Fusobacterium nucleatum, Streptococcus sanguinis, F. Hwasookii, Anaerococcus mediterraneensis and further species enriched in primary CRCs. The primary CRC of one patient was enriched for F. alocis, S. anginosus, Parvimonas micra and Gemella sp. 948. Enrichment of Escherichia coli strains IAI1, SE11, K‐12 and M8 was observed in metastases together with coliphages enterobacteria phage φ80 and Escherichia phage VT2φ_272. Virome analysis showed that phages were the most preponderant viral species (46%), the main families being Myoviridae, Siphoviridae and Podoviridae. Primary CRCs were enriched for bacteriophages, showing five phages  together with their pathogenic hosts in contrast to normal tissues. The most frequently detected, and Blast‐confirmed, viruses included human endogenous retrovirus K113, human herpesviruses 7 and 6B, Megavirus chilensis, cytomegalovirus (CMV) and Epstein–Barr virus (EBV), with one patient showing EBV enrichment in primary tumour and metastases. EBV was PCR‐validated in 80 pairs of CRC primary tumour and their corresponding normal tissues; in 21 of these pairs (26.3%), it was detectable in primary tumours only. The number of viral species was increased and bacterial species decreased in CRCs compared with normal tissues, and we could discriminate primary CRCs from metastases and normal tissues by applying the Hutcheson t‐test on the Shannon indices based on viral and bacterial species. Taken together, our results descriptively support hypotheses on microorganisms as potential (co)risk factors of CRC and extend putative suggestions on critical microbiome species in CRC metastasis.The paucity of microbiome studies at intestinal tissues has contributed to a yet limited understanding of potential viral and bacterial cofactors of colorectal cancer (CRC) carcinogenesis or progression. We analysed whole‐genome sequences of CRC primary tumours, their corresponding metastases and matched normal tissue for sequences of viral, phage and bacterial species. Bacteriome analysis showed  This work analysed the presence of sequences of bacteria and viruses in primary colorectal cancer (CRC) tissues, compared with corresponding normal colorectal tissues and metastases. We observed a higher viral species diversity and lower bacterial species diversity in primary CRCs than in matched normal tissues. Some microbial species were enriched in primary CRCs and some metastases, particularly phages.  AcMNPVautographa californica nucleopolyhedrovirusCDCrohn’s diseaseCMVcytomegalovirusCRCcolorectal cancerEBVEpstein–Barr virusEMCVencephalomyocarditis virusHERV‐K113human endogenous retrovirus K113HHV‐6Bhuman herpesvirus 6BHHV‐7human herpesvirus 7HPVhuman papillomavirusHPyV7human polyomavirus 7IBDinflammatory bowel diseaseJCVJC virusSV40simian virus 40UCulcerative colitisVHDVirus‐Host DatabaseWGSwhole‐genome sequencing1Fusobacterium nucleatum, Bacteroides fragilis, Streptococcus bovis and Enterococcus faecalis are the most frequently cited [Colorectal cancer (CRC) is the third most prevalent form of cancer worldwide, with a global incidence of over 1.4 million cases annually , 3. Abouly cited .Polyomaviridae family [Moreover, viruses could play a yet underestimated role in the carcinogenic process. In the past decades, several studies have pointed out an increased prevalence of viral infections in patients suffering from CRC or associated inflammatory conditions, such as inflammatory bowel disease (IBD), which include Crohn’s disease (CD) and ulcerative colitis (UC) . IBD is e family , 20, 21.Caudovirales  has been observed in patients affected by these diseases [Recent microbiome work has expanded beyond human‐infecting viruses and has highlighted, for example, the importance of bacteria‐infecting viruses (phages) in CRC and IBD. For instance, an increased prevalence of phage species belonging to the order of diseases , 24, 25.diseases , 31, 32.diseases . This asdiseases . Furtherdiseases , 35, faediseases . The fewdiseases , 38.The paucity of microbiome studies based on intestine tissues has contributed to the poor understanding of the conditions that foster CRC. Specifically, there is certainly a lack of research based on metastases. About one‐quarter of CRC cases progress towards metastasis , 40; thuIn this study, we (re‐)analysed whole‐genome sequences (WGS), which we generally described in previous work , to asse22.1All of the samples were completely anonymized and handled exclusively in strictly anonymized conditions. Tissues had been generally obtained from the biobank of the Medical Faculty Mannheim (Dept. of Surgery), University of Heidelberg, Germany, and the biobanking and tissue sampling approved by the Ethical Committee of this institution. The primary colorectal tumours, matched healthy colorectal tissues, and corresponding metastases re‐analysed here were described previously , the ana2.2Ensembl database. Viral reference sequences (n = 10 384) were retrieved from the National Center for Biotechnology Information (NCBI) database using Entrez utilities (https://www.ncbi.nlm.nih.gov/books/NBK25497/) and were concatenated into a single sequence  using mingle v. 2.3 [bwa v. 0.7.17 [Genomic sequences from the human assembly issue 38 built 12 (GChr38) were obtained from the e v. 2.3 . Human a. 0.7.17 .trimmomatic v. 0.38 [bwa‐mem v. 0.7.17 [sambamba v. 0.6.7 [Mingle to determine the corresponding viral species. Reads mapping to phage φX174, which was used as quality control in the libraries’ preparation, were discarded.Sequencing adapters and reads with quality below 33 phred‐score units were removed with  v. 0.38 , 45, 46.. 0.7.17 . De‐duplv. 0.6.7 , and onlsamtools v. 1.9‐18 and qualimap v. 2.2.1 [blast+ v. 2.9.0 [clustal omega v. 1.2.4 [blastx v. 2.9.0 [delly v. 0.8.3 [The quality of the alignment and sample coverage was assessed with v. 2.2.1 , 50. blav. 2.9.0 , 52 was v. 1.2.4 . To furtv. 2.9.0 . Sequencv. 0.8.3 .m of each dNTP, 1 μm of forward primer (5´‐CCGCTCCTACCTGCAATATCA‐3´), 1 μm of reverse primer (5´‐CAATAACGGCAGCAAGCTTG −3´), 1 mm MgCl2, 1.25 units of enzyme, 100 ng template DNA and water to 25 μL. The amplification conditions were as follows: 5 min at 95 °C followed by 30 cycles of 1 min at 95 °C, 1 min at 58 °C, 1 min at 72 °C and final extension for 7 min at 72 °C.In a series of 80 colorectal primary tumours and corresponding normal colorectal tissues, EBV DNA was detected as a validation by end‐point PCR targeting EBNA1 as previously described . BrieflyTo address the question of cell types primarily affected within these tissues, the presence of EBV was detected by in situ hybridization with an EBER‐specific peptide nucleic acid probe, in conjunction with a PNA detection kit (Dako) following the manufacturer’s protocol as described . Images 2.3spades v. 3.13.0 [Virus‐Host Database (VHD) [PhageAI database [Reads not mapping to either the human chromosomes or the composite virus chromosomes were assembled with . 3.13.0 . Sequencse (VHD) . Classifdatabase .2.4julia v. 1.3.1  and bash v. 5.0.3 languages. Plotting, statistical analysis and hierarchical clustering were performed with r v. 3.6.1 [r package Qsutils [r package iNEXT [t‐test. The Hutcheson t‐test [r with the package ecoltest .Data processing was performed with v. 3.6.1 . The Shav. 3.6.1  and its  Qsutils . Species Qsutils , 65 was ge iNEXT . Statistn t‐test  was used33.1Myoviridae (n = 154), Siphoviridae (n = 79) and Podoviridae (n = 29) as the most represented , including the aforementioned HERV‐K113 (an endogenous retrovirus) and encephalomyocarditis virus (EMCV). There were 73, 404 and 24 viral species present either in normal tissues, primary colorectal tumours, or metastases only, respectively, and an additional three (27.3%) were common to both primary colorectal tumours and metastases: EBV, Qinghai Himalayan marmot astrovirus and Tipula oleracea nudivirus. EBV was present in the lung metastasis together with AcMNPV, Synechococcus phage S‐SM2 and HERV‐K113.Twelve cases previously described  with matidae n = 54, Siphoidae n = 54, SiphoSiphoviridae (n = 11), Myoviridae (n = 10) and Podoviridae (n = 7) as the most represented  and 15 (53.6%) were observed in primary colon tumours. Stratification of the phagial species by tissue type showed that one species (Enterobacteria phage λ) was common to all tissues types, one (Shigella phage SfII) was common to normal colorectal tissues and liver metastases. Enterobacteria phage VT2φ_272 and enterobacteria phage φ80, Shigella phage SfIV and enterobacteria phage mEp460 were confirmed specifically in one metastasis each. An additional 16 species were present only in primary carcinomas, and six were observed only in normal colorectal tissues. Overall, 87.5%, 70.6% and 100.0% of phages in in normal tissues, primary colorectal tumours and liver metastases were temperate, respectively. Filtering confirmed HERV‐K113, AcMNPV, phage λ (but not Synechococcus phage S‐SM2) as the most preponderant viral species across all tissue entities. HHV‐7 was present collectively in five patients, and specifically in the normal colorectal tissue and primary colorectal carcinomas of two patients as well as in the primary colorectal carcinoma and liver metastasis of another patient. HHV‐6B was confirmed collectively in four patients, and specifically in the normal colon tissue and primary colorectal carcinoma of one patient as well as the normal colon tissue and liver metastasis of another patient. CMV was present specifically in the primary colorectal carcinoma tissues of two patients. Torque teno (TT) viruses 16 and 24 were also confirmed in primary colorectal carcinomas. We also confirmed SV40 and HPyV7 in one case each . EMCV was specifically present in liver metastasis tissue. Of the aforementioned giant viruses, only megavirus chiliensis was confirmed in two patients. The phage crAssphage was confirmed in the normal colon tissues and the primary colorectal tumours of three patients. EBV was confirmed after BLAST filtering in the primary colorectal tumour and the lung metastasis of the same patient. Himalayan marmot astrovirus and Tipula oleracea nudivirus were not confirmed in the lung metastasis after Blast filtering.To avoid the possibility of false‐positive detection, we further filtered the data with the highly sensitive Blast alignment . To gain additional information on putative cell types infected with EBV within colorectal carcinoma tissues, the presence of EBV was further investigated in a subset of 5 samples using EBER staining (ISH)  and either liver or lung metastases  as well as primary colorectal tumours from either liver or lung metastases .Next, we sought to determine whether we could differentiate primary colorectal tumour tissues, corresponding normal colorectal tissues, and metastases based on the viral content. We used rarefaction analysis to calculate the estimated number of species normalized for the sample size, and the Shannon index to have a comparative value. Overall, there were an estimated 124 (95% CI: 60–296), 232 (95% CI: 99–674) and 57 (95% CI: 26–157) normalized viral species, in the normal, primary colorectal tumour and metastatic tissues, respectively  and Klebsiella pneumoniae concomitantly in primary colon tumour and liver metastasis. There were no bacterial species observed in the lung metastatic tissue. We found Fusobacterium nucleatum enriched only in the primary colorectal tumour sections of three patients in contrast to corresponding normal colorectal tissues, whereas Fusobacterium hwasookii was present in the primary colorectal tumour sections of two patients. Porphyromonas gingivalis was present in the normal colorectal tissue and primary colon carcinoma sections of one patient. Anaerococcus mediterraneensis and Prevotella denticola were observed in the primary colorectal tumours of two patients, as opposed to corresponding normal tissues. Among the 122 species observed only in primary colorectal tumours, the families Bacillaceae and Clostridiaceae represented the most represented, with 20 species each (16.4%), followed by the Streptococcaceae with 17 species (13.9%). Bacteroides fragilis was prevalent in normal colorectal tissues and also present in primary colorectal tumour sections. Streptococcus anginosus was observed in the normal colorectal tissue of one patient, and in the primary colorectal tumours as opposed to their matched normal tissues of two patients. Furthermore, among the species observed only in primary colorectal tumours, S. sanguinis, Filifactor alocis, Gemella sp. oral taxon 928 and Parvimonas micra were present in high frequency in one patient and were represented by a number of sequence assemblies in the range of 468–147, compared to a mean of 5.7, indicating their highly specific representation.We identified 518 bacterial species, corresponding to 143 bacterial families in our sample set. Overall, there were 391, 217 and 10 bacterial species in normal colorectal tissues, primary colorectal tumours and liver metastases, respectively, corresponding to a ratio of phages over bacteria of 0.02, 0.08 and 0.60. There were 122 species present only in primary colon tumour tissues, four strains of t‐test allowed discriminating normal tissues from both primary colorectal tumours  and metastases ; similarly, primary colorectal tumours could be discriminated from metastases .The normalized species count, obtained by rarefaction analysis, was 2370 (95% CI: 1701–3383), 1011 (95% CI: 693–1543) and 40 (95% CI: 14–209) for the normal, primary colorectal carcinoma and metastatic tissues . Our results indicated a certain level of parallelism between the presence of the host and that of the associated phage Table . Half of4The present work is one of the very few, if not the first so far, that carried out microbiome analysis on primary colorectal carcinoma and corresponding metastases in comparison to matched normal tissues. Although, certainly, our analysis from whole genomes so far is largely bioinformatic, our study suggests that some microorganisms, at least their sequences, are more prevalent in some primary colorectal tumours or metastases, this pertaining to viruses and bacteria, and—potentially most interesting since these were not yet systematically investigated in such a setting—(bacterio‐)phages.E. coli as a host, for example enterobacteria phage φ80 and Escherichia phage VT2φ 272 which, after Blast filtering, we even found specifically in metastatic tissues. We observed over half of the most frequent phages especially in primary tumours, two of which (Bacillus prophage PfEFR‐5 and Streptococcus phage φARI0462) showing their specific host  in primary tumour tissues only, not in corresponding normal tissues, which suggests that this might not have been due to contamination. This is hypothesized since the primary colorectal cancers and corresponding normal colorectal tissues were both acquired simultaneously, being exposed to the same microbiologic milieu of the same intestinal lumen within the same patient under the same settings, and therefore, they would have been exposed to the same contaminants. Interestingly, some of the hosts of the phages specifically observed in primary colorectal tumour tissues are known pathogens. For instance, Bacillus prophage PfEFR‐5 infects Bacillus cereus, a pathogen associated with food poisoning [B. cereus and S. pneumoniae in tumour samples only, in contrast to the corresponding normal colorectal tissue of the very same patient, suggested a sort of ‘linkage disequilibrium’ between these tissue types.In both primary colorectal cancers and metastases, we observed several species of phages, for example phage λ or crAssphage, crAssphage having been reported as the most common virus in the colon , 69, 70.oisoning . The obsF. nucleatum, S. anginosus and F. alocis, S. sanguinis, P. micra and Gemella oral taxon 928 confirm the results of other research groups [F. nucleatum, S. anginosus and S. sanguinis at high prevalence in cancer tissues  [Fusobacterium spp. are included in the passenger/driver model of CRC and have been reported to promote the formation of metastases [F. nucleatum and P. micra are commonly enriched in CRC sections [Fusobacterium hwasookii and Porphyromonas gingivalis in colorectal cancer tissues, together with high prevalence of Fusobacteria and Bacteroidetes [F. alocis and P. micra are prevalent at sites of periodontitis (a chronic inflammation of the gum that results in tooth loss) and in oral squamous cell carcinomas [S. sanguinis has been reported in cases of occult colon carcinomas [Prevotella denticola was enriched in colorectal cancer tissues [Klebsiella pneumoniae, although commonly associated with pneumonia, has been reported as a risk factor for colorectal cancer [S. anginosus is an opportunistic bacterium commonly observed in the oral and intestinal flora, but it is also enriched in esophageal and gastric cancers [Gemella is a genus of commensal bacteria of the mouth and gut whose main members (G. morbillorum and G. haemolysans) are usually isolated in abscesses and endocarditis [Anaerococcus mediterraneensis has been recently isolated in a case of vaginosis [The bacterial species we observed enriched, or exclusively, in primary colorectal carcinomas have been reported to show oncogenic or pro‐inflammatory potential. Our tumour‐specific findings of h groups , 79, 80.han 105) . Fusobactastases , 82. F. sections , 84, 85,roidetes . F. alocrcinomas , 88. S. rcinomas , 90, 91.l cancer , 94. S.  cancers , 96, and cancers . Gemellacarditis , 99, 100aginosis , suggestE. coli was commonly observed in our samples, we also observed an enrichment of some E. coli strains specifically in metastatic tissue. E. coli is both a commensal of the human gut but also an opportunistic species [E. coli, unbalancing the gut microbiome. On the other hand, phages can piggyback pathogenic species invading inner tissues. Therefore, our detection of coliphages might reflect these scenarios, and further data are needed to discriminate between these possibilities. Interestingly, it has been shown that this bacterium can use α‐haemolysin to invade the colon epithelium in a process known as ‘focal leak’ [E. coli (C25 and HBTEC‐1) have been reported to transcytose colonic cells [Although  species . Thus, lal leak’ . Specifiic cells . The poric cells . It has ic cells . The intic cells , 108, whic cells . In turnic cells . An impaic cells .The gut and the liver share a special relationship due to their connection via the portal vein, the biliary tract and systemic circulation. Hence, the presence of pathogenic bacteria in the gut might be reflected in the liver especially if there are leakages in the mucosal barrier in the intestine. Such a hypothesis is backed by extensive literature, demonstrating a linkage between intestinal dysbiosis and liver disease due to an increased permeability of the intestinal barrier. If certain bacteria or phages (viruses), or their disbalance, can be pathogenic for the colorectal tissue, they might affect the liver, too, in a similar or also slightly different way as compared to the intestinal tissue. Specifically, if particular pathogens, or their interaction, are able to induce inflammation in the gut, they might also induce inflammatory processes in the liver. However, with the exception of some specific systemically infecting viruses such as HCV, EBV, and others which are able to induce pro‐oncogenic pathways in cells including hepatocytes , 114, stNevertheless, the implications for the presence of bacteria and phages in the liver are still poorly understood. It is feasible to assume that these microorganisms might induce a chronic inflammation that can foster liver disease, albeit more experimental data are required to determine whether microorganisms might also induce metastasis. The fact that the ratio of phages to bacteria was much higher in metastases than in colorectal tissues might reflect the fact that phages are more likely to cross the gut epithelial barrier. Recent metagenomic analysis suggested that the induction of temperate phages might be responsible for propagating intestinal dysbiosis, resulting in an increased amount of phages in relation to bacteria , 116. ThEnterobacteriaceae family  in the inflamed gut [E. coli, in addition to B. fragilis, E. faecalis and F. nucleatum, all of which we found represented in our tissues analysed, have been suggested to be able to act as pathogens, questioning their in part uncritical use as probiotics [Enterobacteriaceae family, including strains of E. coli and K. pneumoniae, have been shown to release a genotoxin (colibactin) able to induce genetic damage, fostering the insurgence of cancer [Furthermore, recent metagenomic studies have reported the increased prevalence of members of the amed gut . Our obsobiotics , 121. Sef cancer , 123.per se, favouring the expansion of bacteria that promote inflammation or even carcinogenesis [The oncogenic potential of these bacteria, together with, at least in part, an enrichment in primary colorectal carcinomas we observed herein, suggest a putatively complex scenario of several infectious components which, in their interaction, might lead to a netto support of carcinogenesis and/or CRC progression. Towards this end, phages such as the ones we identified here could further support the growth of pathobionts, which can cause damage to intestinal cells, local inflammation and more. Thus, phages are increasingly acknowledged as a putative risk factor for CRC, whose importance has been underestimated in the past . As to fogenesis . Again, ogenesis , areas oogenesis . Finallyogenesis . Phages,ogenesis , 126. Thogenesis , 130. Teogenesis  are the Taken together, there are several mechanisms by which phages, and phage‐host interactions, impact the microbiome and pro‐inflammatory, or even pro‐carcinogenic/‐metastatic, conditions in the intestine. Thus, our findings can encourage mechanistic studies on particular bacteriophages as to their putative function in CRC carcinogenesis or progression in the future, especially since, despite the intense research ongoing to describe the microbial communities in the human intestinal tract of CRC and IBD , 38, 131Similar to the bacteria, many of the viral species we observed enriched in primary colorectal tumours and liver metastases, besides phages, have been reported to possess oncogenic and/or pro‐inflammatory potential. This is certainly true for EBV which we found in whole‐genome sequencing, including even in one metastasis after Blast filtering, and also by PCR in an independent series in which about a quarter of the analysed primary tumour/normal colorectal tissue sample pairs showed a specific positivity in the carcinomas. Regarding putative pro‐carcinogenic mechanisms, EBV has been reported to act several fold , 133: itP‐value = 0.115) [We observed other viruses with oncogenic potential. CMV infects preferentially neoplastic epithelium, where it can reach a prevalence of over 40% compared to < 6% in the surrounding normal tissues , and tum= 0.115) .HERV‐K113 is an endogenous retrovirus with a prevalence of up to 30% , and, unWe also observed EMCV, a zoonotic pathogen that has a seroprevalence ranging from two per cent to over 30% , 177. EMc‐MYC to identify breast cancer patients at a higher risk of mortality [Taking advantage of the observed presence of microorganisms within our samples, we tried to apply statistical methods to distinguish between normal colorectal, primary colorectal carcinoma and metastasis tissue types. Previous studies have applied the Shannon index for diagnostic purposes, for instance applying the genetic variability of ortality , 183. InThe much lower Shannon indices observed in metastatic sections was unsurprising since these tissues are expected to be rather sterile. Thus, the presence of some microorganisms in liver metastases still appears counter‐intuitive. Only HERV‐K113, an endogenous retrovirus, displayed a profile characterized by high sequence coverage associated with integration in the host’s chromosomes Fig. , thus, tCertainly, our study has limitations, including methodological ones. First, it was initially designed for whole‐genome screening. Consequently, no viral enrichment, or general enrichment for microbial genomes, had been carried out, nor included, in the processing of the samples. Thus, the low microbial biomass could have introduced a bias in the results, generating lower than expected findings in sequence copies, or in individual sequence coverage, of microbial sequences as compared to sequences from human chromosomes . Lower s5In conclusion, this is the first work carrying out metagenomic analysis on CRC metastases as compared to corresponding primary CRC tumours and normal colorectal tissues and that attempted to apply microbial richness calculations to differentiate these tissues. We showed that the microbial landscape might be used to differentiate primary colorectal tumours from nonmalignant tissue and metastases, in particular by using the Shannon index. Moreover, we highlighted particular species, especially including the previously not extensively considered species of (bacterio‐)phages, for future functional studies as to how they could contribute to colorectal carcinogenesis or even progression and metastasis, for example by creating permissive or nonpermissive microbial environments. Extending this analysis to broader sets of available samples, for example in multicentre approaches, could foster a better understanding of infectious agents as a potentially complex interplay of cofactors for CRC carcinogenesis and progression, and assess whether the use of microbial analysis could support precision medicine to pinpoint patients at increased risk for CRC or CRC metastasis.The authors declare no conflict of interest.LM mainly performed and coordinated the analyses within this paper. LM, TR, and JJML performed all aspects of the bioinformatics analysis, MLA acquired the samples and consents, coordinated the aspects associated with their collection, and led wet laboratory sample analysis and PCR. SD and HJD performed and interpreted EBER stainings. HA initiated the idea of virome, phage and bacteriome analysis in association with CRC carcinogenesis and metastasis, and coordinated the research and funding. All authors participated in the discussion and interpretation of data, the writing and/or revision, and approval of the manuscript.Fig. S1. Visualization of viral integration of human endogenous retrovirus K113 (HERV‐K113).Table S1. Read counts stratified by tissue type.Click here for additional data file.


The Sysmex XN‐V is derived from the new Sysmex XN series of human hematology analyzers. The main changes from the previously validated XT‐2000iV analyzer include an optic‐fluorescent analysis for platelets and nucleated RBC count.We aimed to validate the Sysmex XN‐V for canine blood according to American College for Veterinary Clinical Pathology and International Council for Standardization in Hematology recommendations.Canine EDTA blood specimens and quality control material were analyzed on the Sysmex XN‐V to evaluate imprecision, bias, linearity, a comparison with the XT‐2000iV analyzer, interference effects, carry‐over, and stability. We also verified previously established Sysmex XT‐2000iV reference intervals (RIs).Imprecision and bias were low (<5%) for most variables. Observed total error was lower than allowable total error for most measured variables except lymphocytes and monocytes. Visually determined linearity was excellent for all variables, except for lymphocytes. The correlation between the XN‐V and XT‐2000iV analyzers was high (>0.93) for all variables except MCHC and reticulocyte indices. Correlations between the Sysmex XN‐V and manual differential counts were good for neutrophils and eosinophils, acceptable for lymphocytes, and fair for monocytes. Hemolysis, lipemia, and to a lesser extent icterus, had significant effects on measured hemoglobin concentration and associated variables. Carry‐over was not visually observed for any variable. Changes in the Sysmex XN‐V measurements after storage at 4℃ and 24℃ were similar to those described for the Sysmex XT‐2000iV analyzer. The previously established Sysmex XT‐2000iV RIs can be used to interpret results from the Sysmex XN‐V analyzer for most variables except red blood cell distribution width and mean platelet volume.The performance of the Sysmex XN‐V analyzer was excellent and compared favorably with the Sysmex XT‐2000iV analyzer. The Sysmex XN‐V is a new analyzer, derived from the Sysmex XN series for analyzing human blood. The main changes from the XT‐2000iVValidation of a new instrument is recommended before its implementation. According to the American Society of Veterinary Clinical Pathology (ASVCP), validation includes an evaluation of linearity, repeatability, reproducibility, method comparison, hemolysis, lipemia, and bilirubin interference studies, a recovery study, reference interval (RI) determinations, and a detection limit study.The purpose of this study was to validate the Sysmex XN‐V hematology analyzer for canine blood analyses according to ASVCP and ICSH recommendations. We also compared the Sysmex XN‐V RIs with those of the XT‐2000iV hematology analyzer, which had been previously validated in canine blood samples.2This study was approved by the “Science et Santé animale” Ethics Committee (N° SSA_2019_002).2.1Experiments were performed on fresh canine K3‐EDTA blood samples  and two levels of manufacturer's quality control material . Fresh blood specimens were received for the hematologic evaluation at the Laboratoire Central de Biologie Médicale  to perform patient diagnostic workups or health checkups at the National Veterinary School of Toulouse. As the patients were presented to various disciplines , specimens were obtained from dogs with different diseases or conditions, thus covering a wide range of results and a wide variety of blood disorders. One labeled air‐dried blood smear was prepared within 1 hour of blood sampling and stained with a May‐Grünwald/Giemsa automatic stainer , and was then stored before microscopic evaluation. Prior to hematologic analysis, blood specimens were kept at room temperature (24℃), placed on an agitator  for 20 minutes, and then gently inverted to ensure homogenization. Specimens with visible or microscopic clots were excluded. Measurements were performed within 2 hours after sampling with the Sysmex XN‐V and Sysmex XT‐2000iV (Sysmex Corporation), according to the manufacturer's instructions, using settings for “quality control material” or “dog” .2.2The following variables were analyzed with both analyzers: impedance and optical RBC counts , hematocrit (HCT), hemoglobin concentration (HGB), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), red cell distribution width standard deviation and coefficient of variation , impedance, optical and fluorescence platelet counts , mean platelet volume (MPV), plateletcrit (PCT), platelet distribution width (PDW), platelet large cell ratio (P‐LCR), immature platelet fraction (IPF), reticulocyte count and percentage (RET), and low‐, medium‐, and high‐reticulocyte fluorescence ratio , immature reticulocyte fraction (IRF), reticulocyte hemoglobin equivalent (RET‐He), nucleated red blood cell count (NRBC), mature RBC hemoglobin equivalent (RBC‐He), WBC, counted on the white cell nucleated (WNR) or WBC differential (WDF) channel, and WBC differential count (DIFF). Manual 200‐cell WBC DIFF and NRBC counts (expressed as the number of NRBCs per 100 leukocytes) were performed by the first author  and packed cell volumes (PCVs) were measured by the centrifugation of blood in microhematocrit tubes .2.3Repeatability was evaluated from 20 consecutive repeats of a randomly selected canine specimen analyzed within 2 hours of sampling. Reproducibility was evaluated by duplicate morning and afternoon measurements of the manufacturer's quality control material (QC1 & QC2) for five consecutive days.Linearity was only tested on analytes expressed as concentrations or counts and not for those expressed as indices or percentages. Linearity was evaluated by analyzing four repeats of a 5‐point  serial dilution of a fresh canine EDTA blood specimen with the Sysmex diluent solution, DCL . Results of diluted specimens below the lower limit of measurement were considered as 0.The measurements by the two analyzers were compared by paired analyses of fresh EDTA blood specimens less than 2 hours after blood collection, with a delay of less than 30 minutes between measurements. Blood specimens with platelet clumps visualized on the blood smear or flagged by the analyzer were excluded. Basophils were not included in the comparison because the basophil count has been reported to be unreliable.Each result was classified according to canine RIs previously established for the Sysmex XT‐2000iV,Differences between the XN‐V and XT‐2000iV scattergrams are presented in Figure The effects of hemolysis, hyperbilirubinemia, and hyperlipemia were assessed using previously published protocols.Carry‐over was evaluated using a series of five triplicate analyses with high‐level and then low‐level quality control materials.The canine hematologic RIs previously established for the Sysmex2.4obs = Bias + 2∗CV. When TEobs was higher than the allowable total error (TEa), the quality goal index (QGI) was calculated as recommended by the ASVCP; QGI = Bias/(1.5∗CV). Linearity was tested visually and with linear regression analysis. A comparison of results was based on Spearman's correlation, Passing‐Bablok agreement analysis, and difference diagrams of the results obtained by the XN‐V and the XT‐2000iV, or by the XN‐V and the manual methods for WBC differential count, HCT, and NRBC count.P < 0.05.Repeatability and reproducibility were evaluated as the coefficient of variation, CV = (SD/Mean)∗100. Bias was calculated from manufacturer's data as Bias = ∗100. Observed total error was then calculated as TE3Given the numerous variables, we have chosen to report the full results for RBC, WBC, PLT, and RET in tables and figures within the manuscript, while detailed results for the other variables are given in the supplementary material.3.1obs was <20% for most measured variables, excluding lymphocyte, monocyte, and eosinophil counts, and higher for the PLT and RET indices. TEobs was lower than TEa for all variables except lymphocyte and monocyte counts. The latter QGI were <0.8 and >1.2, respectively  and RET indices . Passing‐Bablok regression equation slopes and intercepts were close to 1 and 0, respectively, for most variables. Results for the comparison between the Sysmex XN‐V analyzer and manual 200‐cell differential leukocyte counts, NRBC counts, and PCVs measured by centrifugation are shown in Table r = 0.96). The correlation between the Sysmex XN‐V and manual differential counts was good for neutrophils and eosinophils , acceptable for lymphocytes (r = 0.76), and fair for monocytes and NRBC  , polycythemia 3), leukocytosis (3), leukopenia (3), thrombocytopenia (7), thrombocytosis (7), leukemia (2), macrocytosis (6), or microcytosis (4), reticulocytosis (7), the presence of NRBC (11), suspected immune‐mediated hemolytic anemia based on regenerative anemia and spherocytosis (1 dog), hemoparasites , and leukocyte abnormalities, such as a left shift (9), toxic change (2), reactive lymphocytes (14), activated monocytes (9), basophilia (6), or mastocytemia (2). To compare the HCT results between analyzers, one specimen was excluded because the manual PCV was unavailable. One specimen was excluded from the comparison between the Sysmex XN‐V and the manual differential leukocyte count because the slide was damaged. A comparison between the two analyzers is reported in Table , leukocyFor 14/64 cases, flags were observed on the result sheets as star symbols next to the numerical result and/or text messages indicating an abnormal WBC, RET, RBC, and/or PLT scattergram or distribution. Platelet, WBC, RBC, and RET flags were observed in 9, 7, 2, and 3/64 cases, respectively. In one case, several flags were observed.Major discrepancies between the Sysmex XN‐V and manual differential counts were observed in nine cases, while flags were raised in six of these. Scattergrams showed two main errors in seven cases mainly associated with a high percentage of band cells based on the manual differential counts, and neutrophils recognized by the XN‐V as neutrophils and lymphocytes, despite a well‐defined but extended population observed in the neutrophil position with no dot clusters in the normal lymphocyte positions Figure . In anot3.4Results for the effects of hemolysis, lipemia, and icterus on hematology variables are shown in Figure 3.5P < 0.05) were observed. They were ≤2.1% for low HCTs, ≤0.7% for high and low MCVs, ≤3.3% for high MCHC values, ≤0.6% for high RBC‐He values, and ≤1.4% for low RET‐He values.Carry‐over was not visually observed for any variable Figure . However3.6T0 except for RBC counts. The effect of storage temperature was significantly different for all variables, except RBC‐O, HGB, PLT‐O, and PLT‐F. Compared with T0, the maximum differences after 72 hours of storage were an increase in the HCT (+18%) and MCV (+25%), and a decrease in the MCHC (−15%) at 24℃. Hemoglobin remained stable for up to 72 hours under both storage conditions. A significant and time‐dependent decrease in PLT‐I, PLT‐O, and PLT‐F was observed with a maximum difference of −47% (at 4℃), −28% (at 4℃), and −41% (at 24℃), respectively, after 72 hours of storage. No significant difference between storage at 4℃ and 24℃ was observed for PLT‐O and PLT‐F.The changes in Sysmex XN‐V measurements after storage at 4℃ and 24℃ are presented in Tables 3.7For most variables except RDW‐SD, RDW‐CV, MPV, and HFR, the acceptance criterion of ≤2 for the 20 values fell inside the previously established RIs for the Sysmex XT‐2000iV when the confidence interval of the limits was taken into account.4According to the results obtained for the validation process based on the ASVCP and ICSH recommendations,obs with the TEa proposed by experts for some variables.obs values were lower than TEa values, except for lymphocytes at low concentrations and monocytes within RIs. This was due to lymphocyte count imprecision and monocyte bias, according to the QGI calculation.obs than the other hematology measurands in agreement with the recently proposed TEa.The ASVCP guidelines help clinical pathologists assess the quality of analyzers.Carry‐over and linearity were excellent for all variables except lymphocytes. This was expected since similar results were obtained with the Sysmex XT‐2000iV analyzer.Comparison of the canine blood hematology results obtained with the Sysmex XT‐2000iV and Sysmex XN‐V analyzers was good to excellent for all the measured variables. These results were expected as the analyses, except for PLT‐F, involved the same analytical methods. Whereas the agreement of results between the Sysmex XN‐V and manual methods was good for HCT, neutrophil, and eosinophil counts, it was only fair for lymphocyte, monocyte, and NRBC counts, which was likely a consequence of the above‐mentioned imprecision.A similar observation has already been reported for the Sysmex XT‐2000iV.Our study revealed significant effects of hemoglobinemia and lipemia on HGB and associated variables. Furthermore, lipemia and bilirubinemia also had a significant effect on PLT counts. ASVCP recommendationsT0. Such results are well‐known and secondary to red blood cell swelling, as previously reported in numerous studies.The mean RBC variable values after storage for up to 72 hours were significantly different from According to the IFCC, CLSI, and ASVCP guidelines, validation of a pre‐existing RI can be accepted if one of the following three procedures is employed: subjective assessment, validation using small numbers of reference individuals, or validation using large numbers of reference individuals.To conclude, the Sysmex XN‐V results can readily be trusted for canine blood hematology analyses, even if the lymphocyte and monocyte concentrations need to be confirmed with a blood smear examination. Lipemia, hemolysis, scattergrams, and flags or error messages should be checked before validating results. Finally, specimens should be analyzed as early as possible, a 24‐hour delay being acceptable if the specimen is stored at 4℃, and similar species‐specific studies are necessary for valid use of the XN analyzer in other species.The authors do not have any conflict of interest to disclose.The authors have indicated that they have no affiliations or financial involvement with any organization or entity with a financial interest in, or in financial competition with, the subject matter or materials discussed in this article.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Fig S4Click here for additional data file.Table S1‐4Click here for additional data file.Supplementary MaterialClick here for additional data file.


We used behavioral testing and morphological methods to detail the progression of basal ganglia neuron type‐specific pathology and the deficits stemming from them in male heterozygous Q175 mice, compared to age‐matched WT males. A rotarod deficit was not present in Q175 mice until 18 months, but increased open field turn rate (reflecting hyperkinesia) and open field anxiety were evident at 6 months. No loss of striatal neurons was seen out to 18 months, but ENK+ and DARPP32+ striatal perikarya were fewer by 6 months, due to diminished expression, with further decline by 18 months. No reduction in SP+ striatal perikarya or striatal interneurons was seen in Q175 mice at 18 months, but cholinergic interneurons showed dendrite attenuation by 6 months. Despite reduced ENK expression in indirect pathway striatal perikarya, ENK‐immunostained terminals in globus pallidus externus (GPe) were more abundant at 6 months and remained so out to 18 months. Similarly, SP‐immunostained terminals from striatal direct pathway neurons were more abundant in globus pallidus internus and substantia nigra at 6 months and remained so at 18 months. FoxP2+ arkypallidal GPe neurons and subthalamic nucleus neurons were lost by 18 months but not prototypical PARV+ GPe neurons or dopaminergic nigral neurons. Our results show that striatal projection neuron abnormalities and behavioral abnormalities reflecting them develop between 2 and 6 months of age in Q175 male heterozygotes, indicating early effects of the HD mutation. The striatal pathologies resemble those in human HD, but are less severe at 18 months than even in premanifest HD. Striatal cholinergic (ChAT+) interneurons in WT and heterozygous Q175 mice at 18 months of age. The images show that striatal cholinergic interneuron abundance is comparable, but dendrites in Q175 mice are significantly fewer and shorter. These abnormalities and diverse striatal projection neuron abnormalities develop between 2 and 6 months of age in Q175 heterozygotes, indicating early effects of the HD mutation on the striatum in these mice that in many regards resemble those in human HD. Nonetheless, the pathologies do not yet involve overt striatal neuron loss even by 18 months, indicating they are less severe than even in premanifest HD. This line, nonetheless, is still referred to as the Q175. Although some data are available on the progression of striatal and subthalamic nucleus pathology in the Q175 mouse , 6 months of age , 12 months of age , and 18 months of age . The CAG repeat length at the mutant huntingtin site was assessed and found to be generally around 190 Table . The gen2.12.1.1Rotarod analysis was carried out using a San Diego Instruments rodent rotarod , as described previously  the neurochemical integrity of iSPN projection systems to GPe and the dSPN projections to GPi and SN; (b) the overall striatal neuron abundance; (c) the overall relative SPN abundance; (d) the abundance and morphology of neurochemically defined types of striatal interneurons; (e) the abundance of neurochemically defined types of neurons in GPe, GPi, SNr, and substantia nigra pars compacta (SNc); (f) the abundance of neurons in the subthalamic nucleus (STN); (g) the expression of GABAA‐α1 by GPe, GPi and SNr neurons; and (h) the size and abundance of mutant protein neuronal intranuclear inclusion (NII) in cortex and striatum. Further details on the different lines of studies to pursue these goals follow. Note that all measurements presented below were done blinded as to genotype, and all Q175 images shown were image processed for brightness, contrast, and sharpness with Photoshop or Word in the same manner as the corresponding WT images.2.2.1Immunohistochemical analysis was carried out on fixed tissue to specifically determine: (a) the integrity of the enkephalinergic (ENK+) iSPN projections to GPe and the substance P‐containing (SP+) dSPN projections to GPi and SN; (b) the overall striatal neuron abundance based on stereological analysis of cresyl violet‐stained and NeuN‐immunostained material; (c) the overall striatal projection neuron (SPN) abundance and the integrity of striatal projection systems as determined using DARPP32 immunolabeling; (d) the abundance of the tonically active cholinergic striatal interneurons, of the fast spiking parvalbuminergic (PARV+) striatal interneurons, and of the low threshold spiking (LTS) neuronal nitric oxide synthase‐containing (nNOS+) striatal interneurons as determined by stereological analysis; (e) the dendritic branching of cholinergic striatal interneurons by Sholl analysis of immunostained material; (f) the abundance of PARV+ pallidal type GABAergic projections neurons in GPe , GPi, and SNr as determined by counts in PARV immunolabeled material; (g) the abundance of FoxP2+ arkypallidal neurons in GPe; (h) the abundance of dopaminergic neurons in the substantia nigra pars compacta (SNc) as determined from tyrosine hydroxylase immunostained material; (i) the abundance of neurons in the subthalamic nucleus (STN) as determined by stereological analysis of cresyl violet stained tissue; (j) the expression of GABAA‐α1 by GPe, GPi and SNr neurons as determined from immunostained material; and (k) the abundance and size of neuronal intranuclear inclusion (NII) in cortex and striatum as determined from sections immunostained for ubiquitin or with EM48. For these studies, Q175 and WT mice were deeply anesthetized , and perfused transcardially with 40 ml of 0.9% NaCl in 0.1 M sodium phosphate buffer at pH 7.4 (PB), followed by 200 ml of 4% paraformaldehyde, 0.1 M lysine‐0.1 M sodium periodate in 0.1 M sodium phosphate buffer (pH 7.4). The brains were removed and stored in a 20% sucrose/10% glycerol solution at 4°C until sectioned frozen on a sliding microtome in the transverse  plane at 35 μm. Each brain was collected as 12 separate series in 0.1 M PB 0.02% sodium azide, and stored until processed for immunohistochemistry. A one in six series from each mouse was mounted as sectioned, and stained for cresyl violet.2O2 solution in 0.1 M PB for 30 min, as previously  procedure was used to visualize the above‐noted markers in Q175 and WT brains  immunolabeling was used to detect iSPN terminals in GPe, using a rabbit polyclonal antibody  directed against leucine‐enkephalin , whose specificity has been shown previously Reiner, . Substan2.2.2The GPe has been shown to contain a diversity of neuron types in rodents and primates, with the two main types being called prototypical neurons and arkypallidal neurons. Prototypical neurons are more laterally situated in GPe, are GABAergic, have high, regular basal firing rates, express PARV and Nkx2.1, represent the majority of GPe neurons (about 55%), and account for the canonical inhibitory projection of GPe to STN  and the rabbit anti‐FoxP2. The mouse monoclonal anti‐NeuN  was generated using the first 97aa of N‐terminal murine NeuN and its specificity has been documented by us previously  in Q175 and WT mice. A one‐in‐twelve series of coronal sections immunolabeled for NeuN, DARPP32, PARV, ChAT or nNOS, or stained for cresyl violet was used for type‐specific striatal neuron counts in each mouse, with only one side of the brain analyzed. Typically, this meant that about 10 evenly spaced sections (420 μm apart) were analyzed that spanned the length of the striatum for each striatal marker for each case. In the case of STN, typically six sections were analyzed per animal, from a 1 in 6 series. All counts were performed blinded as to genotype. Neuron counts and volume determinations were obtained using the optical fractionator method with a MicroBrightField Stereo Investigator system , as in our prior studies  on the projection neurons of GPe, GPi, and/or SNr, which use GABA as a neurotransmitter, and all of which (GPi and SNr) or the majority of which (GPe) contain PARV .We previously used Sholl analysis to show that the dendrites of striatal cholinergic interneurons were significantly fewer and shorter in heterozygous Q140 males at 1 and 4 months of age, although there was no reduction in striatal cholinergic interneuron abundance itself , with measurements conducted separately on layers II/III and V‐VI. For NII spatial abundance and size in striatum, measurements were made for central striatum. To determine NII diameter from the images of anti‐ubiquitin immunolabeled sections, NIH ImageJ was used to threshold NIIs. Artifacts in the images were removed prior to thresholding, and the size of the thresholded NIIs determined using the Particle Measurement function. The same thresholding method was also used to determine the spatial abundance of NIIs . The anti‐ubiquitin was used in the analysis, since it yielded more prominent immunolabeling than EM48, but colocalizes with EM48 and thus provides suitable information on mutant huntingtin aggregates , and determining volume from these areal measurements, section thickness (20 μm), section abundance, and section spacing. We counted neurons in a single standard field in central striatum at a level just anterior to the anterior commissure. We also used ImageJ to measure the optical density of the labeling at this level. For these intensity measurements, striatal background was set to zero by subtracting background intensity, the striatum was circumscribed to select it, and the optical density of the entire striatum measured. Additionally, labeled neurons in the entire striatum were selected by thresholding using the default automatic threshold function, and the specific labeling intensity determined. These assessments were performed on digital images captured using a high‐resolution Aperio Scanscope XT Scanner. All assessments were done in a blinded fashion as to genotype, although using autothreshold controlled for bias as well.2.3.7We also evaluated the spatial abundance and labeling intensity of neurons in GPe, GPi, and SNr containing PARV and GABAA‐α1 as detected by ISHH. This provided an opportunity to evaluate changes at the transcript level that might not be reflected at the level of protein as detected by IHC. For these studies, cryostat sections were used as in the case of ENK‐ISHH and SP‐ISHH, and sections through the striatal target areas from the same cases were used. For intensity measurements, background was set to zero, the region of interest  was circumscribed to select it, and the optical density of the entire region measured. Then, labeled neurons were selected by thresholding using the default automatic threshold function, and the specific labeling intensity of the neurons determined. A count of labeled neurons per unit area was also obtained. Matched levels through the midpoint of GPe, GPi, or SN were used for analysis across cases. The analysis was conducted blinded as to genotype.2.3.8t‐tests. For some endpoints, as noted on a case by case basis in the Results section, ANOVA was used to compare WT and mutant across age. The relationship of various neurochemical parameters to behavioral outcomes, and of mutant protein aggregate abundance to behavioral and neurochemical outcomes was assessed using regression analysis.In general, and unless stated otherwise, comparisons were made between WT and mutant at each age by unpaired, two‐tailed 33.13.1.1The WT versus Q175 body weight difference was significant at 12 and 18 months Figure , but not3.1.2p = .0447). As we have observed that rotarod performance in normal mice is inversely linked to body weight, we believe it likely that the better performance by mutant mice at 12 months of age is related to their considerably lesser body weight than shams. In contrast, in 18‐month‐old Q175 mice, we observed poorer rotarod performance (13.7% less) than in WT mice. For the average of the three test session trials, this difference trended toward significance (p = .062), and a consistent reduction in the mutants across all three test trials was significant by one‐way ANOVA (p = .0059). The emergence of a rotarod deficit by 18 months of age despite the yet lesser weight of the mutant mice than at 12 months is likely to be a consequence of the greater neural decline, as detailed below, evident by 18 months of age.Rotarod performance was not significantly different between the WT and Q175 mice at 2 or 6 months of age Figure . Somewha3.1.3p = .00681), but a reduction in distance traveled was not seen at either 2 months or, somewhat surprisingly, the later ages  that was not evident at 2 months (p = .42666) or 12 months (p = .97959) in Q175 mice, but was significant in 18‐month‐old Q175 mice , but none at 2 months (p = .54671), and the increase in turn rate remained significantly greater in the mutant mice than in WT at 12 months  and 18 months . We interpret the increased turn rate as a hyperkinetic behavior, suggesting Q175 mice show early onset and persistence to at least 18 months of hyperkinesia  that was also seen at 12 months , and 18 months , but not at 2 months , 9.6% less at 12 months (p = .00489), and 8.7% less at 18 months . The reduction in signal intensity for ENK neurons tended to be greater in medial than lateral striatum. Additionally, neuron counts in the ISHH material revealed significantly fewer ENK neurons in Q175 mice than WT mice at all three older ages, 15.3% fewer at 6 months (p = .03584), 15.8% fewer at 12 months (p = .00701), and 25.5% fewer at 18 months (p = .000017), but not at 2 months (p = .94546). As discussed in more detail below in the section on overall striatal neuron counts, despite the reduction in ENK+ iSPNs, stereological pan‐striatal neuron counts showed no overall striatal neuron loss per se in Q175 mice. Thus, the reduction in ENK+ iSPNs appears to reflect ENK message loss, rendering some ENK neurons undetectable, rather than ENK neuron loss.The ISHH revealed a significant, substantial, and early‐onset reduction in message for individual ENK neurons in Q175 mice—30.3% less than WT at 6 months (3.2.2p = .00054), 12 months of age , and 18 months of age .We performed blinded densitometric analysis of ENK immunolabeling in GPe. Given the reduced ENK expression in iSPNs, it was a surprise to see a significant increase in ENK striato‐GPe terminals in Q175 mice in our immunohistochemical analyses at 6 months of age  and the length of progression segments in open field (r = .356), meaning fewer ENK perikarya led to less distance traveled in open field and shorter units of progression. Moreover, the abundance of ENK‐ISHH perikarya counted in striatum across the WT and Q175 cases at 6, 12, and 18 months of age was significantly inversely correlated with turn rate (r = −.392), meaning the reduction in ENK neurons in Q175 mice was associated with increased turning. At 6 months of age, when speed was seen to be reduced in the mutant mice, the abundance of ENK‐ISHH perikarya was highly and significantly correlated with maximum speed (r = .796) in open field, suggesting the lessening of ENK‐labeled neurons in Q175 mice also led to slowing. The linking of ENK neuron reduction to increased turning, a hyperkinetic behavior, is of interest, as hyperkinesia is the outcome predicted for striatal ENK neuron loss/hypofunction in the indirect–direct pathway model of basal ganglia function , 98.2% at 6 months (p = .30828), 92.4% at 12 months (p = .20121), and 114.2% at 18 months , 6 months , 12 months , or 18 months  and 114.0% of WT for striato‐SN (p = .00141) at 6 months, 115.1% of WT for striato‐GPi (p = .00106) and 119.9% of WT for striato‐SN (p = .00127) at 12 months, and 123.8% of WT for striato‐GPi (p = .00023) and 109.4% of WT for striato‐SN (p = .02517) at 18 months . Thus, the overall results show the importance of SP+ dSPNs for maintaining progression in open field, as fewer SP+ dSPNs led to a diminished ability to progress . Regression analysis at 18 months, showed that striato‐GPi SP levels were inversely correlated with rotarod performance (−0.455). Thus, the higher striato‐GPi SP fiber abundance in the mutants was significantly associated with poorer rotarod performance, suggesting that a dysfunctional striato‐GPi dSPNs, as reflected in abnormally elevated SP+ fiber abundance in GPi, may have contributed to impaired motor performance.Despite the absence of SP+ neuron reduction in the Q175 mice, regression analysis showed that the abundance of SP+ striatal neurons across WT and mutant mice at 6 months was significantly inversely correlated with stops per unit distance (3.2.7p = .01950) at 6 months, a significant 20.6% reduction (p = .02939) at 12 months, and a significant 43.3% reduction (p = .00002) at 18 months. No significant reduction was seen at 2 months of age (p = .37718). The reductions at 6, 12, and 18 months appears to reflect loss of DARPP32 expression rather than SPN loss, since (as discussed in more detail below) stereological counts of cresyl violet‐stained striatal neurons and NeuN‐immunolabeled neurons showed there was no significant overall neuron loss in striatum per se.DARPP32 is found in both iSPNs and dSPNs , but in mice, we have found it is only detectible by immunolabeling in about 35% of SPNs, apparently due to a limitation in primary antibody penetration  and maximum speed in open field (r = .266), meaning fewer DARPP32+ perikarya led to less distance traveled in open field and slower progression. Moreover, the abundance of DARPP32+ perikarya in striatum across the WT and Q175 cases at 6, 12, and 18 months of age was significantly inversely correlated with turn rate (r = −.354), meaning the reduction in DARPP32+ neurons in Q175 mice was associated with increased turning, the hyperkinetic behavior also associated with reduced ENK striatal neurons. At 18 months, both the loss of striatal DARPP32+ neurons and the loss of DARPP32+ fibers in GPe were significantly inversely correlated with the large increase in turn rate . This increase in turning with fewer DARPP32 neurons and fewer DARPP32 striato‐GPe fibers reflects a hyperkinesia that would be expected from iSPN dysfunction or loss, based on the standard direct–indirect pathway model of basal ganglia function  and Q175 mice (p = .000419). Post hoc comparisons using the Bonferroni correction for multiple comparisons confirmed that the declines from 6 to 18 months were significant for both WT (p = .039026) and Q175 mice (p = .000337). Notably, the comparisons further showed that the cresyl violet stained striatal neuron abundance at 18 months was not significantly less than at 12 months for WT mice (p = .285896), but was for Q175 mice (p = .016428). Thus, there may be an age‐related progressive striatal neuron decline from 6 to 12 to 18 months that is slightly compounded by the mutant genotype from 12 to 18 months. Similar decline from 6 to 18 months was also seen in the counts of NeuN‐immunostained striatal neurons, but in this case, the decline did not differ significantly between WT and Q175 mice.Our stereological analysis did not detect a difference in cresyl violet‐stained neuron abundance between WT and mutant at 6, 12, or 18 months of age Figure . Our ste3.2.11p = .03696). Averaging these values together yielded a 2.5% lessening in mutants that was not significant. In the case of cresyl violet‐stained or immunolabeled tissue from 12‐month‐old mice, Q175 striatal volume ranged from about 3–7% less, and was significantly less in the case of the DARPP32 immunolabeled tissue (6.2% less) and ChAT immunolabeled tissue (5.0% less), and for all labeling methods averaged together . At 18 months, we also observed a consistently lesser striatal volume in the Q175 mice, which ranged, depending on the stain, from 2.5% less (DARPP32 immunolabeling) to 9.7% less (ChAT immunolabeling). Averaging all estimates of striatal volume obtained from stereological analysis of the fixed tissue  at 18 months, we observed a 4.7% lesser volume in Q175 mice, which was not significantly different between WT and Q175 (p = .10937). Pooling rather than averaging all WT volume measures and comparing to all pooled Q175 volume measures, however, yielded a significantly lower striatal volume for Q175 mice . Given the consistency across markers and the pooled values, these results suggest that striatal volume appears about 5% less in the fixed Q175 tissue at 12 and 18 months of age, but is not substantially different at the earlier ages.In our stereological studies of 2‐month‐old mice, no significant differences were seen between WT and Q175 mice in striatal volume for fixed tissue . Similar results were observed for the fresh frozen tissue at 12 months (11.2% less in Q175) and 18 months (14.4% less in Q175), with the difference between WT and Q175 significant in both cases . No differences between WT and Q175 striatal volume were seen for the fresh frozen tissue at 2 months of age  and 12 months of age , but not at 2 months of age . Thus, despite the preservation in numbers, defects in the functioning of cholinergic interneurons are likely to be present in 6‐ and 12‐month‐old Q175 mice. Examination of striatal cholinergic interneurons in 18‐month‐old WT and Q175 mice suggests a similar dendritic reduction in mutant mice at this age.We evaluated the abundance of the three major types of striatal interneurons in Q175 mice—the cholinergic tonically active neurons (TANs) immunolabeled for ChAT, the fast‐spiking interneurons (FSI) immunolabeled for PARV, and the low‐threshold spiking (LTS) interneurons immunolabeled for nNOS. We found no difference between mutant and WT mice at 6, 12, or 18 months in the abundance of cholinergic or PARV interneurons Figure . Because3.33.3.1p = .00020) and 18 months (p = .00252) but not at 2 and 6 months. This increased volume is likely to be associated with the increased iSPN fiber abundance in GPe.No abnormalities were evident in the Q175 mutants in the total abundance of PARV+ prototypical neuron in GPe at 6, 12, or 18 months, as determined from the immunolabeled tissue Figure . ISHH al3.3.2p = .41723) in Q175 mice  in the spatial density of FoxP2+ arkypallidal neurons in GPe at 18 months of age in the Q175 mice  in Q175 mice (33.4% less) at 18 months than in WT mice of the same age. Thus, arkypallidal GPe neurons appear to sustain significant 20–30% loss by 18 months of age.Because several studies have shown that the arkypallidal neurons of GPe can be identified by their expression of FoxP2 , and a trend toward an increase at 12 months (p = .06900). The increase seen with immunolabeling was no longer evident at 18 months, although a slight increase in the total abundance of GABAA‐α1 neurons in mutant mice that trended toward statistical significance (p = .05318) was seen at 18 months. Thus, the GABAA‐α1 data showed only a weak tendency to be increased in the mutant mice as they aged. Nonetheless, a correlation with rotarod performance was seen—rotarod performance was worse with more GABAA‐α1 neurons in GPe at 18 months, with a significant −0.685 correlation between GABAA‐α1 neuron abundance in GPe and rotarod latency to fall. This suggests a possible role of GPe dysfunction as reflected in an increased GPe expression of GABAA‐α1 in motor impairment at this age.Because loss or hypoactivity of the striatal input to its target areas is associated with an upregulation of GABAA receptors, especially the α1 subunit, we assessed localization of this receptor subunit in GPe by immunolabeling and by ISHH. As shown in Figure 3.3.4No significant neuronal pathology was evident in GPi as assessed using PARV ISHH or PARV immunolabeling at 6, 12, and 18 months of age. Neuronal abundance for the entire GPi as assessed by immunolabeling or the spatial density of PARV neurons in GPi as assessed by ISHH did not differ significantly between mutants and WT mice, nor did PARV labeling intensity , suggesting hypofunction in the striato‐GPi circuit by this age. Similarly, immunolabeling also revealed a highly significant increase in the GABAA‐α1 signal in GPi neurons at 18 months (p = .00057), consistent with our SP immunolabeling data showing an abnormality in SP+ terminals in GPi at 18 months . The GABAA‐α1 data showed correlations with the behavioral data for these mice, further reinforcing the possible role of the circuit‐level abnormalities revealed by the GABAA‐α1 localization in GPi in motor dysfunction. For example, the elevated GABAA‐α1 signal intensity in the GPi of 18‐month‐old Q175 mice was significantly inversely associated with turn radius (−0.779), meaning the turning ability diminished as GABAA‐α1 signal on GPi neurons increased. Thus, the motor impairment reflected in the narrower turning angles was associated with the striato‐GPi hypofunction implied by the elevated GABAA‐α1 signal on GPi neurons.Based on analysis of the ISHH tissue labeled for GABAA‐α1 and the tissue immunolabeled for GABAA‐α1, there were no significant differences between mutant and WT in the intensity of GABAA‐α1 signal for GPi neurons at 2 or 6 months of age, although we did see a trend toward an increased spatial density of GPi neurons labeled by ISHH for GABAA‐α1 at 6 months . Thus, the generally lower spatial density of PARV+ SNr neurons in mutants may reflect the generally greater SNr volume in the mutants, rather than PARV+ neuron loss, particularly at 12 months.No significant change in overall PARV+ neuron abundance was detected at 6 months of age Figure  in count3.3.7p = .00882) but significantly greater GABAA‐α1 neuron spatial density (p = .01925) in Q175 SNr than WT SNr at 2 months of age. No difference was seen, however, between WT and Q175 mice in GABAA‐α1 neuron labeling intensity or spatial density by ISHH at 2 months of age . Immunolabeling detected no differences between mutant and WT mice in intensity or abundance of GABAA‐α1 SNr neurons at 12 or 18 months, although like with ISHH, Q175 GABAA‐α1 SNr neurons were significantly lower in spatial density than WT at 6 months of age (p = .02277). The lower spatial density of GABAA‐α1 SNr neurons in mutants also may reflect the generally greater SNr volume in mutants.Immunolabeling detected significantly lower GABAA‐α1 neuron labeling intensity , consistent with the findings of Atherton et al. , as did the percent of the cortex covered by aggregates (−0.722). The abundance of DARPP32+ striatal neurons showed a more complicated relationship to aggregate load. Among 6‐month‐old Q175 mice, the aggregate abundance was associated with a harmful effect, as the percent of the striatum covered by aggregates tended to inversely correlate with the abundance of DARPP32+ striatal neurons (−0.595). In 18‐month‐old Q175 mice, however, DARPP32+ neuron abundance was highly correlated with striatal aggregate load (r = .616), suggesting that mutant protein aggregation protected this neurochemical feature of SPNs at this age. It may be that mutant protein aggregates play a biphasic role, being a corollary of toxic mutant protein production early in disease process, and thus reflecting disease severity at early stages, but serving to sequester and mitigate mutant protein toxicity later in disease, and thus be inversely correlated with disease endpoints reflecting neuronal pathology at these later stages.Among 6‐month‐old Q175 mice, the percent of the striatum covered by aggregates tended to inversely correlate with the abundance of ENK‐immunostained GPe fibers (−0.481) and with the abundance of SP‐immunostained SN fibers (−0.540). Although this was short of significance for an 3.3.12r = .524), suggesting that increased aggregate load improved this sign of motor endurance in open field. Note that, overall, 6‐month Q175 mice showed a trend toward a deficit compared to WT for the length of progression segments in open field. Thus, although the mutation seemingly adversely affected this motor parameter, among Q175 mice a larger striatal aggregate load at this age was associated with a mitigation of this hypokinetic sign. At 12 months, regression analysis for the mutant mice showed that rotarod performance was negatively correlated with the aggregate burden (% coverage) for striatum (r = −.508), indicating that higher aggregate load in striatum was associated with poorer rotarod performance. Note that at 12 months of age the mutant mice actually performed better than the WT on our accelerating rotarod task. At 18 months, progression segment length in open field was inversely correlated for Q175 mice with aggregate burden in Layer 5 of cerebral cortex (r = −.580), meaning greater aggregate abundance in presumptive corticostriatal neurons was associated with poorer motor performance, as reflected in shorter progression segment length. Moreover, at 18 months the increase in turn rate seen in Q175 mice (a possible sign of hyperkinesia) was highly correlated for Q175 mice with aggregate burden in Layers 5–6 of cerebral cortex (r = .637). Thus, disease magnitude (as reflected in aggregate load) in corticostriatal neurons was positively associated with this putative hyperkinetic behavior. Nonetheless, cortical aggregate abundance for Layers 2/3 and 5/6 combined was associated with a protective effect for rotarod at 18 months, since regression analysis for mutant mice showed that rotarod test performance was significantly positively correlated with the aggregate burden (% coverage) for cortical Layers 2, 3, 5, and 6 (r = .708). Our results thus suggest a complex role of mutant protein aggregates in pathogenesis and pathophysiology.Regression analysis for mutant mice by themselves also showed that some aspects of motor performance were improved by a higher aggregate burden. For example, the percent of the striatum covered by aggregates at 6 months of age was positively associated with the length of progression segments in open field  and open field anxiety were a constant feature in Q175 from 6 months to 18 months. Somewhat oddly, Q175 mice performed better than WT mice on rotarod at 12 months of age. This may reflect the much lighter body weight of the Q175 mice, since we have found in our rotarod testing of mice that all other things being equal (or nearly so), lighter mice stay on the accelerating rotarod longer. For striatal perikarya, there was no overall significant loss of striatal neurons out to 18 months, but there was a deficiency in detectible ENK+ and DARPP32+ striatal perikarya (due apparently to reduced expression) already at 6 months that is more severe by 18 months. In contrast, there was no significant change in the abundance of detectible SP+ striatal perikarya over the 2‐ to 18‐month period. Moreover, striatal cholinergic, PARV, and NOS+ interneurons did not change in their abundance either, although cholinergic neurons showed significant dendrite attenuation in Q175 mice by 6 months of age. Despite the reduced ENK expression in striatal perikarya of the indirect pathway, the abundance of ENK‐immunostained terminals in GPe was significantly elevated at 6 months of age, and remained so out to 18 months of age. Similarly, the abundance of SP‐immunostained terminals from SP+ striatal direct pathway neurons in GPi and SN was elevated at 6 months of age, and also remained so out to 18 months. Although we saw no loss of prototypical PARV+ GPe pallidal neurons out to 18 months of age, there was a 20–30% loss of arkypallidal neurons as detected by FoxP2 immunolabeling at 18 months of age, and a 6.6% reduction at 12 months that trended toward significance. The presence of behavioral and SPN abnormalities as early as 6 months of age in Q175 male heterozygotes, which persist or grow worse, but not in 2‐month‐old Q175 male heterozygous mice, indicates that the mutation drives pathological progression after 2 months of age. These various findings are discussed below in more detail in relationship to findings in human HD and in prior mouse models of HD, notably prior studies on Q175 mice.4.1Neuropathological and imaging studies show that brain abnormalities in HD develop before evident symptoms, are progressive, and eventually involve the entire brain, resulting in about 25% brain weight loss in advanced HD  tonically active aspiny cholinergic neurons  prototypical GABAergic pallidal neurons that are more laterally situated, express PARV, make up about 55% of all GPe neurons, and project to STN and parafascicular nucleus of the thalamus; and (b) arkypallidal GABAergic neurons that are more medially situated, express FoxP2 but not PARV, make up about 25% of all GPe neurons, and project back to SPNs of dorsolateral motor striatum , and so their loss at 18 months in Q175 mice might contribute to the increased hyperkinesia seen at this age. In HD, significant progressive atrophy occurs in GPe and GPi, with greater atrophy and gliosis in GPe than GPi  was highly correlated for Q175 mice with aggregate burden in Layers 5–6 of cerebral cortex, meaning increased deep cortical aggregate burden led to more hyperkinesia. Thus, disease magnitude (as reflected in aggregate load) in corticostriatal neurons was positively associated with behavioral abnormalities. Nonetheless, cortical aggregate abundance at 18 months was associated with a protective effect for rotarod, since regression analysis for mutant mice showed that rotarod performance was significantly positively correlated with the aggregate burden (% coverage) for cortical layers 2, 3, 5, and 6. Our results thus reflect a complex role of mutant protein aggregates in pathogenesis and pathophysiology, generally suggesting that they protect against pathogenesis but cause neuronal dysfunction that hinders behavior.None of the authors has any interest or relationship that might be perceived as influencing their objectivity.Yunping Deng conducted the analysis in the immunolabeling studies. Hongbing Wang conducted the analysis in the in situ studies. Marion Joni performed some of the analysis in the immunolabeling studies. Radhika Sekhri performed some of the analysis in the immunolabeling studies. Anton Reiner planned and supervised the studies, participated in analysis, and wrote the manuscript, and made its illustrations.All work was conducted in a manner consistent with Journal guidelines regarding ethical and responsible research conduct.https://publons.com/publon/10.1002/cne.25023.The peer review history for this article is available at


GSE100666 and GSE68417. DAVID evaluation of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses was used. We constructed a protein–protein interaction (PPI) network of DEGS using Cytoscape software and analyzed the submodules with the CytoHubba plugin. Finally, we performed western blot, immunohistochemistry, and PCR validation by collecting tissues, and also utilized cells for in vitro functional analysis of ceruloplasmin (CP). In total, 202 DEGs (52 upregulated and 150 downregulated genes) were identified. Upregulated DEGs are significantly rich in angiogenesis, cell adhesion, and response to hypoxia, whereas downregulated DEGs are involved in intracellular pH regulation, excretion, coagulation, and chloride transmembrane transport. We selected the interactions of the top 20 hub genes provided by the PPI network, all of which are involved in important physiological pathways in vivo, such as complement and coagulation cascades. Tissue protein assays demonstrated that renal cancer highly expressed CP, while in vitro experiments showed that CP could promote the invasion of renal cancer cells. Our study suggests that ALB, C3, LOX, HRG, CXCR4, GPC3, SLC12A3, CP, and CASR may be involved in the development of ccRCC, and is expected to provide theoretical support for future studies on the diagnosis and targeted therapy of ccRCC.Clear‐cell renal cell carcinoma (ccRCC) is the most prevalent renal malignancy. The pathogenesis of the disease is currently poorly understood, and the prognosis is poor. Therefore, in this study, we focused on exploring and identifying genes and signal transduction pathways that are closely related to ccRCC. Differentially expressed genes (DEGs) were analyzed using the renal cell oncogene expression profiles  in vitro experiments showed that CP could promote the invasion of renal cancer cells. Overall, CP may promote the poor prognosis of renal cancer.DEGs in clear cell renal cell carcinoma (ccRCC) were identified and their expression analyzed in ccRCC patient tissues and an RCC cell line. Tissue protein assays demonstrated that renal cancer highly expressed ceruloplasmin (CP), and  Of thesWith the development of gene sequencing technology, tumor markers have been widely used for clinical diagnosis and treatment of tumors, prognosis, and efficacy assessment . AnalysiIn this study, we selected differential genes by GEO2R and then constructed a protein–protein interaction (PPI) network based on DEGs to identify key genes and related signaling pathways that may be involved in RCC development. We found some significant genes, especially CP. CP is an α2‐glycoprotein that is mainly produced by hepatocytes. It is the main ferroxidase in the human body and is important to regulate both systemic and intracellular iron levels . It has GSE100666 and GSE68417 were downloaded from the GEO database, where GSE100666 consisted of three renal cell carcinoma samples and three normal tissue samples, and the raw data processed by Phalanx Human OneArray Ver. 6 Release 1. GSE68417 consist of 29 RCC samples and 20 non‐cancerous samples, raw data processed by Affymetrix Human Gene 1.0 ST Array. Transcriptional expression data from renal cancer patients were obtained in the TCGA database. Including: Case: Primary Site: kidney, Program: TCGA, Project: TCGA‐KIRC. Files: Data Category: transcriptome profiling, Data Type: Gene Expression Quantification, Experimental Strategy: RNA‐Seq, Workflow Type: HTSeq‐FPKM. Eventually, we screened a total of 611 sequencing files.The mRNA expression profiles of datasets http://www.ncbi.nlm.nih.gov/geo/geo2r), an analytical tool of the GEO database. Consistent with the criteria of previous investigators , we defined log2FC < −2 (downregulated genes) or log2FC > 2 (upregulated genes) as DEGs. In the TCGA database, we assessed whether the two groups were statistically different by Wilcoxon test using the gene expression after normalization of tumor tissue and normal tissue. P < 0.05 was considered statistically significantWe screened DEGs between RCC and noncancerous samples by GEO2R . https://string‐db.org/), network (PPI), and to construct predicted protein‐protein interactions (interaction score > 0.4), the constructed PPI network was displayed with Cytoscape software. In this study, the degree values are used to evaluate the nodes in the network. CytoHubba is a plugin for Cytoscape for predicting key nodes and subnets in a network. The top 20 ranked genes were selected and identified as the central genes for degree ranking.To assess the interaction of DEGs, we submitted DEGs to the interacting gene/protein (STRING) database , including mRNA, RNA‐seq, pan‐cancer, and kidney renal clear‐cell carcinoma. Renal cell carcinoma patients were divided into two groups (high and low expression) based on auto select best cutoff parameter expression levels of specific genes. OS was calculated for both groups and plotted, and hazard ratios (HR) and log‐rank P were calculated with 95% confidence intervals (CI).http://gepia.cancer‐pku.cn/index.html), the RNA‐sequencing expression data of tumor tissue samples, and normal tissue samples were obtained from the TCGA database and the GTEx database, respectively (10), and it can be directly used for tumor/normal tissue differential expression analysis. In this study, GEPIA was used to validate the expression differences of the hub genes. We defined this difference as statistically significant (P < 0.05).GEPIA  software was used to analyze the enrichment results. criteria .From August 2017 to December 2017, a total of four pairs of ccRCC tissues and adjacent normal tissues were obtained from the Second Affiliated Hospital of Nantong University. The experiments were performed with the understanding and written consent of each subject, and the methods of this study complied with the standards laid down in the Declaration of Helsinki. The study was approved by the Human Research Ethics Committee of the Second Affiliated Hospital of Nantong University, and written informed consent was obtained from the patients. Clear renal cell carcinoma cell line 786‐O was purchased from Shanghai Institute of Cellular Biochemistry, Chinese Academy of Sciences . The cells were cultured in 1640 medium with 10% fetal bovine serum at 37 °C, 5% carbon dioxide, harvested when the cells were in the logarithmic growth phase, and used for subsequent experiments.m. In this study, Lipofectamine® 2000  and siRNA were diluted with 250 μL of OPTI‐MEM, respectively, allowed to stand for 5 min, mixed well and allowed to stand for 30 min again, and finally added to a six‐well plate to incubate with cells for 6 h, changed to 1640 medium containing 10% fetal bovine serum for another 48 h before use in the experiment.SiRNA for CP (si‐CP) and negative control (si‐NC) were chemically synthesized by GenePharma . The working concentration of Si‐CP and Si‐NC was 40 n−ΔΔCt.In this experiment, total RNA was extracted from cells and tissues by using TRIzol reagent . RT‐PCR was performed on RNA samples using TaKaRa one‐step RT‐PCR kit . For real‐time PCR, cDNA was analyzed in triplicate using SYBR Green (Takara). Relative mRNA concentrations were determined from 2Paraffin‐embedded sections were deparaffinized and hydrated. Slides were placed in citrate buffer (pH 6.0) and heated for 3 min at high pressure. Incubate with 0.3% H2O2 solution at room temperature for 15 min, rinse with PBS for 3 × 3 min, then block with goat serum for 30 min, then incubate primary antibody anti‐CP  with sections overnight at 4 °C and wash with PBS for 3 × 3 min, then incubate secondary antibody  and sections at room temperature for 30 min, then incubate with peroxidase‐antiperoxidase complex at room temperature for 30 min, rinse with PBS for 3 × 3 min, then stain with DAB, finally counterstain nuclei with hematoxylin, mount with neutral gum and observe sections under microscope.m PMSF  for 30 min. The lysates were centrifuged at 12 000 g for 15 min at 4 °C, and the supernatants were collected. Protein concentration was determined using the Bradford protein assay kit . Approximately 40 μg of protein was loaded by SDS/PAGE gel electrophoresis, and then, the proteins were transferred to PVDF membranes. After blocking with 5% skimmed milk in TBST buffer for 2 h, PVDF membranes were incubated with anti‐CP  and GAPDH antibodies  overnight at 4 °C. After washing the PVDF membrane with PBST for 10 min*3, the secondary antibody conjugated with horseradish peroxidase  was incubated at room temperature for 1 h. After washing the PVDF membrane with PBST for 10 min*3, the bands were detected using a chemiluminescent substrate ECL kit .SI‐NC cells, SI‐CP cells, and kidney cancer patient tissue samples were collected and lysed with RIPA lysis buffer  on ice and added to a final concentration of 1 m6/mL was added to the upper chamber, and 500 μL of 20% fetal bovine serum containing medium was placed in the lower chamber and routinely cultured for 24 h before removing the cells on the upper side of the chamber with a cotton swab, and the cells under the filter membrane were fixed with 4% paraformaldehyde for 10 min and stained with crystal violet for 10 min. Three randomly selected areas under the microscope were used for counting the stained cells. The number of cells in each region was used as a statistical result.Diluted matrigel (diluted 1 : 6 with serum‐free medium) was added to the cell culture chamber, and after Matrigel solidification, 100 μL of SI‐CP and SI‐NC cells at a concentration of about 10Cells 48 h after siRNA transfection were cultured in 6‐well plates containing serum medium, and when the cell density reached 80%, the cells were washed three times with sterile PBS, and the bottom of the six‐well plate was scraped with a 200‐μL pipette tip, the debris was removed by PBS, and the cells were cultured in serum‐free medium for 12 h, cell migration was analyzed in three randomly different microscopic fields and the percentage of wound healing was calculated. Images were captured on a microscope at 0 and 12 h.graphpadprism5  and spss 22.0 software  were used for all statistical analyses. Quantitative data were expressed as mean ± SD. Significant differences in quantitative data were compared by unpaired t‐test. P‐values < 0.05 were considered statistically significant. Statistical significance was determined as #P < 0.05, ##P < 0.01, and ###P < 0.001.GSE100666 and GSE68417 datasets, respectively  enrichment analysis of upregulated and downregulated DEG using David tool. As far as biological processes (BP) are concerned, the increased DEGs is significantly enriched angiogenesis, cell adhesion, and response to hypoxia Fig. . Downreg−16. The network contains 52 upregulated genes and 150 downregulated genes , LOX , CXCR4 , and CP  usually have a lower overall survival rate. The survival rate of patients with a lower expression of ALB , HRG , GPC3 , SLC12A3 , and CASR  tend to be shorter compared with patients over‐expressing those genes  and overall survival (OS) than those with low Alb . It has in vitro . This fuin vitro . Similarin vitro , colon [in vitro , and panin vitro . LOX is response . Early sresponse . CP is aresponse . Howeverresponse . Also, iresponse . Thereforesponse , CP can response , plasma response , 37. ComIn this study, our study has some shortcomings. First, the clinical sample size was too small and only four eligible frozen samples were analyzed by real‐time quantitative RT‐PCR and western blots. Therefore, in order to understand our results more accurately, we intend to collect more satisfactory samples later. Second, only one type of renal cancer cell line was validated, and the reliability was relatively low. In summary, in this study, based on the DEGs between ccRCC and normal tissues, DEGs were first identified by bioinformatics analysis, and then, potential key genes involved in ccRCC development were analyzed, and finally, central genes and signaling pathways that may be involved in ccRCC progression and metastasis were revealed. These epigenetic changes may be associated with poor overall survival in ccRCC. However, the specific biological roles of these candidate biomarkers in ccRCC and the specific molecular mechanisms involved in ccRCC metastasis require further investigation.In summary, a total of 20 central genes were screened in this study; particularly, ALB, C3, LOX, HRG, CXCR4, GPC3, SLC12A3, CP, and CASR were closely related to the prognosis of RCC patients. In addition, high expression of CP was associated with lower overall survival in ccRCC patients. CP is expected to provide a theoretical basis for enriching ccRCC pathogenesis as well as new signaling pathways.The authors declare that they have no competing interests, and all authors should confirm its accuracy.BZ designed and directed the project. YZ, ZC, and JGC processed and analyzed the experimental data, and they had equal contributions to this research; YZ and XFC wrote and revised the manuscript. ZML conducted immunohistochemistry experiments. DHG conducted western blotting experiments. YDG and XFC conducted cell experiments. All authors have read and approved the manuscript and agreed to be responsible for all aspects of the research to ensure proper investigation and resolution of the accuracy or completeness of any part of the work.