (TMB), microsatellite instability (MSI) and immune therapy sensitivity analysis \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMSI and TMB are currently clinical effectively used biomarkers for predicting the potential benefit of patients from immune therapies. Therefore, after analyzing the association with immune cells infiltration and CTL functions, C5orf46 gene expression correlation with cancers TMB and MSI scores were next step evaluated based on ACLBI online database[28]. \u003c/p\u003e\n\u003cp\u003eMoreover, based on ROC plotter analysis platform, C5orf46 gene expression differences between the responders and non-responders to specific immune therapies drugs for instance anti PD-1, PD-L1 and CTLA4 inhibitors were displayed, together with the receiver operating characteristic curves (ROC) of therapy-related cancer patients survival. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChemotherapy related drugs sensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBesides immune therapy, for preliminary investigating the effects of C5orf46 has on the routine chemotherapeutic response for human cancers, based on ROC plotter analysis platform, C5orf46 gene expression between the responders and non-responders to commonly used chemotherapy drugs in different types of human cancers, as well as the ROC curve of therapy-related patients survival were additionally displayed[29].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMost of the bioinformatics analyses were performed on corresponding online databases. As for the processing of downloaded TCGA pan-cancer data, statistical analysis were performed using SPSS 26.0. For enumeration data for instance when comparing genes expression difference between cancers and corresponding normal samples, the data were analyzed using t test. As for the measurement data including the association between gene expression and cancers pathological parameters, the data were analyzed by \u0026chi;2 test. And for correlation analysis, for instance the correlation between C5orf46 gene expression and angiogenesis signature, the data were analyzed by Spearman analysis. p\u0026lt;0.05 was considered statistically significant. (For all analysis results, * represents p\u0026lt;0.05, ** represents p\u0026lt;0.01, *** represents p\u0026lt;0.001).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eC5orf46 genetic information and physicochemical\u0026nbsp;property\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC5orf46 which is short for chromosome 5 open reading frame 46 is also known as AP-46 and SSSP1. Based on combine analysis of different platforms, the basic genetic information and physiochemical property of the gene was discovered. The results revealed that C5orf46 gene locates in 5q32, comprises of 5 exons and encodes a 87 amino acids containing protein. The protein formula is C\u003csub\u003e433\u003c/sub\u003eH\u003csub\u003e686\u003c/sub\u003eN\u003csub\u003e108\u003c/sub\u003eO\u003csub\u003e135\u003c/sub\u003eS\u003csub\u003e4\u0026nbsp;\u003c/sub\u003ewith estimated weight as 9.7KD, and the computed theoretical isoelectric point of the protein is 4.67, meanwhile, the instability index of the protein is estimated to be 39.96 and the grand average of hydrophobic value is -0.254 indicating that C5orf46 works as a cellular stable and hydrophilic protein.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeanwhile, as for the cellular location, C5orf46 was supported by multiple databases including Uniprot, GeneCards and Human Protein Atlas to be cellular secreted and locating in extracellular exosomes (Figure 1A), which is consistent with its computed protein stability and hydrophilic property (Figure 1B). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC5orf46 was up-regulated in KIRC included multiple cancers and associated with advanced cancer grades and stages\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC5orf46 gene expression in broad spectrum cancers and corresponding normal control samples were investigated, and the results indicated that C5orf46 was statistical significantly up-regulated in various cancer types expect for PAAD (Figure 1C). In PAAD, the gene seemed to express lower in the cancer comparing to normal samples even though the difference was not statistical significant (Figure 1D). And of the up-regulated cancers, KIRC was tend to be the top cancer with highest expression difference between cancer and corresponding normal tissues, and the tendency was also validated using local hospital cancer samples based on IHC experiment (Figure 2A-2D). Although the gene expression was also aberrant up-regulated in other cancers, the difference was not as high as in KIRC, including in other kidney cancers for instance KIRP and KICH (Figure 2E, 2F).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeanwhile, as for the association with KIRC clinical pathological features, C5orf46 was indicated by TCGA data to tend to express higher in male patients than female ones (Figure 2G), and the up-regulated expression was associated with more advanced cancer grades and stages (Figure 2H, 2I). Moreover, the gene expression was also higher in cases with nodal metastasis, although the difference was not statistical significant which might be due to the limited case number in the metastatic group (Figure 2J). The association between C5orf46 expression and KIRC clinical features were also validated using local hospital samples, which also revealed the similar tread between C5orf46 expression and patients gender, ISUP Grade as well as bone metastasis (the main metastasis site for local KIRC samples). Meanwhile, no statistical significant correlation was observed between C5orf46 and patients age, tumor size, membrane and renal sinus invasion and other clinical features (Table 1). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs for the other cancers, similar association has also been discovered between C5orf46 gene and the clinical features in multiple cancers, for instance, higher gene expression was related with more advanced cancer stages in READ, BLCA and KICH (Figure 1E, 1G, 1H), and also it expressed higher in cases with nodal metastasis in BLCA (Figure 1F). Interestingly, similar trends were observed between C5orf46 and PAAD clinical features which cancer was revealed by previous analysis to be the only cancer with down regulated C5orf46 expression. C5orf46 expression was also higher in more advanced PAAD stages and grades. Even though the expression in Grades 4 and stage 4 seemed to be lower than Grade 3 and stage 3 (Figure 1I-1K), the difference was not statistical significant and it might be the result from the limited samples cases in the 4\u003csup\u003eth\u003c/sup\u003e group. The similar trend with cancer stages and grades in PAAD as in the KIRC included other cancers indicating C5orf46 works as an oncogene in these cancers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigher expression of C5orf46 was correlated with worse patients survival in cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBesides the association with advanced cancers grades and stages, the direct prognosis indicating value of C5orf46 gene was next step analyzed. The results indicated that higher expression of the gene was associated with statistical significantly worse overall survival (OS) and shorter recurrence free survival (RFS) in nearly all cancers including KIRC (Figure 2K, 2L) and others for instance CESC, STAD, PAAD and KIRP (Figure 3A-3C). The consistent results of the association with both advancing cancer stages and worse patients survival including in PAAD assisted previous speculation that C5orf46 works as a potential oncogene in broad spectrum human cancers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC5orf46 was correlated with the cancer metastasis status \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCancer metastasis has been a major reason for poor patients prognosis, and for evaluating the association between C5orf46 and cancer metastasis, IHC experiment was applied on KIRC included cancer samples. And the results revealed that C5orf46 expression was not only higher in KIRC cancer cells comparing to corresponding normal kidney cells (Figure 4A), the gene expression was higher in bone (Figure 4B) and lung metastatic (Figure 4C) KIRC cancer cells comparing to surrounding remained normal bone and lung tissues. Meanwhile, besides epithelium originated cancers, for preliminary detecting C5orf46 expression in mesenchymal tissues originated tumors, tissue microarray made of GCTB were also applied to detect C5orf46 expression (Figure 4D), and the results revealed that the gene expression was higher in tumor lesions (Figure 4E, 4F) comparing to surrounding normal soft tissues (Figure 4G, 4H), although the gene was seemed to diffusely distributed with no difference in tumor or immune cells. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAltered C5orf46 expression in cancers was partly attributed to DNA methylation regulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor exploring the potential reason for the altered C5orf46 gene expression in KIRC included human cancers, the methylation level of the gene was investigated based on UALCAN platform. And the results revealed that expect for PAAD which showed that C5orf46 gene methylation was higher in cancer comparing to normal tissues which trend was actually in consistent with the lower gene expression in cancer, as for KIRC included other cancers for instance BRAC, LUAD and LUSC, the gene methylation was widely lower in cancers comparing with corresponding normal samples (Figure 5A-5N). The results indicated gene methylation accounts for at least part of the up-regulated expression of C5orf46 in human cancers. Meanwhile, as for mesenchymal tissue originated sarcomas, the gene methylation level was not statistical significantly different in tumor comparing to normal tissues (Figure 5O) indicating a much more complex regulation mechanism in this kind of tumors. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOther genetic alterations of C5orf46 gene in cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBesides mRNA expression, for comprehensively understanding C5orf46 gene status in cancers, other genetic alterations including gene mutation, protein structure variant and copy number variation were investigated based on cBioPortal database. And the results revealed that gene amplification was one of the main types of C5orf46 alterations in cancers, especially in KIRC that gene amplification has been the only type of alteration being detected so far (Figure 5P). Meanwhile, as for the other cancers, a certain percent of deletion and genetic mutations which were mostly single nucleotide variations were also discovered in several types of cancers including skin cutaneous melanoma, diffuse large B-cell lymphoma, uterine corpus endometrioid carcinoma, cervical squamous cell carcinoma, head and neck squamous cell carcinoma and esophageal adenocarcinoma (Figure 5Q). \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC5orf46 centering Protein-protein interaction (PPI) network construction and functional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo preliminary explore the potential mechanism of C5orf46 regulation on KIRC included cancers development, STRING was used to construct the C5orf46 gene centering PPI network followed by genes enrichment analysis, thus uncovering the probable signaling pathways C5orf46 and its interacting genes involved in. The results actually showed consistent direction as current understanding of C5orf46 as an exosome containing gene, that it mainly participates in biological processes such as cell communication, secretion and chemical synaptic transmission (Figure 6A-6D). The results supported further deeper and in detail exploration of the correlation between C5orf46 and currently acknowledged exosomes related biological cellular effects. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC5orf46 related with TME angiogenesis \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTME angiogenesis has been a major aspect during tumor development and metastasis, to validate the association between C5orf46 gene and tumor angiogenesis, we selected 32 angiogenesis related genes and analyzed their association with C5orf46 expression in various cancers, and the results showed a statistical significant positive correlation between the gene and KIRC included multiple cancers for instance OV, BLCA and COAD (Figure 7A-7G).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeanwhile, IHC experiment that was conducted on KIRC tissue microarray also revealed that although C5orf46, CD31 and CD34 expressions vary in different cancer samples, the C5orf46 expression was indeed associated with the number of both CD31 and CD34 stained blood vessels. We took a case with obvious heterogeneous C5orf46 expression as example (Figure 7H), in the area with high C5orf46 expression, both the number of CD31 stained immature blood vessels (Figure 7I) and CD34 marked mature blood vessels (Figure 7J) were higher that in the area with low C5orf46 expression, supporting the potential effect C5orf46 has on TME angiogenesis. The colocalization of C5orf46 with CD31/CD34 haven\u0026rsquo;t been included in the study yet due to the regent and equipment limitation in our laboratory, and also KIRC has been well known as a typical cancer abundant with blood sinuses, not only the number of blood vessels, but also the vessel lumen structure, size and morphology shall effect on cancer development, more detailed experiments and analysis are still needed for further investigating the effect C5orf46 has on KIRC blood vessels remodeling. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC5orf46 correlated with ECM degradation and EMT transition in metastasis process of cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBesides angiogenesis, ECM degradation has also been an important aspect of TME remodeling which was not only a critical step for cancer metastasis, but also a major biological process that reacts on oncogenes containing exosomes regulation. For investigating the association between C5orf46 and ECM degradation, 23 ECM degradation related genes were selected, and the analysis result revealed significant positive correlation in various cancers including OV, BLCA, LUSC and COAD (Figure 8B-8G), however, the association in KIRC was revealed to be not statistical significant (Figure 8A). \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeanwhile, similar correlation trends was also observed between C5orf46 and cancer EMT transition. Although positive correlation were revealed in various cancers between C5orf46 gene and the signature composed of 14 EMT related genes, for instance in OV, BLCA, PAAD and COAD (Figure 8L-8Q), the correlation in KIRC was not statistical significant neither (Figure 8K). The significant correlation between the gene and multiple important cancer traits shall provide promising insights for further detailed investigation of the gene role on specific cancer progresses. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between C5orf46 and cancer stemness index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCancer stemness index is a recently developed indicator which is calculated based on genes methylation and expression data for evaluating the similarity between tumor cells and stem cells. In the study, we calculated the mRNAsi in different cancers based on TCGA pan-cancer genes expression data (Figure 9A), followed by analyzing its correlation with C5orf46 gene expression, and the results revealed that mRNAsi index score differed significantly between high C5orf46 expression and low expression groups in KIRC included various cancers (Figure 9B-9L). Interestingly, similar trend was observed among various cancers that C5orf46 gene was negatively correlated with the mRNAsi index, namely the index was higher in low C5orf46 expression groups, indicating a uniform effect C5orf46 has on the stemness of the cancers. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between C5orf46 and HRR signature as well as MRR status analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering DNA repair has been a major mechanism for maintaining cancer genome stability, which also contributes to the cancers stemness maintenance, we further analyzed the association between C5orf46 and 27 commonly known HRR genes, and the results revealed that only mild interaction has been observed (Figure 10A-10H), indicating C5orf46 might not be a main regulator for cancers DNA deficiency nor HRR DNA repair.\u003c/p\u003e\n\u003cp\u003eBesides HRD, given MRR has also been another well known DNA deficiency repair system, the association between C5orf46 expression and cancer MMR status were also preliminary explored. In the study, we included in 5 cases of COAD cancer samples which has been a common cancer type with high dMMR ratio, and based on the IHC experiment of four MMR proteins MLH1, PMS2, MSH2 and MSH6, two cases were revealed to be MSI, meanwhile, the other three cases were MSS (Figure 10I). We compared the relative C5orf46 expression between the two MSI samples and three MSS cases, and non statistical significance was discovered (Figure 10J). And even between the two cases with MSI status, the MMR proteins expression were different, one with MLH1, PMS2 loss of expression (Figure 10K, 10L), meanwhile, the other with MSH2 and MSH6 loss of expression (Figure 10M, 10N). Based on current results, none specific correlation was supported between C5orf46 and cellular DNA deficiency repair system, although further deeper analysis with bigger sample number covering more cancer types would help validating the conclusion. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The positive correlation with specific cancer traits including EMT, ECM degradation and TME angiogenesis, meanwhile negative correlation with cancer stemness index and none specific correlation with DNA repair might reflects the diversified functions of exosomal genes in cellular biological processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC5orf46 correlates with cancers immune landscape especially cytotoxic T lymphocytes infiltration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the effect C5orf46 gene has on cancers immune infiltration, the 22 tumor infiltration immunocytes (TIC) landscape in different types of cancers were calculated based on CIBERSORT database, followed by analyzing its relationship with C5orf46 expression. And as indicted in the distribution heatmap, strong positive correlations between C5orf46 and macrophages were observed in multiple cancers including BRCA, COAD, KICH, LUSC, OV, PAAD, READ and so on. Meanwhile, negative correlations were displayed with CD4+ T cell and CD8+T cells in above cancers. As for the association with endothelial cells, the distribution various in different cancers, indicating a cancer-specific function mode (Figure 11A). \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsidering CD8+T cells have been an important immune cell type playing significant cancer killing function, the association between the gene and CD8+T cells distribution was mainly validated using KIRC tissue microarray (the same as previous described, Figure 11B, 11C). And what is controversial is that the association between C5orf46 gene and CD8+T cells distribution in KIRC was only stochastic. Although the level of CD8+T cells infiltration vary in different samples, it seems inconsistent with C5orf46 expression level (Figure 11D, 11E), especially in the same case with heterogeneous C5orf46 expression that was previously applied to observe angiogenesis, the CD8+T cells infiltration was non statistical different in the areas with higher or lower C5orf46 expression (Figure 11F). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeanwhile, given the dysfunction of cytotoxic T lymphocyte (CTL) has been a major aspect during tumors immunosuppression, we also investigated C5orf46 association with CTL functions based on TIDE analysis. And the analysis result revealed that CTL dysfunction level were different between high-C5orf46 expression and low expression samples in multiple cancers including lung cancers, melanoma, colorectal cancer, invasive breast cancer and pancrestic cancers, but not in KIRC (Figure 12A-12F), indicating C5orf46 indeed has potential value in predicting CTL functions in cancers, but it works a cancer-specific mode, which means it might only functions in certain types of cancers. Further deeper analysis with bigger sample number covering more cancer types would help validating the conclusion. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor mutation burden (TMB), microsatellite instability (MSI) and immune therapy sensitivity analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTMB and MSI have been not only well acknowledged clinical evaluation indicators of cancers immune microenvironment, but also widely used biomarkers for predicting patients sensitivity for immune therapies. Despite the significant correlation between C5orf46 and immune cells distribution, the analysis of association with TMB and MSI revealed that C5orf46 affects only mildly of the MSI status in CHOL, as for the other cancers, none significant correlation were observed in neither TMB nor MSI analysis (Figure 12G, 12H).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeanwhile, consistent results have been revealed in the immune therapy drugs sensitivity analysis which showed that none statistical significant expression difference of C5orf46 has been observed between the responders and non-responders of patients after receiving immune therapies drugs including anti PD-1, PD-L1 and CTLA4 inhibitors (data not shown). The results indicated that although relates with the immune infiltration landscape in cancers, C5orf46 might not be a potential drug target for immune therapy. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC5orf46 affects drugs sensitivity in certain chemotherapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDrug sensitivity has been a critical and also insurmountable problem in clinic cancer treatment, despite the none specific correlation with immune therapy drugs, to explore whether C5orf46 would be a potential indicator to predict chemotherapeutic responses of cancers, ROCplotter database was accessed to evaluate the association between C5orf46 and the therapeutic outcomes in certain cancer types. The results revealed that in BRCA, C5orf46 expression was significantly higher in the non-responders comparing to responders after endocrine therapy using aromatase inhibitors as well as after anti-HER2 therapy using trastuzumab (Figure 13A-13D). Similar trends were observed in ovary and colorectal cancer patients that C5orf46 expression was higher in non-responders comparing to responders after chemotherapy using platin, taxane, 5-fluorouracil, oxaliplatin and fluoropyrimidines (Figure 13E-13L). \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, based on RNAactDrug platform, the top 8 anti-C5orf46 small molecular compounds with FDR \u0026lt; 0.05 were also displayed (Table 2). Although deeper experiments validation and clinical trials were needed before clinical application of these drugs, the results shall provide promising insights for further researches.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCancer has been a major health threat for people, and the development of cancer involves not only the genetic alterations for instance oncogenes activation and tumor inhibiting genes mutations, but also involving intensive interaction between cancer cells and resident non-cancerous cells in TME[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. And the interaction modes among cancers and surrounding TME cells includes direct cell-cell contact and paracrine signaling, during which process the release of extracellular vesicles (EVs), for instance exosomes has been an important paracrine mechanism for cell communicating. Exosomes have been gradually revealed to play diversified functions in cancer development, for instance inducing normal cells malignant transformation, participating in TME regulation, influencing the drug resistance of tumor cells, as well as regulating the premetastatic niche of tumors[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the study, we mainly focused on a recently discovered tumor regulation related gene C5orf46, the gene firstly exposed to us from previous studies that when we analyzing the different expressed genes in primary cancers comparing to responding normal control samples, the gene was generated repeatedly in multiple types of cancers for instance lung cancer[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], pancreatic cancer[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and even osteosarcoma[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. It certainly provoked our interest, although we soon discovered that up to now, only very limited functions have been reported about the gene. A pan-cancer analysis of the gene shall provide meaningful direction for further detailed researches of the gene\u0026rsquo;s role in the initiation and development of different types of cancers.\u003c/p\u003e \u003cp\u003eThe study was started with the investigation of the basic physiochemical property of C5orf46, the computed molecular weight, hydrophobicity/hydrophilicity feature as well as instability index all supported C5orf46 working as a cellular stable and hydrophilic protein, and the gene was supported by multiple databases to be mainly being secreted and locating in extracellular exosomes. However, the main limitation of the current validation is that as a potential exosome containing gene, the exosomes hasn\u0026rsquo;t been detected using electron microscopy because of the equipment limitation in our laboratory, this part of experimental validation hasn\u0026rsquo;t been included in the study.\u003c/p\u003e \u003cp\u003eAfter basic understanding of the physiochemical property of C5orf46, the gene expression patterns as well as prognosis correlation were comprehensively investigated based on TCGA pan-cancer data. We encouragingly discovered that C5orf46 expression was widely up-regulated in broad spectrum human cancers especially in KIRC which was indicated to be the top cancer with highest C5orf46 expression difference between cancer and normal tissues. And IHC experiment using local hospital KIRC cancer samples supported the gene not only expressed high in cancers comparing to corresponding normal tissues, but also the gene associated with multiple cancer clinical features including more advanced cancer stages, grades and metastatic status. Meanwhile, the higher gene expression was indicated to statistical significantly correlates with worse patients OS and RFS in various cancers.\u003c/p\u003e \u003cp\u003eFor preliminary exploring the potential reason of the up-regulated expression of C5orf46 in cancers, DNA methylation level was then evaluated, and we discovered that Co5orf46 gene methylation level was lower in various cancers comparing to corresponding normal control samples expect for in PAAD, the results supported that gene methylation be a main regulation of C5orf46 gene and accounted for at least part of gene\u0026rsquo;s altered expression in human cancers. Besides mRNA expression, other types of genetic alterations including mutation ratio, protein structure variant and copy number variations that commonly affect gene functions were also analyzed, and the result indicated that although a certain percent of deletion and single nucleotide mutations were discovered, the gene amplification and proteion gain of expression shall be one of the the major alteration types of C5orf46 in human cancers.\u003c/p\u003e \u003cp\u003eFurther, for investigating the potential role of C5orf46 in cancers, the PPI network centering on the gene was constructed followed by preliminary analyzing the main enrichment of the related surrounding interacting genes. Afterwards, the association between C5orf46 and multiple important clinical cancers traits including TME angiogenesis, ECM structures, tumor transition EMT and immune modulation were in succession analyzed.\u003c/p\u003e \u003cp\u003eFirstly, considering tumor microenvironment angiogenesis has been a well acknowledged important process for tumor growth and metastasis[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], multiple critical elements including the known vascular endothelial growth factor (VEGF), VEGF receptor (VEGFR), fibroblast growth factor (FGF), platelet derived growth factor (PDGF), transforming growth factor β (TGF-β) have been acknowledged to play roles in the process[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], thus the association between C5orf46 and angiogenesis was mainly focused. As a matter of fact, exosomes from cell lines or plasma sources of various human tumors for instance glioblastoma, pancreatic cancer and nasopharyngeal carcinoma have been reported to be able to effectively induce angiogenesis in vitro and in vivo[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In the study, we discovered that exosomal C5orf46 also related with TME angiogenesis, statistical significant positive correlation have been observed in KIRC included various cancers, for instance COAD, CESC, KIRC, LUSC, LUAD and READ. However, as KIRC has been known as a typical cancer abundant with blood sinuses, not only the number of blood vessels, but also the vessel lumen structure, size and morphology shall effect on cancer development, more detailed experiments are still needed for validating the association between C5orf46 and KIRC angiogenesis.\u003c/p\u003e \u003cp\u003eBesides angiogenesis, ECM degradation has also been an important aspect of TME remodeling[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The association analysis between C5orf46 and ECM degradation revealed statistical significant positive correlations in KIRC included multiple cancers for instance BLCA, BRCA, KIRP, PAAD, HNSC, CHOL, SKCM, THCA and LIHC. Additionally, considering ECM degradation is not only a type of TME structures remodeling, but also a critical step for cancer metastasis, to be more comprehensively understanding the potential effect C5orf46 has on cancer metastasis, we also analyzed the association between the gene and 29 cytoskeleton dynamics related genes that were related with actin filaments stabilization, F-actin polymerization and actin-myosin contractile force generation, but only barely mild correlation was detected (data not shown).\u003c/p\u003e \u003cp\u003eMoreover, based on the fact that EMT is also an important step in cancer formation which is characterized by the loss of E-cadherin expression ,gain of N-cadherin as well as regulators for instance snail, proteases, and some totipotent transcription factors expression[\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In the study, we selected 14 genes that were well known to be EMT related and analyzed their correlation with C5orf46, and discovered statistical significant positive correlation in multiple cancers including BLCA, BRCA, CESC, CHOL, HNSC, COAD, KIRP, and LUSC, however, the correlation was not significant in KIRC. Meanwhile, as for the DNA deficiency and repair system, none specific correlation was observed between C5orf46 gene expression and cancers HRR related gene signatures.\u003c/p\u003e \u003cp\u003eIncreasing studies have been revealing that exosomes participates in tumor immune escape by delivering its contained molecules, such as proteins, mRNA, and miRNA to certain receptor TME cells[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. For instance, exosomes from lung cancer cells have been reported to induce immune escape by reducing T cell activity, expressing PD-L1, and promoting tumor growth. In the study, the potential effect of C5orf46 has on cancers immune infiltration landscape was explored. And the result revealed that C5orf46 strongly correlated with the distribution of multiples TICs, most significantly macrophages, CD4\u0026thinsp;+\u0026thinsp;T cell and CD8\u0026thinsp;+\u0026thinsp;T cells. Although the results are still in dispute in KIRC, the observed CTL dysfunction level difference between high-C5orf46 expression and low expression samples in multiple cancers indicating the potential regulation F5orf46 gene has on certain types of cancers immune environment modulation.\u003c/p\u003e \u003cp\u003eFurther, for preliminary evaluating the potential of C5orf46 as a probable drug target, we explored the gene expression association with cancer patients response to certain therapies, and discovered that although C5orf46 correlates with the TMB and MSI status in some cancers, none statistical significant expression difference of gene has been observed between the responders and non-responders of patients after receiving immune therapies drugs including anti PD-1, PD-L1 and CTLA4 inhibitors. Meanwhile, as for the chemotherapy drugs, C5orf46 expression was significantly different in the non-responders comparing to responders after receiving certain drugs in BRCA, colorectal cancer and GBM. Although deeper in vitro experiments validation and clinical trials were needed before clinical application of these drugs, the results shall provide promising insights for further clinical researches of C5orf46 gene functions in cancers.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, based on TCGA pan-cancer data and certain local hospital patients samples, the complex and comprehensive roles of C5orf46 gene in cancers were preliminary explored. C5orf46 expression was aberrant up-regulated in various cancers which correlates directly with worse patients OS and shorter RFS. Meanwhile, the gene was also supported to participate in the regulation of TME angiogenesis, ECM degradation, EMT transition as well as cancer stemness. Significantly, C5orf46 was also involved in cancer immunity and might work as a potential biomarker for certain chemotherapy drugs sensitivity, the results shall provide meaningful insights for better understanding the molecular mechanism behind C5orf46 regulation on cancers development.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAbbreviations for TCGA cancer types: Bladder urothelial carcinoma (BLCA), Breast invasive carcinoma (BRCA), Colon adenocarcinoma (COAD), Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), Cholangio carcinoma (CHOL), Esophageal carcinoma (ESCA), Glioblastoma multiforme (GBM), Head and neck squamous cell carcinome (HNSC), Kidney renal papillary cell carcinoma (KIRP), Kidney clear cell carcinoma (KIRC), Pancreatic adenocarcinoma (PAAD), Liver hepatocellular carcinoma (LIHC), Lung squamous cell carcinoma (LUSC), Lung adenocarcinoma (LUAD), Thymoma (THYM), Ovarian serous cystadenocarcinoma (OV), Mesothelioma (MESO), Pheochromocytoma and paraganglioma (PCPG), Prostate adenocarcinoma (PRAD), Rectum adenocarcinoma (READ), Stomach adenocarcinoma (STAD), Thyroid carcinoma (THCA), Uterine corpus endometrial carcinoma (UCEC), Uterine carcinosarcoma (UCS).\u003c/p\u003e\n\u003cp\u003eOther abbreviations: Protein-protein interaction network (PPI), Overall survival rate (OS), Recurrence free survival rate (RFS), Extra cellular matrix (ECM), Tumor microenvironment (TME), Epithelial Mesenchymal Transition (EMT), Homologous Recombination Repair (HRR), Tumor infiltrating cell (TIC), Cytotoxic T lymphocytes (CTLs), Tumor mutation burden (TMB), Microsatellite instability (MSI). \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthnic approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll of the local hospital patients samples that were used for IHC experiments were obtained from hospital BioBank (Second Hospital of ShanXi Medical University, ShanXi Province, China). Informed consent with signed signatures of the potential scientific application of the samples have been obtained from patients at the same time they made the donation to BioBank. The certain number of Biobank samples that were used in this study was approval by Hospital Institutional Board (Second Hospital of ShanXi Medical University, ShanXi Province, China). All methods were carried out in accordance with relevant guidelines and regulations or declaration of Helsinki. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTCGA pan-cancer profiles were analyzed in the study, which was downloaded from UCSC Xena (https://www.cancer.gov/ccg/research/genome-sequencing/tcga).\u0026nbsp;All data generated or analyzed during this study are included in the article and supplementary files.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll of the authors agreed the publication of the paper and declare no conflicts of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was supported by China central government funds for guiding local scientific and technological development (YDZJSX2021A042), the Science project from Health Commission of ShanXi Province (2023103) and grants of Natural Science Foundation of ShanXi Province in China (202203021222393, 202303021222333, 202403021211135).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXW and JL designed the study and drafted the manuscript, contributed equally to the whole study. HY, FW, LM, SL and NS performed the data collecting and analysis. ZY and LG participated in data interpretation and study design, WM and CW were involved in the drafting and critical revision of manuscript. As the corresponding authors, both WM and CW have full access to all data of the manuscript, CW made the eventual decision to submit the article for publication. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely acknowledge TCGA database for providing the platform and the rich data and information resources for cancers analysis, we also appreciate the science projects from worldwide for uploading their meaningful data on the platform.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ede Visser KE, Joyce JA: \u003cstrong\u003eThe evolving tumor microenvironment: From cancer initiation to metastatic outgrowth\u003c/strong\u003e. \u003cem\u003eCancer Cell \u003c/em\u003e2023, \u003cstrong\u003e41\u003c/strong\u003e(3):374-403.\u003c/li\u003e\n\u003cli\u003eWang JJ, Lei KF, Han F: \u003cstrong\u003eTumor microenvironment: recent advances in various cancer treatments\u003c/strong\u003e. \u003cem\u003eEur Rev Med Pharmacol Sci \u003c/em\u003e2018, \u003cstrong\u003e22\u003c/strong\u003e(12):3855-3864.\u003c/li\u003e\n\u003cli\u003eZhang Y, Han X, Nie G: \u003cstrong\u003eResponsive and activable nanomedicines for remodeling the tumor microenvironment\u003c/strong\u003e. \u003cem\u003eNat Protoc \u003c/em\u003e2021, \u003cstrong\u003e16\u003c/strong\u003e(1):405-430.\u003c/li\u003e\n\u003cli\u003eSarode P, Schaefer MB, Grimminger F, Seeger W, Savai R: \u003cstrong\u003eMacrophage and Tumor Cell Cross-Talk Is Fundamental for Lung Tumor Progression: We Need to Talk\u003c/strong\u003e. \u003cem\u003eFront Oncol \u003c/em\u003e2020, \u003cstrong\u003e10\u003c/strong\u003e:324.\u003c/li\u003e\n\u003cli\u003eCarnevalli LS, Ghadially H, Barry ST: \u003cstrong\u003eTherapeutic Approaches Targeting the Natural Killer-Myeloid Cell Axis in the Tumor Microenvironment\u003c/strong\u003e. \u003cem\u003eFront Immunol \u003c/em\u003e2021, \u003cstrong\u003e12\u003c/strong\u003e:633685.\u003c/li\u003e\n\u003cli\u003eBejarano L, Jordao MJC, Joyce JA: \u003cstrong\u003eTherapeutic Targeting of the Tumor Microenvironment\u003c/strong\u003e. \u003cem\u003eCancer Discov \u003c/em\u003e2021, \u003cstrong\u003e11\u003c/strong\u003e(4):933-959.\u003c/li\u003e\n\u003cli\u003eZhou L, Lv T, Zhang Q, Zhu Q, Zhan P, Zhu S, Zhang J, Song Y: \u003cstrong\u003eThe biology, function and clinical implications of exosomes in lung cancer\u003c/strong\u003e. \u003cem\u003eCancer Lett \u003c/em\u003e2017, \u003cstrong\u003e407\u003c/strong\u003e:84-92.\u003c/li\u003e\n\u003cli\u003eAghabozorgi AS, Ahangari N, Eftekhaari TE, Torbati PN, Bahiraee A, Ebrahimi R, Pasdar A: \u003cstrong\u003eCirculating exosomal miRNAs in cardiovascular disease pathogenesis: New emerging hopes\u003c/strong\u003e. \u003cem\u003eJ Cell Physiol \u003c/em\u003e2019, \u003cstrong\u003e234\u003c/strong\u003e(12):21796-21809.\u003c/li\u003e\n\u003cli\u003eDeep G, Panigrahi GK: \u003cstrong\u003eHypoxia-Induced Signaling Promotes Prostate Cancer Progression: Exosomes Role as Messenger of Hypoxic Response in Tumor Microenvironment\u003c/strong\u003e. \u003cem\u003eCrit Rev Oncog \u003c/em\u003e2015, \u003cstrong\u003e20\u003c/strong\u003e(5-6):419-434.\u003c/li\u003e\n\u003cli\u003eZhang HG, Grizzle WE: \u003cstrong\u003eExosomes and cancer: a newly described pathway of immune suppression\u003c/strong\u003e. \u003cem\u003eClin Cancer Res \u003c/em\u003e2011, \u003cstrong\u003e17\u003c/strong\u003e(5):959-964.\u003c/li\u003e\n\u003cli\u003ede Freitas RCC, Hirata RDC, Hirata MH, Aikawa E: \u003cstrong\u003eCirculating Extracellular Vesicles As Biomarkers and Drug Delivery Vehicles in Cardiovascular Diseases\u003c/strong\u003e. \u003cem\u003eBiomolecules \u003c/em\u003e2021, \u003cstrong\u003e11\u003c/strong\u003e(3).\u003c/li\u003e\n\u003cli\u003eJiang Y, Wang X, Li L, He J, Jin Q, Long D, Liu C, Zhou W, Liu K: \u003cstrong\u003eA systematic analysis of C5ORF46 in gastrointestinal tumors as a potential prognostic and immunological biomarker\u003c/strong\u003e. \u003cem\u003eFront Genet \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e:926943.\u003c/li\u003e\n\u003cli\u003eMa M, Zhang Z, Liu Y, Li Z, Fu S, Chen Q, Wang S: \u003cstrong\u003ePreliminary study on the role of the C5orf46 gene in renal cancer\u003c/strong\u003e. \u003cem\u003eTransl Oncol \u003c/em\u003e2022, \u003cstrong\u003e21\u003c/strong\u003e:101442.\u003c/li\u003e\n\u003cli\u003eZhou YJ, Liu JM, Liu B, Wang ZX, Fan XY, Huang PZ, Huang YX, Sun JN, Chen QQ, Shen HM: \u003cstrong\u003e[Significance of high expression of C5orf46 in gastric cancer and potential intervention of tarditional Chinese medicine based on bioinformatics, molecular docking, and cell experiments]\u003c/strong\u003e. \u003cem\u003eZhongguo Zhong Yao Za Zhi \u003c/em\u003e2023, \u003cstrong\u003e48\u003c/strong\u003e(9):2368-2378.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTCGA-UCSC software\u003c/strong\u003e. In: \u003cem\u003ehttps://xenabrowsernet/.\u003c/em\u003e Accessed 07 Apr 2022.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eProtParam software\u003c/strong\u003e. In: \u003cem\u003ehttps://webexpasyorg/protparam/.\u003c/em\u003e Accessed 21 Apr 2023.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eProtScale software\u003c/strong\u003e. In: \u003cem\u003ehttps://webexpasyorg/protscale/.\u003c/em\u003e Accessed 27 Apr 2023.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eUniprot software\u003c/strong\u003e. In: \u003cem\u003ehttps://wwwuniprotorg/.\u003c/em\u003e Accessed 25 Apr 2023.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eGeneCards software\u003c/strong\u003e. In: \u003cem\u003ehttps://wwwgenecardsorg/.\u003c/em\u003e Accessed 09 May 2023.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHumanProteinAtlas software\u003c/strong\u003e. In: \u003cem\u003ehttps://wwwproteinatlasorg/.\u003c/em\u003e Accessed 23 May 2023.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eUALCAN software\u003c/strong\u003e. In: \u003cem\u003ehttp://ualcanpathuabedu/analysishtml.\u003c/em\u003e Accessed 24 May 2023.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKalpan-Meier software\u003c/strong\u003e. In: \u003cem\u003ehttp://kmplotcom/analysis/.\u003c/em\u003e Accessed 11 Jun 2023.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ecBioPortal software\u003c/strong\u003e. In: \u003cem\u003ehttps://wwwcbioportalorg/.\u003c/em\u003e Accessed 13 Jun 2023.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSTRING software\u003c/strong\u003e. In: \u003cem\u003ehttps://string-dborg/.\u003c/em\u003e Accessed 27 Jun 2023.\u003c/li\u003e\n\u003cli\u003eMalta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN, Kaminska B, Huelsken J, Omberg L, Gevaert O\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMachine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation\u003c/strong\u003e. \u003cem\u003eCell \u003c/em\u003e2018, \u003cstrong\u003e173\u003c/strong\u003e(2):338-354 e315.\u003c/li\u003e\n\u003cli\u003eLiang X, Zhang H, Wang Z, Zhang X, Dai Z, Zhang J, Luo P, Zhang L, Hu J, Liu Z\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eJMJD8 Is an M2 Macrophage Biomarker, and It Associates With DNA Damage Repair to Facilitate Stemness Maintenance, Chemoresistance, and Immunosuppression in Pan-Cancer\u003c/strong\u003e. \u003cem\u003eFront Immunol \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e:875786.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTIDE software\u003c/strong\u003e. In: \u003cem\u003ehttp://tidedfciharvardedu/login/.\u003c/em\u003e Accessed 02 Aug 2023.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eACLBI software\u003c/strong\u003e. In: \u003cem\u003ehttps://wwwaclbicom/static/indexhtml#/.\u003c/em\u003e Accessed 09 Aug 2023.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eROC-Plotter software\u003c/strong\u003e. In: \u003cem\u003ehttps://wwwrocplotorg/.\u003c/em\u003e Accessed 17 Aug 2023.\u003c/li\u003e\n\u003cli\u003eHanahan D, Weinberg RA: \u003cstrong\u003eHallmarks of cancer: the next generation\u003c/strong\u003e. \u003cem\u003eCell \u003c/em\u003e2011, \u003cstrong\u003e144\u003c/strong\u003e(5):646-674.\u003c/li\u003e\n\u003cli\u003eDominiak A, Chelstowska B, Olejarz W, Nowicka G: \u003cstrong\u003eCommunication in the Cancer Microenvironment as a Target for Therapeutic Interventions\u003c/strong\u003e. \u003cem\u003eCancers (Basel) \u003c/em\u003e2020, \u003cstrong\u003e12\u003c/strong\u003e(5).\u003c/li\u003e\n\u003cli\u003evan Niel G, Carter DRF, Clayton A, Lambert DW, Raposo G, Vader P: \u003cstrong\u003eChallenges and directions in studying cell-cell communication by extracellular vesicles\u003c/strong\u003e. \u003cem\u003eNat Rev Mol Cell Biol \u003c/em\u003e2022, \u003cstrong\u003e23\u003c/strong\u003e(5):369-382.\u003c/li\u003e\n\u003cli\u003eWei R, Wang Z, Zhang Y, Wang B, Shen N, E L, Li X, Shang L, Shang Y, Yan W\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eBioinformatic analysis revealing mitotic spindle assembly regulated NDC80 and MAD2L1 as prognostic biomarkers in non-small cell lung cancer development\u003c/strong\u003e. \u003cem\u003eBMC Med Genomics \u003c/em\u003e2020, \u003cstrong\u003e13\u003c/strong\u003e(1):112.\u003c/li\u003e\n\u003cli\u003eWei R, Qi G, Zeng Z, Shen N, Wang Z, Shen H, Gao L, Song C, Ma W, Wang C: \u003cstrong\u003eIMUP and GPRC5A: two newly identified risk score indicators in pancreatic ductal adenocarcinoma\u003c/strong\u003e. \u003cem\u003eCancer Cell Int \u003c/em\u003e2021, \u003cstrong\u003e21\u003c/strong\u003e(1):620.\u003c/li\u003e\n\u003cli\u003eWang Z, Zeng Z, Gao F, Gui Z, Du J, Shen N, Shang Y, Yang Z, Shang L, Wei R\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eOsteosarcoma transcriptome data exploration reveals STC2 as a novel risk indicator in disease progression\u003c/strong\u003e. \u003cem\u003eBMC Med Genomics \u003c/em\u003e2023, \u003cstrong\u003e16\u003c/strong\u003e(1):30.\u003c/li\u003e\n\u003cli\u003eLee SH, Jeong D, Han YS, Baek MJ: \u003cstrong\u003ePivotal role of vascular endothelial growth factor pathway in tumor angiogenesis\u003c/strong\u003e. \u003cem\u003eAnn Surg Treat Res \u003c/em\u003e2015, \u003cstrong\u003e89\u003c/strong\u003e(1):1-8.\u003c/li\u003e\n\u003cli\u003eHicklin DJ, Ellis LM: \u003cstrong\u003eRole of the vascular endothelial growth factor pathway in tumor growth and angiogenesis\u003c/strong\u003e. \u003cem\u003eJ Clin Oncol \u003c/em\u003e2005, \u003cstrong\u003e23\u003c/strong\u003e(5):1011-1027.\u003c/li\u003e\n\u003cli\u003eMomeny M, Sabourinejad Z, Zarrinrad G, Moghaddaskho F, Eyvani H, Yousefi H, Mirshahvaladi S, Poursani EM, Barghi F, Poursheikhani A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAnti-tumour activity of tivozanib, a pan-inhibitor of VEGF receptors, in therapy-resistant ovarian carcinoma cells\u003c/strong\u003e. \u003cem\u003eSci Rep \u003c/em\u003e2017, \u003cstrong\u003e7\u003c/strong\u003e:45954.\u003c/li\u003e\n\u003cli\u003eLorenc P, Sikorska A, Molenda S, Guzniczak N, Dams-Kozlowska H, Florczak A: \u003cstrong\u003ePhysiological and tumor-associated angiogenesis: Key factors and therapy targeting VEGF/VEGFR pathway\u003c/strong\u003e. \u003cem\u003eBiomed Pharmacother \u003c/em\u003e2024, \u003cstrong\u003e180\u003c/strong\u003e:117585.\u003c/li\u003e\n\u003cli\u003eMonaci S, Coppola F, Filippi I, Falsini A, Carraro F, Naldini A: \u003cstrong\u003eTargeting hypoxia signaling pathways in angiogenesis\u003c/strong\u003e. \u003cem\u003eFront Physiol \u003c/em\u003e2024, \u003cstrong\u003e15\u003c/strong\u003e:1408750.\u003c/li\u003e\n\u003cli\u003eSkog J, Wurdinger T, van Rijn S, Meijer DH, Gainche L, Sena-Esteves M, Curry WT, Jr., Carter BS, Krichevsky AM, Breakefield XO: \u003cstrong\u003eGlioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers\u003c/strong\u003e. \u003cem\u003eNat Cell Biol \u003c/em\u003e2008, \u003cstrong\u003e10\u003c/strong\u003e(12):1470-1476.\u003c/li\u003e\n\u003cli\u003eGesierich S, Berezovskiy I, Ryschich E, Zoller M: \u003cstrong\u003eSystemic induction of the angiogenesis switch by the tetraspanin D6.1A/CO-029\u003c/strong\u003e. \u003cem\u003eCancer Res \u003c/em\u003e2006, \u003cstrong\u003e66\u003c/strong\u003e(14):7083-7094.\u003c/li\u003e\n\u003cli\u003eNajafi M, Farhood B, Mortezaee K: \u003cstrong\u003eExtracellular matrix (ECM) stiffness and degradation as cancer drivers\u003c/strong\u003e. \u003cem\u003eJ Cell Biochem \u003c/em\u003e2019, \u003cstrong\u003e120\u003c/strong\u003e(3):2782-2790.\u003c/li\u003e\n\u003cli\u003eJablonska-Trypuc A, Matejczyk M, Rosochacki S: \u003cstrong\u003eMatrix metalloproteinases (MMPs), the main extracellular matrix (ECM) enzymes in collagen degradation, as a target for anticancer drugs\u003c/strong\u003e. \u003cem\u003eJ Enzyme Inhib Med Chem \u003c/em\u003e2016, \u003cstrong\u003e31\u003c/strong\u003e(sup1):177-183.\u003c/li\u003e\n\u003cli\u003eJihu Y, Leng R, Liu M, Ren H, Xie D, Yao C, Yan H: \u003cstrong\u003eAngiotensin (1-7) Inhibits Transforming Growth Factor-Beta1-Induced Epithelial-Mesenchymal Transition of Human Keratinocyte Hacat Cells in vitro\u003c/strong\u003e. \u003cem\u003eClin Cosmet Investig Dermatol \u003c/em\u003e2024, \u003cstrong\u003e17\u003c/strong\u003e:1049-1058.\u003c/li\u003e\n\u003cli\u003eGao X, Lin X, Lin M, Lan Y, Wang Y, Wu R, Li J, Huang C, Zhong D: \u003cstrong\u003eSilencing Rac1 and Prex1 Inhibit Epithelial-Mesenchymal Transition in Human Gastric Cancer Cells Induced by Transforming Growth Factor-beta1\u003c/strong\u003e. \u003cem\u003eTurk J Gastroenterol \u003c/em\u003e2023, \u003cstrong\u003e34\u003c/strong\u003e(9):975-981.\u003c/li\u003e\n\u003cli\u003eShrestha R, Bridle KR, Crawford DHG, Jayachandran A: \u003cstrong\u003eImmune checkpoint molecules are regulated by transforming growth factor (TGF)-beta1-induced epithelial-to-mesenchymal transition in hepatocellular carcinoma\u003c/strong\u003e. \u003cem\u003eInt J Med Sci \u003c/em\u003e2021, \u003cstrong\u003e18\u003c/strong\u003e(12):2466-2479.\u003c/li\u003e\n\u003cli\u003eRomeo E, Caserta CA, Rumio C, Marcucci F: \u003cstrong\u003eThe Vicious Cross-Talk between Tumor Cells with an EMT Phenotype and Cells of the Immune System\u003c/strong\u003e. \u003cem\u003eCells \u003c/em\u003e2019, \u003cstrong\u003e8\u003c/strong\u003e(5).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 A\u003c/strong\u003e\u003cstrong\u003essociation between C5orf46 expression and KIRC clinical features\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 179px;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 278px;\"\u003e\n \u003cp\u003eC5orf46 expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eLow expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eHigh expression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 179px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e8 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e16 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.042*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e10 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e5 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 179px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026le;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3 (60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e2 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026gt;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e15 (44.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e19 (55.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 179px;\"\u003e\n \u003cp\u003eWHO/ISUP grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eG1/G2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e14 (60.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e9 (39.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.027*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eG3/G4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e4 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e12 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 179px;\"\u003e\n \u003cp\u003eTumor diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026le;4cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e6 (54.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e5 (45.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e4~7cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e10 (41.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e14 (58.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026gt;7cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e2 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 179px;\"\u003e\n \u003cp\u003eMembrane invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e14 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e15 (51.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e4 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e6 (60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 179px;\"\u003e\n \u003cp\u003eNeurovascular invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e2 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e17 (47.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e19 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 179px;\"\u003e\n \u003cp\u003eRenal sinus invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e4 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e16 (48.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e17 (51.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 179px;\"\u003e\n \u003cp\u003eRenal pelvis invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e4 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e14 (41.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e20 (58.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 179px;\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eIa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e5 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e5 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eIb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e10 (63.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e14 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e2 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 179px;\"\u003e\n \u003cp\u003eBone metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e4 (23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e13 (76.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e14 (63.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e8 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e* Represents p\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 2. The\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e8 compounds correlated with C5orf46 based on RNAact Drug platform\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"622\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompounds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOmics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpearman.stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpearman.fdr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e3-bromo-4-n,n-bis-2\u0026apos;-cyanoethylaminobenzylidene rhodanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eExpression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eCellMiner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003ePanobinostat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eExpression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eCCLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e7.643e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003esulfonaphtholazoresorcinol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eMethylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eCellMiner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e1,4-dimethoxy-7-azaisoindole[2,1-a]quinoxalin-6(5h)-one\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eMethylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eCellMiner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e1,3-diphenyl-4-(3-phenyl-4,5-dihydro-1H-pyrazol-5-yl)-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eExpression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eCellMiner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003emercury(acetyloxy)(pentamethylphenyl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eMethylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eCellMiner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e-0.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003evaracin trifluoroacetate salt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eExpression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eCellMiner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e-0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e8-Chloro-adenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eExpression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eCellMiner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e-0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Pan-cancer analysis, C5orf46, tumor microenvironment, prognosis risk, immune infiltration, drug target","lastPublishedDoi":"10.21203/rs.3.rs-6650741/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6650741/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eC5orf46 is a recently discovered tumor progression related gene whose function in most cancers are still unknown, especially its potential regulation on tumor microenvironment (TME). The aim of the study is to explore C5orf46 gene function in kidney renal clear cell carcinoma (KIRC) included human pan-cancer for potential clinical application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eThe study started with the physicochemical property of C5orf46, and then the gene expression as well as alteration patterns in diverse cancers, followed by post transcription modulation of the gene and then survival analysis. Moreover, the correlation between C5orf46 and multiple cancer TME related parameters including angiogenesis, extracellular matrix (ECM) degradation and immune infiltration were in succession explored. Further, C5orf46 association with others critical cancer features for instance cancer stemness, tumor epithelial mesenchymal transition (EMT) and DNA repair were also investigated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eFirstly, physicochemical properties including the aminoacid composition, estimated molecular weight and protein half life of C5orf46 gene were in succession computed. Then, based on gene expression as well as survival analysis result, C5orf46 was shown to be up-regulated in various human cancers, of which KIRC was the top cancer with highest C5orf46 expression difference between cancer and corresponding normal tissues. And the changed expression was partly due to DNA methylation modulation. Meanwhile, of more clinical significance, the up-regulated C5orf46 expression was correlated with both worse patients overall survival and shorter recurrence free survival. Moreover, the association between C5orf46 and multiple critical cancer traits including microenvironment angiogenesis, immune infiltration, ECM degradation and cancer EMT were validated. Further, C5orf46 gene was indicated to correlate with the sensitivity of several chemotherapy related drugs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eBased on TCGA pan-cancer data and local hospital samples validation, C5orf46 was indicated to potentially works as an oncogene in diverse cancers, and the gene was associated with multiple critical cancers traits.\u003c/p\u003e","manuscriptTitle":"C5orf46: a Promising Prognosis Risk Indicator with Implication in the Remodeling of KIRC included Pan-cancer Tumor Microenvironment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 08:13:38","doi":"10.21203/rs.3.rs-6650741/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":"601948ee-8c9f-4a3e-a177-94964e76527d","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T08:13:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-08 08:13:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6650741","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6650741","identity":"rs-6650741","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}