piRNAs and circRNAs acting as diagnostic biomarkers in clear cell renal cell carcinoma

piRNAs and circRNAs acting as diagnostic biomarkers in clear cell renal cell carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article piRNAs and circRNAs acting as diagnostic biomarkers in clear cell renal cell carcinoma Yin Xu, Huiling Liu, Yingzhi Zhang, Jing Luo, Haomin Li, Caiyong Lai, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4681552/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Objective: The discovery of diverse functions and mechanisms in cancer has underscored the significance of emerging non-coding RNAs (ncRNAs), such as PIWI-interacting RNAs (piRNAs) and circular RNAs (circRNAs), within the clinical context of cancer. Understanding their role in clear cell renal cell carcinoma (ccRCC) is imperative and necessitates comprehensive investigation. This study aims to further explore the diagnostic potential of piRNAs and circRNAs for ccRCC. Methods: The dysregulated piRNAs and circRNAs in ccRCC were identified using small RNA (sRNA) high-throughput sequencing technology, while their expression in clinical samples was assessed by RT-qPCR. A paired t-test was performed to compare the expression levels of piRNAs and circRNAs between ccRCC and adjacent tissues. Additionally, ROC curve analysis was conducted to evaluate the diagnostic specificity, sensitivity, and area under the curve (AUC) of piRNAs and circRNAs. Results: High-throughput sequencing revealed a significant downregulation of 17 piRNAs and 694 circRNAs in ccRCC tissues, accompanied by a significant upregulation of 5 piRNAs and 490 circRNAs. RT-qPCR analysis demonstrated markedly lower expression levels of piR-has-150997, 133872, 132556, 154502, and uniq-84737 in the ccRCC group compared to the adjacent tissue group (p < 0.05). When considering the combined detection of piR-hsa-150997,piR-hsa-133872, piR-hsa-132556,piR-hsa-154502,uniq_84737,circABCC1,circNETO2_006,and circARID1B_037, the diagnostic AUC for ccRCC was found to be high at an approximate value of AUC=0.878. Conclusions: The diagnostic performance of piR-has-150997, 133872, 132556, 154502, uniq-84737, circABCC1, circNETO2_006, and circARID1B_037 demonstrates promise for ccRCC. A model incorporating piR-hsa-150997, uniq_84737, circABCC1, circNETO2_006, and circARID1B_037 could serve as an ideal diagnostic marker system with significant clinical utility. Biological sciences/Genetics/Cancer genetics Biological sciences/Genetics/Gene expression Clear cell renal cell carcinoma piRNAs circular RNAs high-throughput sequencing diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Despite being the most common genitourinary malignancy, renal cell carcinoma (RCC) often goes undetected until it reaches an advanced stage. Timely identification of RCC plays a pivotal role in managing patients effectively and reducing mortality rates; however, there remains a dearth of optimal screening techniques 2 . Currently, computed tomography (CT) serves as the primary modality for diagnosing RCC, while surgical intervention proves to be highly effective in treating localized forms of this disease 3 . Unfortunately, approximately 30% of patients are diagnosed with distant metastasis upon initial evaluation which not only hampers their chances for receiving timely treatment but also leads to an unfavorable prognosis 4 . As a result thereof these developments have played an instrumental role in identifying more efficient strategies pertaining to diagnosis and management by shedding light on its molecular characteristics alongside therapeutic interventions 2 . Material and method 1.Collection of paired clinical tissue samples The clinical tissues utilized in this study were obtained from patients who underwent surgical tumorectomies at the Department of Urology, First Affiliated Hospital of Jinan University, between 2014 and 2018. Upon collection, the tissues were immediately immersed in RNA-later for preservation and subsequently refrigerated at 4 o C overnight. Following this, the samples were transferred to liquid nitrogen for long-term storage. Histological and pathological diagnoses of the clinical samples were confirmed by experienced clinical pathologists as per the study protocol. Prior to sample collection, all patients provided informed consent for their tissue utilization in research, and the study protocol was approved by the Ethical Committee of the First Affiliated Hospital of Jinan University in accordance with the Declaration of Helsinki. 2.High-throughput Sequencing The sequencing data of piRNAs and circRNAs were obtained from the preliminary data of our research group. In order to comprehensively analyze and compare the datasets, we have re-extracted sequencing data with significant differences, which is crucial for understanding the implications of these variations 1 . 3.RT-PCR Total RNA was extracted using TRIzol (Life Technologies, USA) according to the manufacturer's instructions, as is customary. Subsequently, the miScript II RT Kit (Qiagen, Hilden, Germany) was employed for reverse transcription of RNA into cDNA utilizing random primers. RT-PCR reactions were conducted on a Bio-Rad T100 PCR machine (Bio-Rad, USA). For quantification of RNA levels, qRT-PCR analysis was performed using the Qiagen miScript SYBR Green PCR technology (Hilden, Germany) on an ABI 7500 quantitative real-time PCR instrument (Life Technologies, USA). These integrated methodologies were consistently applied within our research group to evaluate gene expression levels(Table 1 ). Table 1 Primer sequences used for qRT-PCR assays piRBase ID 5’--3’ piR-hsa-88768 TGGTTCGTGCTGAAGGCCTG piR-hsa-150997 TGAATCTGACAACAGAGGCT piR-hsa-357123 TACTGGAAAGTGCACTTGGA piR-hsa-133872 TTTCAACTTAACTTGACCGC piR-hsa-31238 TAGAGCATGAGACTCTTAAT piR-hsa-132556 GATTTCAACTTAACTTGACC piR-hsa-154502 TTCAACTTAACTTGACCGCT uniq_84737 TGGAGTTAAAGACTTTTTCT hsa_circ_0038076 F: GACCTCTGGTCCTTAAACAAGG R: GAATGTAGCCTCGGTCATGTCG hsa_circ_0003520 F: CATCTGGACCATTAAAGCCACTC R: CCAAATGCCACACTGGGTTGC hsa_circ_0004381 F: GAAGCAACCAGTCTCGATCTG R: GACTGCGCCGGGTTGCTCTG ABCC1 F: CCTGGAAACGGACGACCTCATC R: CACCAAGCCGGCGTCTTTGGC NETO2 F: CCTCAGTTCCATGGAACTTC R: CAATGGATATGGATGCTTGTGC ARID1B F: GCTAGACTTGTCTGCTTACACG R: GGAGTGGCCAAGATCAGG 4.Statistical analysis The data were presented as mean ± SD. Statistical analyses were performed using SPSS 26.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism 7.0 (GraphPad Software, San Diego, CA, USA). The differences between ccRCC tissues and their paired adjacent tissues were evaluated using the Student's t-test. Statistical significance was defined as p values < 0.05. Results 1.1 Characteristics of piRNAs in ccRCC In our investigation of piRNAs, small RNA sequencing was performed on ccRCC and adjacent normal renal tissues. Clean reads were obtained from all six samples, with both ccRCC tissues and adjacent normal renal tissues exhibiting significant clean data. Additionally, we presented the predicted distribution of the first nucleotide of piRNAs and the base distribution of predicted piRNAs at each position (Fig. 1 A, B). The distribution of piRNAs across chromosomes was found to be non-uniform, with no correlation between the distribution density and chromosome length (Fig. 1 C). To characterize the genomic regions where piRNAs are distributed, putative piRNAs were aligned to transposon and gene sequences (Fig. 1 D). Analysis revealed that the read count of piRNAs originating from repeat sequence regions, including transposons (5,099,251; 57.01%), exceeded that from gene regions (37,128; 0.42%). Furthermore, examination of the unique piRNA sequence distribution across the genome indicated that 15.84% (14,803 reads) aligned with repeat sequence regions while only 1.34% (1,255 reads) mapped to gene regions. 1.2 Differentially expressed piRNAs Unique putative piRNAs were detected in all six libraries. By applying stringent filtering criteria (q-value 1), we identified 22 significantly differentially expressed piRNAs between adjacent normal renal tissues and ccRCC tissues. Among these, 17 piRNAs exhibited upregulation, while 5 showed downregulation in the adjacent normal renal samples compared to the ccRCC tissues (Fig. 2 A). 1.3 Differential expression analysis of piRNA clusters The differentially expressed piRNA clusters within the shared piRNA clusters between adjacent normal renal samples and ccRCC tissues were identified using stringent filtering criteria, with a q-value 1. We observed three piRNA clusters that exhibited differential expression between adjacent normal renal tissues and ccRCC tissues, with two being upregulated and one downregulated in adjacent normal renal samples compared to ccRCC tissues (Fig. 2 B). A heatmap was used to visually represent the distinct expression patterns of these piRNA clusters, segregating them into two clusters corresponding to adjacent normal renal samples and ccRCC tissues (Fig. 2 C). 1.4 Validation of differentially expressed piRNAs Consistent with the sequencing data, reverse transcription polymerase chain reaction (RT-PCR) analysis revealed a significant downregulation of piR-has-150997, 133872, 132556, 154502, and uniq-84737 expression levels in clear cell renal cell carcinoma (ccRCC) samples (p 0.05) (Fig. 3 A/B). 2.1 Differentially expressed circRNAs The presence of all unique putative circRNAs was detected across all six libraries. Among these, significantly differentially expressed circRNAs were identified using stringent filtering criteria with a q-value 1. We observed 22 circRNAs that exhibited differential expression between adjacent normal renal tissues and ccRCC tissues, with 490 upregulated and 694 downregulated in adjacent normal renal samples compared to ccRCC tissues (Fig. 4 A). Based on the length and expression levels of circRNAs, we selected 23 significantly differentially expressed circRNAs for presentation in Table 2 . Table 2 Differentially expressed circRNAs 2.2 Validation of differentially expressed circRNAs Consistent with the sequencing data, RT-PCR analysis demonstrated significantly upregulated expression levels of circABCC1 and circNETO2_006, while downregulation was observed in the expression level of circARID1B_037 in the adjacent tissue group (p < 0.05) (Fig. 5 ). 3 Function of piRNAs and circRNAs as indicators for ccRCC diagnosis Subsequently, the area under the receiver operating characteristic (ROC) curve (AUC) for piR-has-150997, 133872, 132556, 154502, and uniq-84737 in ccRCC samples were determined to be 0.734, 0.715, 0.708, 0.705, and 0.748 respectively. The AUC values for circABCC1, circNETO2_006, and circARID1B_037 in ccRCC samples were found to be 0.742, 0.657 and 0.704 respectively. Integration of piRNAs with circRNAs significantly improved the diagnostic performance of ccRCC by developing a composite diagnostic model that included piR-hsa-150997, piR-hsa-133872, piR-hsa-132556, piR-hsa-154502, uniq_84737, circABCC1, circNETO2_006 and circARID1B_037 which achieved an impressive AUC value of up to 0.878, compared to a model consisting with piR-hsa-150997, uniq_84737, circABCC1, circNETO2_006, and circARID1B_037 had an AUC of 0.872. These diagnostic models hold great potential as superior clinical diagnostic markers for ccRCC patients (Fig. 6 A/B/C/D; Table 3 ). Figure 6 C/D. The diagnostic value of differential circRNAs and differential piRNAs expression in ccRCC (n = 68) Table 3 Diagnostic ROC curve area of differential circRNAs and piRNAs expression in ccRCC test outcome variables AUC standard error a Asymptotic significance b Asymptotic 95% confidence intervals lower limit Upper limit piR-hsa-150997 + piR-hsa-133872 + piR-hsa-132556 + piR-hsa-154502 + uniq_84737 + circABCC1 + circNETO2_006 + circARID1B_037 0.878 0.029 0.000 0.821 0.934 piR-hsa-150997 + uniq_84737 + circABCC1 + circNETO2_006 + circARID1B_037 0.872 0.030 0.000 0.814 0.931 piR-hsa-150997 0.734 0.043 0.000 0.649 0.818 piR-hsa-133872 0.715 0.044 0.000 0.628 0.801 piR-hsa-132556 0.708 0.045 0.000 0.620 0.795 piR-hsa-154502 0.705 0.045 0.000 0.616 0.794 uniq_84737 0.748 0.043 0.000 0.664 0.831 circABCC1 0.743 0.042 0.000 0.661 0.825 circNETO2_006 0.657 0.047 0.002 0.565 0.749 circARID1B_037 0.704 0.045 0.000 0.616 0.792 a.According to nonparametric assumption; b.Null hypothesis: true region = 00.5 Discussion The present study elucidates the clinical significance, intrinsic mechanisms, and biological functions of piRNAs and circRNAs in RCC, providing invaluable insights for cancer exploration and the development of novel therapeutic targets in a wide array of cancers including testicular tumors 5 , cervical cancer 6 , epithelial ovarian cancer 7 , gastric cancer 8 , breast cancer 9 , multiple myeloma 10 ,liver cancer 11 ,colorectal cancer 12 ,and bladder cancer 13 . Notably, piRNAs have been implicated in the de novo regulation of DNA methylation 14 – 16 , and the Piwi-piRNA pathway has been demonstrated to uphold genome integrity in germline stem cells by repressing transposition factors through DNA methylation 17 – 19 . Furthermore, Zhao et al. investigated the expression levels of mitochondrial piR-51810 and piR-34536 in tissues and serum samples from ccRCC patients, revealing significantly decreased expression levels in ccRCC tissues compared to normal tissues 20 . Emerging evidence suggests that this silencing mechanism also extends to non-germline genomic regions with tumor backgrounds, such as tumor suppressor genes, resulting in an aberrant "stem-like" state and subsequent tumorigenesis 21 – 22 . The downregulation of mitochondrial piRNAs was found to be negatively associated with the prognosis of ccRCC patients, highlighting the clinical relevance of these non-coding RNAs. However, the precise functions of piRNAs and their generation pathway genes remain elusive in ccRCC. It is suggested that piRNAs act as epigenetic regulators involved in tumor angiogenesis, invasiveness, growth, and metastasis 23 – 24 . The role of circRNAs in kidney disease is currently a subject of intense research, captivating the attention of scholars worldwide. Emerging evidence suggests that circRNAs possess immense potential to serve as invaluable biomarkers for disease activity and hold promise as therapeutic targets. Numerous ongoing studies are fervently focused on unraveling the intricate correlation between circRNAs and various types of human cancers 25 – 26 . Remarkably, circRNAs play a pivotal role in orchestrating cellular metabolism, with specific circRNAs implicated in modulating crucial processes such as glycolysis, lipolysis, lipogenesis, and oxidative phosphorylation. This profound understanding further enhances our comprehension of aberrant cell metabolism in cancer 27 .Furthermore, extracellular vesicle-derived circRNAs possess the capacity to be internalized by distal cells and exert a significant impact on crucial biological pathways within these recipient cells, thereby potentially facilitating tumor metastasis. Therefore, circRNAs demonstrate promising potential as diagnostic and prognostic biomarkers, as well as therapeutic targets for renal cancer 28 . In studies focused on kidney cancer, hsa_circ_0035483 has been shown to contribute to gemcitabine-induced autophagy, consequently enhancing resistance of RCC towards gemcitabine treatment. By functioning as a competitive endogenous RNA for hsa-miR-335, hsa_circ_0035483 augments resistance against gemcitabine and promotes tumor growth through modulation of cyclin B1 (CCNB1) expression 29 . Conversely, silencing of hsa_circ_0035483 enhances in vivo sensitivity to gemcitabine. Moreover, circRNAs display distinct expression patterns in response to cisplatin treatment, implying their involvement in the pathophysiology of cisplatin-induced nephrotoxicity. Investigation into the potential impact of radiation therapy on circRNA expression profiles revealed significant alterations upon irradiation of human embryonic kidney (HEK293T) cells. These findings suggest that CircRNAs may function as regulators of drug resistance in human cancer therapy and could be implicated in addressing drug resistance issues specific to kidney cancer treatment 30 . Therefore, elucidating the role and mechanism by which circRNAs function in kidney cancer is an urgent topic necessitating further investigation. Moreover, enclosed within extracellular vesicles, circRNAs can be internalized by distant cells, exerting influence on crucial biological pathways in these recipient cells and potentially facilitating tumor metastasis. Hence, circRNAs exhibit immense potential as diagnostic and prognostic biomarkers as well as appealing therapeutic targets for renal cancer. Our study aims to investigate the roles and mechanisms of circRNAs and piRNAs in ccRCC. This involves utilizing high-throughput sequencing to detect aberrantly expressed circRNAs and piRNAs in ccRCC, followed by screening for differentially expressed ones and validating them using clinical samples. Furthermore, we analyze the diagnostic value of circRNAs and piRNAs in ccRCC. Key findings include the identification of 17 upregulated and 5 downregulated piRNAs in tumor tissues compared to normal tissues, as well as 694 downregulated and 490 upregulated circRNAs in ccRCC tissues. Specifically, significant reductions were observed in specific piRNA species such as piR-has-150997, 133872, 132556, 154502, and uniq-84737 when comparing ccRCC samples with normal tissues. Additionally, elevated expression levels were detected for circABCC1 and circNETO2_006 while decreased expression was found for circARID1B_037 in ccRCC tissues. We evaluated the diagnostic performance of these identified circRNAs and piRNAs using AUC values which indicated good diagnostic potential. Moreover, a combined diagnostic model based on differentially expressed circRNAs and piRNAs demonstrated even higher AUC values suggesting its potential use as a superior diagnostic marker system for ccRCC. The main objective of this study was focused on detection and verification of pathological specimens, however, it should be noted that the evidence provided here is restricted to only one source with a limited number of samples. In order to augment the significance attributed to piRNAs and circRNAs in both diagnosis as well as treatment approaches for renal cell carcinoma (ccRCC), expanding sample sizes for further validation is highly recommended. Additionally, there exists a research gap pertaining to variations in expression levels among these biomarkers within urine, exosomes, as well as serum when considering ccRCC diagnosis or treatment options. Future research endeavors should strive towards addressing this knowledge void through refinement of research plans along with exploration into new investigative pathways, thus contributing towards an enhanced comprehension surrounding potential diagnostic capabilities alongside therapeutic roles played by piRNAs and circRNA molecules within ccRCC management practices. The ultimate goal of these endeavors is to develop pioneering strategies that efficiently handle patients with ccRCC. Conclusion 1.Abnormal expression of piRNAs in ccRCC included 17 significantly downregulated and 5 significantly upregulated piRNAs, as well as 2 downregulated and 1 upregulated piRNA clusters in tumor tissues. The expressions of piR-has-150997, 133872, 132556, 154502, and uniq-84737 were significantly lower in the ccRCC group compared to the normal tissue group, all showing excellent diagnostic performance for ccRCC. Abnormal expression of circRNAs in ccRCC included significant downregulation of 694 circRNAs and significant upregulation of 490 circRNAs in tumor tissues. The expressions of circABCC1 and circNETO2_006 were significantly higher than those in the adjacent tissue group, while circARID1B_037 was significantly lower. Combining specific piRNAs improved the diagnostic performance for ccRCC, with an AUC of 0.878 for the combined diagnostic model including piR-hsa-150997, piR-hsa-133872, piR-hsa-132556, piR-hsa-154502, uniq_84737,circABCC1,circNETO2_006,andcircARID1B_037. Additionally, the combination of piRs-hsa-150997, uniq_84737,circABCC1, circNETO2_006, and circARID1B_037 also had a high AUC of 0.872. These two models may serve as effective clinical diagnostic markers. Declarations Authors’Contributions Y.X., C.L. and B.H. conceptualized and designed the study; Y.Z. and H.L. contributed to formal analysis of data; Y.X., H.L., Y.Z., J.L. and HM.L. investigated the study; H.L., Y.Z. and J.L. contributed to data curation; C.L. and B.H. supervised the study; J.L. and HM.L. validated the study; Y.X., H.L., Y.Z. and J.L. visualized the study; Y.X., C.L. and B.H. was responsible for project ad-ministration; Y.X., H.L., Y.Z. and J.L. provided resources; Y.X. wrote the original draft; C.L. and B.H. reviewed and edited the manuscript. Yin Xu, Huiling Liu, Yingzhi Zhang and Jing Luo equally contributed to this work. Ethical Approval All samples were obtained by protocols approved by the Ethics Committee of the First Affliated Hospital of Jinan University in accordance with the Declaration of Helsinki. Consent Informed consent was obtained from each patient. Conflicts of Interest The authors declare that they have no conflicts of interest. 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Cite Share Download PDF Status: Published Journal Publication published 05 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 06 Jan, 2025 Reviews received at journal 30 Dec, 2024 Reviews received at journal 28 Dec, 2024 Reviewers agreed at journal 20 Dec, 2024 Reviewers agreed at journal 19 Dec, 2024 Reviewers agreed at journal 11 Nov, 2024 Reviewers agreed at journal 06 Sep, 2024 Reviewers agreed at journal 29 Jul, 2024 Reviewers invited by journal 12 Jul, 2024 Editor assigned by journal 12 Jul, 2024 Editor invited by journal 12 Jul, 2024 Submission checks completed at journal 08 Jul, 2024 First submitted to journal 03 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4681552","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":333015017,"identity":"83c74cd7-1ec7-497c-8905-dc24b045ae69","order_by":0,"name":"Yin Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACNvbm4x8+8EjU2x9vIFILH8+xNMYZMjYJDGcOEKlFTiLHjJnDJi2B4UYCsQ5jSDB7zJBzOI9x5uONNxhqbKKJ0HIg3bjgzOFiZum0YguGY2m5DQS1MDYckJ7Zc5ixTTrHTIKx4TARWpgZG6R5/x1m7JE8Q6wWNmY2aR6etMQZEjzEauFhYzacwWNjbMAD9EsCMX6Rn//+4wNgVMoZsB/eeONDjQ1hLcjAQCKBFOUQLaTqGAWjYBSMgpEBAO41PFL+yRr5AAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Jinan University","correspondingAuthor":true,"prefix":"","firstName":"Yin","middleName":"","lastName":"Xu","suffix":""},{"id":333015018,"identity":"63fe7428-1b40-4de5-b11e-6a11b2a20329","order_by":1,"name":"Huiling Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Huiling","middleName":"","lastName":"Liu","suffix":""},{"id":333015019,"identity":"807e348d-c20a-4f27-b1ca-7deee0d1159f","order_by":2,"name":"Yingzhi Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Yingzhi","middleName":"","lastName":"Zhang","suffix":""},{"id":333015020,"identity":"cbf1597a-9a74-4f44-818d-a2bda01f1ab6","order_by":3,"name":"Jing Luo","email":"","orcid":"","institution":"The First College of Clinical Medical Science, China Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Luo","suffix":""},{"id":333015021,"identity":"7241b2ba-0b47-45c5-a383-8aa25e25e661","order_by":4,"name":"Haomin Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Haomin","middleName":"","lastName":"Li","suffix":""},{"id":333015022,"identity":"6a9586e8-dffc-467b-9ad9-79274cc644d6","order_by":5,"name":"Caiyong Lai","email":"","orcid":"","institution":"The First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Caiyong","middleName":"","lastName":"Lai","suffix":""},{"id":333015023,"identity":"676b732c-e2cb-4762-b48c-94c9333860bb","order_by":6,"name":"Baoli Heng","email":"","orcid":"","institution":"The First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Baoli","middleName":"","lastName":"Heng","suffix":""}],"badges":[],"createdAt":"2024-07-03 15:44:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4681552/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4681552/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-90874-8","type":"published","date":"2025-03-05T15:58:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61499432,"identity":"65ff64fa-b68b-4e3c-8386-6aac1c1c3095","added_by":"auto","created_at":"2024-07-31 12:23:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1603580,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4681552/v1/c097d9d4996b0d106b1db6a3.png"},{"id":61499437,"identity":"55c3c4ee-ead3-45b8-96c6-fe18038a14b9","added_by":"auto","created_at":"2024-07-31 12:23:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":864823,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4681552/v1/b31e99a2157a7e82d3da0c5d.png"},{"id":61499434,"identity":"90afbc6d-f836-46ba-8092-fe0024177656","added_by":"auto","created_at":"2024-07-31 12:23:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":467940,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4681552/v1/747af6fc8a49eba55db8826a.png"},{"id":61499438,"identity":"fc567f26-a936-41dd-be37-745539cd92c9","added_by":"auto","created_at":"2024-07-31 12:23:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":426029,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4681552/v1/2e068b386293ac48a89d71b9.png"},{"id":61500392,"identity":"d14a9378-9e4d-47ee-a0a4-38c62dbe0702","added_by":"auto","created_at":"2024-07-31 12:39:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":234652,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4681552/v1/9d15bcc92280814e242023e1.png"},{"id":61499975,"identity":"62ec6f99-6ab4-4c49-9748-cfe1f7e34aab","added_by":"auto","created_at":"2024-07-31 12:31:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":863161,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4681552/v1/c5b2c294504759627758e3ee.png"},{"id":78190633,"identity":"25fd7ad1-9fe5-46e0-a1a9-31467e6a5ca5","added_by":"auto","created_at":"2025-03-10 19:50:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6199399,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4681552/v1/28b80733-0512-49ce-8e6a-c1a6e5adbeb8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"piRNAs and circRNAs acting as diagnostic biomarkers in clear cell renal cell carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite being the most common genitourinary malignancy, renal cell carcinoma (RCC) often goes undetected until it reaches an advanced stage. Timely identification of RCC plays a pivotal role in managing patients effectively and reducing mortality rates; however, there remains a dearth of optimal screening techniques\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Currently, computed tomography (CT) serves as the primary modality for diagnosing RCC, while surgical intervention proves to be highly effective in treating localized forms of this disease\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Unfortunately, approximately 30% of patients are diagnosed with distant metastasis upon initial evaluation which not only hampers their chances for receiving timely treatment but also leads to an unfavorable prognosis\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. As a result thereof these developments have played an instrumental role in identifying more efficient strategies pertaining to diagnosis and management by shedding light on its molecular characteristics alongside therapeutic interventions\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Material and method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.Collection of paired clinical tissue samples\u003c/h2\u003e\u003cp\u003e The clinical tissues utilized in this study were obtained from patients who underwent surgical tumorectomies at the Department of Urology, First Affiliated Hospital of Jinan University, between 2014 and 2018. Upon collection, the tissues were immediately immersed in RNA-later for preservation and subsequently refrigerated at 4\u003csup\u003eo\u003c/sup\u003eC overnight. Following this, the samples were transferred to liquid nitrogen for long-term storage. Histological and pathological diagnoses of the clinical samples were confirmed by experienced clinical pathologists as per the study protocol. Prior to sample collection, all patients provided informed consent for their tissue utilization in research, and the study protocol was approved by the Ethical Committee of the First Affiliated Hospital of Jinan University in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.High-throughput Sequencing\u003c/h2\u003e\u003cp\u003eThe sequencing data of piRNAs and circRNAs were obtained from the preliminary data of our research group. In order to comprehensively analyze and compare the datasets, we have re-extracted sequencing data with significant differences, which is crucial for understanding the implications of these variations\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.RT-PCR\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted using TRIzol (Life Technologies, USA) according to the\u003c/p\u003e\u003cp\u003emanufacturer's instructions, as is customary. Subsequently, the miScript II RT Kit (Qiagen, Hilden, Germany) was employed for reverse transcription of RNA into cDNA utilizing random primers. RT-PCR reactions were conducted on a Bio-Rad T100 PCR machine (Bio-Rad, USA). For quantification of RNA levels, qRT-PCR analysis was performed using the Qiagen miScript SYBR Green PCR technology (Hilden, Germany) on an ABI 7500 quantitative real-time PCR instrument (Life Technologies, USA). These integrated methodologies were consistently applied within our research group to evaluate gene expression levels(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer sequences used for qRT-PCR assays\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiRBase ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;--3\u0026rsquo;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiR-hsa-88768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGTTCGTGCTGAAGGCCTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiR-hsa-150997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGAATCTGACAACAGAGGCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiR-hsa-357123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTACTGGAAAGTGCACTTGGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiR-hsa-133872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTTCAACTTAACTTGACCGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiR-hsa-31238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAGAGCATGAGACTCTTAAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiR-hsa-132556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGATTTCAACTTAACTTGACC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiR-hsa-154502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTCAACTTAACTTGACCGCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003euniq_84737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGAGTTAAAGACTTTTTCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa_circ_0038076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: GACCTCTGGTCCTTAAACAAGG\u003c/p\u003e \u003cp\u003eR: GAATGTAGCCTCGGTCATGTCG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa_circ_0003520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: CATCTGGACCATTAAAGCCACTC\u003c/p\u003e \u003cp\u003eR: CCAAATGCCACACTGGGTTGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa_circ_0004381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: GAAGCAACCAGTCTCGATCTG\u003c/p\u003e \u003cp\u003eR: GACTGCGCCGGGTTGCTCTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABCC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: CCTGGAAACGGACGACCTCATC\u003c/p\u003e \u003cp\u003eR: CACCAAGCCGGCGTCTTTGGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNETO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: CCTCAGTTCCATGGAACTTC\u003c/p\u003e \u003cp\u003eR: CAATGGATATGGATGCTTGTGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARID1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: GCTAGACTTGTCTGCTTACACG\u003c/p\u003e \u003cp\u003eR: GGAGTGGCCAAGATCAGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.Statistical analysis\u003c/h2\u003e \u003cp\u003eThe data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Statistical analyses were performed using SPSS 26.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism 7.0 (GraphPad Software, San Diego, CA, USA). The differences between ccRCC tissues and their paired adjacent tissues were evaluated using the Student's t-test. Statistical significance was defined as p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Characteristics of piRNAs in ccRCC\u003c/h2\u003e \u003cp\u003eIn our investigation of piRNAs, small RNA sequencing was performed on ccRCC and adjacent normal renal tissues. Clean reads were obtained from all six samples, with both ccRCC tissues and adjacent normal renal tissues exhibiting significant clean data. Additionally, we presented the predicted distribution of the first nucleotide of piRNAs and the base distribution of predicted piRNAs at each position (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). The distribution of piRNAs across chromosomes was found to be non-uniform, with no correlation between the distribution density and chromosome length (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eTo characterize the genomic regions where piRNAs are distributed, putative piRNAs were aligned to transposon and gene sequences (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Analysis revealed that the read count of piRNAs originating from repeat sequence regions, including transposons (5,099,251; 57.01%), exceeded that from gene regions (37,128; 0.42%). Furthermore, examination of the unique piRNA sequence distribution across the genome indicated that 15.84% (14,803 reads) aligned with repeat sequence regions while only 1.34% (1,255 reads) mapped to gene regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Differentially expressed piRNAs\u003c/h2\u003e \u003cp\u003eUnique putative piRNAs were detected in all six libraries. By applying stringent filtering criteria (q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and |log2(fold change)| \u0026gt; 1), we identified 22 significantly differentially expressed piRNAs between adjacent normal renal tissues and ccRCC tissues. Among these, 17 piRNAs exhibited upregulation, while 5 showed downregulation in the adjacent normal renal samples compared to the ccRCC tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Differential expression analysis of piRNA clusters\u003c/h2\u003e \u003cp\u003eThe differentially expressed piRNA clusters within the shared piRNA clusters between adjacent normal renal samples and ccRCC tissues were identified using stringent filtering criteria, with a q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and |log2(fold change)| \u0026gt; 1. We observed three piRNA clusters that exhibited differential expression between adjacent normal renal tissues and ccRCC tissues, with two being upregulated and one downregulated in adjacent normal renal samples compared to ccRCC tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). A heatmap was used to visually represent the distinct expression patterns of these piRNA clusters, segregating them into two clusters corresponding to adjacent normal renal samples and ccRCC tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Validation of differentially expressed piRNAs\u003c/h2\u003e \u003cp\u003eConsistent with the sequencing data, reverse transcription polymerase chain reaction (RT-PCR) analysis revealed a significant downregulation of piR-has-150997, 133872, 132556, 154502, and uniq-84737 expression levels in clear cell renal cell carcinoma (ccRCC) samples (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, there was no significant difference in the expression of piR-has-88768, 357123, and 31238 between the two groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eA/B).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Differentially expressed circRNAs\u003c/h2\u003e \u003cp\u003eThe presence of all unique putative circRNAs was detected across all six libraries. Among these, significantly differentially expressed circRNAs were identified using stringent filtering criteria with a q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and |log2(fold change)| \u0026gt; 1. We observed 22 circRNAs that exhibited differential expression between adjacent normal renal tissues and ccRCC tissues, with 490 upregulated and 694 downregulated in adjacent normal renal samples compared to ccRCC tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Based on the length and expression levels of circRNAs, we selected 23 significantly differentially expressed circRNAs for presentation in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable 2 Differentially expressed circRNAs\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"690\" height=\"331\"\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Validation of differentially expressed circRNAs\u003c/h2\u003e \u003cp\u003eConsistent with the sequencing data, RT-PCR analysis demonstrated significantly upregulated expression levels of circABCC1 and circNETO2_006, while downregulation was observed in the expression level of circARID1B_037 in the adjacent tissue group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3 Function of piRNAs and circRNAs as indicators for ccRCC diagnosis\u003c/h2\u003e \u003cp\u003eSubsequently, the area under the receiver operating characteristic (ROC) curve (AUC) for piR-has-150997, 133872, 132556, 154502, and uniq-84737 in ccRCC samples were determined to be 0.734, 0.715, 0.708, 0.705, and 0.748 respectively. The AUC values for circABCC1, circNETO2_006, and circARID1B_037 in ccRCC samples were found to be 0.742, 0.657 and 0.704 respectively. Integration of piRNAs with circRNAs significantly improved the diagnostic performance of ccRCC by developing a composite diagnostic model that included piR-hsa-150997, piR-hsa-133872, piR-hsa-132556, piR-hsa-154502, uniq_84737, circABCC1, circNETO2_006 and circARID1B_037 which achieved an impressive AUC value of up to 0.878, compared to a model consisting with piR-hsa-150997, uniq_84737, circABCC1, circNETO2_006, and circARID1B_037 had an AUC of 0.872. These diagnostic models hold great potential as superior clinical diagnostic markers for ccRCC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e6\u003c/span\u003eA/B/C/D; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e6\u003c/span\u003eC/D. The diagnostic value of differential circRNAs and differential piRNAs expression in ccRCC (n\u0026thinsp;=\u0026thinsp;68)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic ROC curve area of differential circRNAs and piRNAs expression in ccRCC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003etest outcome variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003estandard error\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAsymptotic significance\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eAsymptotic 95% confidence intervals\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003elower limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper limit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiR-hsa-150997\u0026thinsp;+\u0026thinsp;piR-hsa-133872\u0026thinsp;+\u0026thinsp;piR-hsa-132556\u0026thinsp;+\u0026thinsp;piR-hsa-154502\u0026thinsp;+\u0026thinsp;uniq_84737\u0026thinsp;+\u0026thinsp;circABCC1\u0026thinsp;+\u0026thinsp;circNETO2_006\u0026thinsp;+\u0026thinsp;circARID1B_037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiR-hsa-150997\u0026thinsp;+\u0026thinsp;uniq_84737\u0026thinsp;+\u0026thinsp;circABCC1\u0026thinsp;+\u0026thinsp;circNETO2_006\u0026thinsp;+\u0026thinsp;circARID1B_037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiR-hsa-150997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiR-hsa-133872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiR-hsa-132556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiR-hsa-154502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003euniq_84737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecircABCC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecircNETO2_006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecircARID1B_037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ea.According to nonparametric assumption; b.Null hypothesis: true region\u0026thinsp;=\u0026thinsp;00.5\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study elucidates the clinical significance, intrinsic mechanisms, and biological functions of piRNAs and circRNAs in RCC, providing invaluable insights for cancer exploration and the development of novel therapeutic targets in a wide array of cancers including testicular tumors\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, cervical cancer\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, epithelial ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, gastric cancer\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, breast cancer\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, multiple myeloma\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e,liver cancer\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e,colorectal cancer\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e,and bladder cancer\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, piRNAs have been implicated in the de novo regulation of DNA methylation \u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and the Piwi-piRNA pathway has been demonstrated to uphold genome integrity in germline stem cells by repressing transposition factors through DNA methylation \u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Furthermore, Zhao et al. investigated the expression levels of mitochondrial piR-51810 and piR-34536 in tissues and serum samples from ccRCC patients, revealing significantly decreased expression levels in ccRCC tissues compared to normal tissues\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Emerging evidence suggests that this silencing mechanism also extends to non-germline genomic regions with tumor backgrounds, such as tumor suppressor genes, resulting in an aberrant \"stem-like\" state and subsequent tumorigenesis\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The downregulation of mitochondrial piRNAs was found to be negatively associated with the prognosis of ccRCC patients, highlighting the clinical relevance of these non-coding RNAs. However, the precise functions of piRNAs and their generation pathway genes remain elusive in ccRCC. It is suggested that piRNAs act as epigenetic regulators involved in tumor angiogenesis, invasiveness, growth, and metastasis\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe role of circRNAs in kidney disease is currently a subject of intense research, captivating the attention of scholars worldwide. Emerging evidence suggests that circRNAs possess immense potential to serve as invaluable biomarkers for disease activity and hold promise as therapeutic targets. Numerous ongoing studies are fervently focused on unraveling the intricate correlation between circRNAs and various types of human cancers\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Remarkably, circRNAs play a pivotal role in orchestrating cellular metabolism, with specific circRNAs implicated in modulating crucial processes such as glycolysis, lipolysis, lipogenesis, and oxidative phosphorylation. This profound understanding further enhances our comprehension of aberrant cell metabolism in cancer\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.Furthermore, extracellular vesicle-derived circRNAs possess the capacity to be internalized by distal cells and exert a significant impact on crucial biological pathways within these recipient cells, thereby potentially facilitating tumor metastasis. Therefore, circRNAs demonstrate promising potential as diagnostic and prognostic biomarkers, as well as therapeutic targets for renal cancer \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. In studies focused on kidney cancer, hsa_circ_0035483 has been shown to contribute to gemcitabine-induced autophagy, consequently enhancing resistance of RCC towards gemcitabine treatment. By functioning as a competitive endogenous RNA for hsa-miR-335, hsa_circ_0035483 augments resistance against gemcitabine and promotes tumor growth through modulation of cyclin B1 (CCNB1) expression \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Conversely, silencing of hsa_circ_0035483 enhances in vivo sensitivity to gemcitabine. Moreover, circRNAs display distinct expression patterns in response to cisplatin treatment, implying their involvement in the pathophysiology of cisplatin-induced nephrotoxicity. Investigation into the potential impact of radiation therapy on circRNA expression profiles revealed significant alterations upon irradiation of human embryonic kidney (HEK293T) cells. These findings suggest that CircRNAs may function as regulators of drug resistance in human cancer therapy and could be implicated in addressing drug resistance issues specific to kidney cancer treatment \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Therefore, elucidating the role and mechanism by which circRNAs function in kidney cancer is an urgent topic necessitating further investigation. Moreover, enclosed within extracellular vesicles, circRNAs can be internalized by distant cells, exerting influence on crucial biological pathways in these recipient cells and potentially facilitating tumor metastasis. Hence, circRNAs exhibit immense potential as diagnostic and prognostic biomarkers as well as appealing therapeutic targets for renal cancer.\u003c/p\u003e \u003cp\u003eOur study aims to investigate the roles and mechanisms of circRNAs and piRNAs in ccRCC. This involves utilizing high-throughput sequencing to detect aberrantly expressed circRNAs and piRNAs in ccRCC, followed by screening for differentially expressed ones and validating them using clinical samples. Furthermore, we analyze the diagnostic value of circRNAs and piRNAs in ccRCC. Key findings include the identification of 17 upregulated and 5 downregulated piRNAs in tumor tissues compared to normal tissues, as well as 694 downregulated and 490 upregulated circRNAs in ccRCC tissues. Specifically, significant reductions were observed in specific piRNA species such as piR-has-150997, 133872, 132556, 154502, and uniq-84737 when comparing ccRCC samples with normal tissues. Additionally, elevated expression levels were detected for circABCC1 and circNETO2_006 while decreased expression was found for circARID1B_037 in ccRCC tissues. We evaluated the diagnostic performance of these identified circRNAs and piRNAs using AUC values which indicated good diagnostic potential. Moreover, a combined diagnostic model based on differentially expressed circRNAs and piRNAs demonstrated even higher AUC values suggesting its potential use as a superior diagnostic marker system for ccRCC.\u003c/p\u003e \u003cp\u003e The main objective of this study was focused on detection and verification of pathological specimens, however, it should be noted that the evidence provided here is restricted to only one source with a limited number of samples. In order to augment the significance attributed to piRNAs and circRNAs in both diagnosis as well as treatment approaches for renal cell carcinoma (ccRCC), expanding sample sizes for further validation is highly recommended. Additionally, there exists a research gap pertaining to variations in expression levels among these biomarkers within urine, exosomes, as well as serum when considering ccRCC diagnosis or treatment options. Future research endeavors should strive towards addressing this knowledge void through refinement of research plans along with exploration into new investigative pathways, thus contributing towards an enhanced comprehension surrounding potential diagnostic capabilities alongside therapeutic roles played by piRNAs and circRNA molecules within ccRCC management practices. The ultimate goal of these endeavors is to develop pioneering strategies that efficiently handle patients with ccRCC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e1.Abnormal expression of piRNAs in ccRCC included 17 significantly downregulated and 5 significantly upregulated piRNAs, as well as 2 downregulated and 1 upregulated piRNA clusters in tumor tissues. The expressions of piR-has-150997, 133872, 132556, 154502, and uniq-84737 were significantly lower in the ccRCC group compared to the normal tissue group, all showing excellent diagnostic performance for ccRCC.\u003c/p\u003e \u003cp\u003eAbnormal expression of circRNAs in ccRCC included significant downregulation of 694 circRNAs and significant upregulation of 490 circRNAs in tumor tissues. The expressions of circABCC1 and circNETO2_006 were significantly higher than those in the adjacent tissue group, while circARID1B_037 was significantly lower.\u003c/p\u003e\u003cp\u003eCombining specific piRNAs improved the diagnostic performance for ccRCC, with an AUC of 0.878 for the combined diagnostic model including piR-hsa-150997, piR-hsa-133872, piR-hsa-132556, piR-hsa-154502, uniq_84737,circABCC1,circNETO2_006,andcircARID1B_037. Additionally, the combination of piRs-hsa-150997, uniq_84737,circABCC1, circNETO2_006, and circARID1B_037 also had a high AUC of 0.872. These two models may serve as effective clinical diagnostic markers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo;Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.X., C.L. and B.H. conceptualized and designed the study; Y.Z. and H.L. contributed to formal analysis of data; Y.X., H.L., Y.Z., J.L. and HM.L. investigated the study; H.L., Y.Z. and J.L. contributed to data curation; C.L. and B.H. supervised the study; J.L. and HM.L. validated the study; Y.X., H.L., Y.Z. and J.L. visualized the study; Y.X., C.L. and B.H. was responsible for project ad-ministration; Y.X., H.L., Y.Z. and J.L. provided resources; Y.X. wrote the original draft; C.L. and B.H. reviewed and edited the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYin Xu,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eHuiling Liu,\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eYingzhi Zhang and Jing Luo\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eequally contributed to this work.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll samples were obtained by protocols approved by the Ethics Committee of the First Affliated Hospital of Jinan University\u0026nbsp;in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from each patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDate is provided within the manuscript or contact this e-mile([email protected]) for more date information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHaomin Li, Baoli Heng,Peng Ouyan,Xuexia Xie,Tingshun Zhang,Guo Chen,Zheng Chen, Kahong Cheang, Caiyong Lai.Comprehensive profling of circRNAs and the tumor suppressor function of circHIPK3 in clear cell renal carcinoma.Journal of Molecular Histology (2020) 51:317\u0026ndash;327 \u003c/li\u003e\n\u003cli\u003eBahadoram S, Davoodi M, Hassanzadeh S, Bahadoram M, Barahman M, Mafakher L. Renal cell carcinoma: an overview of the epidemiology, diagnosis, and treatment. G Ital Nefrol. 2022 Jun 20;39(3):2022-vol3. \u003c/li\u003e\n\u003cli\u003eZhan X, Chen T, Liu Y, Wan H, Liu X, Deng X, Fu B, Xiong J: Trends in cause of death among patients with renal cell carcinoma in the United States: a SEER-based study. BMC public health 2023, 23:770.\u003c/li\u003e\n\u003cli\u003eLipworth L, Tarone RE, McLaughlin JK. The epidemiology of renal cell carcinoma. J Urol. 2006;176(6 pt 1):2353-2358.\u003c/li\u003e\n\u003cli\u003eFerreira HJ, Heyn H, Garcia del Muro X, Vidal A, Larriba S, Munoz C, Villanueva A, Esteller M: Epigenetic loss of the PIWI/piRNA machinery in human testicular tumorigenesis. Epigenetics : official journal of the DNA Methylation Society 2014, 9:113-8.\u003c/li\u003e\n\u003cli\u003eXie Q, Li Z, Luo X, Wang D, Zhou Y, Zhao J, Gao S, Yang Y, Fu W, Kong L, Sun T: piRNA-14633 promotes cervical cancer cell malignancy in a METTL14-dependent m6A RNA methylation manner. Journal of translational medicine 2022, 20:51.\u003c/li\u003e\n\u003cli\u003eLim SL, Ricciardelli C, Oehler MK, Tan IM, Russell D, Grutzner F: Overexpression of piRNA pathway genes in epithelial ovarian cancer. PloS one 2014, 9:e99687.\u003c/li\u003e\n\u003cli\u003eAmeli Mojarad M, Ameli Mojarad M, Shojaee B, Nazemalhosseini-Mojarad E: piRNA: A promising biomarker in early detection of gastrointestinal cancer. Pathology, research and practice 2022, 230:153757.\u003c/li\u003e\n\u003cli\u003eOu B, Liu Y, Gao Z, Xu J, Yan Y, Li Y, Zhang J: Senescent neutrophils-derived exosomal piRNA-17560 promotes chemoresistance and EMT of breast cancer via FTO-mediated m6A demethylation. Cell death \u0026amp; disease 2022, 13:905.\u003c/li\u003e\n\u003cli\u003eYan H, Wu QL, Sun CY, Ai LS, Deng J, Zhang L, Chen L, Chu ZB, Tang B, Wang K, Wu XF, Xu J, Hu Y: piRNA-823 contributes to tumorigenesis by regulating de novo DNA methylation and angiogenesis in multiple myeloma. Leukemia 2015, 29:196-206.\u003c/li\u003e\n\u003cli\u003eLaw PT, Qin H, Ching AK, Lai KP, Co NN, He M, Lung RW, Chan AW, Chan TF, Wong N: Deep sequencing of small RNA transcriptome reveals novel non-coding RNAs in hepatocellular carcinoma. Journal of hepatology 2013, 58:1165-73.\u003c/li\u003e\n\u003cli\u003eLiu Q, Chen Q, Zhou Z, Tian Z, Zheng X, Wang K: piRNA-18 Inhibition Cell Proliferation, Migration and Invasion in Colorectal Cancer. Biochemical genetics 2023.\u003c/li\u003e\n\u003cli\u003eChu H, Hui Yuan L, Shi D, Wang Y, Du M, Zhong D, Ma L, Tong N, Qin C, Yin C, Zhang Z, Wang M: Identification of novel piRNAs in bladder cancer. Cancer letters 2015, 356:561-7.\u003c/li\u003e\n\u003cli\u003eCheng J, Guo JM, Xiao BX, Miao Y, Jiang Z, Zhou H, Li QN: piRNA, the new non-coding RNA, is aberrantly expressed in human cancer cells. Clinica chimica acta; international journal of clinical chemistry 2011, 412:1621-5.\u003c/li\u003e\n\u003cli\u003eAravin AA, Sachidanandam R, Bourc\u0026apos;his D, Schaefer C, Pezic D, Toth KF, Bestor T, Hannon GJ: A piRNA pathway primed by individual transposons is linked to de novo DNA methylation in mice. Molecular cell 2008, 31:785-99.\u003c/li\u003e\n\u003cli\u003eAravin AA, Bourc\u0026apos;his D: Small RNA guides for de novo DNA methylation in mammalian germ cells. Genes \u0026amp; development 2008, 22:970-5\u003c/li\u003e\n\u003cli\u003eWatanabe T, Tomizawa S, Mitsuya K, Totoki Y, Yamamoto Y, Kuramochi-Miyagawa S, Iida N, Hoki Y, Murphy PJ, Toyoda A, Gotoh K, Hiura H, Arima T, Fujiyama A, Sado T, Shibata T, Nakano T, Lin H, Ichiyanagi K, Soloway PD, Sasaki H: Role for piRNAs and noncoding RNA in de novo DNA methylation of the imprinted mouse Rasgrf1 locus. Science 2011, 332:848-52.\u003c/li\u003e\n\u003cli\u003eCui L, Lou Y, Zhang X, Zhou H, Deng H, Song H, Yu X, Xiao B, Wang W, Guo J: Detection of circulating tumor cells in peripheral blood from patients with gastric cancer using piRNAs as markers. Clinical biochemistry 2011, 44:1050-7.\u003c/li\u003e\n\u003cli\u003eCheng J, Deng H, Xiao B, Zhou H, Zhou F, Shen Z, Guo J: piR-823, a novel non-coding small RNA, demonstrates in vitro and in vivo tumor suppressive activity in human gastric cancer cells. Cancer letters 2012, 315:12-7.\u003c/li\u003e\n\u003cli\u003eZhao C, Tolkach Y, Schmidt D, Toma M, Muders MH, Kristiansen G, Muller SC, Ellinger J: Mitochondrial PIWI-interacting RNAs are novel biomarkers for clear cell renal cell carcinoma. World journal of urology 2019, 37:1639-47.\u003c/li\u003e\n\u003cli\u003eFeldman N, Gerson A, Fang J et al. G9a-mediated irreversible epigenetic inactivation of Oct-3/4 during early embryogenesis. Nat Cell Biol. 2006;8(2):188\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eWang QX, Zhu YQ, Zhang H et al. Altered MiRNA expression in gastric cancer: a systematic review and meta-analysis. Cell Physiol Biochem.2015;35(3):933\u0026ndash;44.\u003c/li\u003e\n\u003cli\u003eKuramochi-Miyagawa S, Watanabe T, Gotoh K, Totoki Y, Toyoda A, Ikawa M, Asada N, Kojima K, Yamaguchi Y, Ijiri TW, Hata K, Li E, Matsuda Y, Kimura T, Okabe M, Sakaki Y, Sasaki H, Nakano T: DNA methylation of retrotransposon genes is regulated by Piwi family members MILI and MIWI2 in murine fetal testes. Genes \u0026amp; development 2008, 22:908-17.\u003c/li\u003e\n\u003cli\u003eAravin AA, Sachidanandam R, Girard A, Fejes-Toth K, Hannon GJ: Developmentally regulated piRNA clusters implicate MILI in transposon control. Science 2007, 316:744-7.\u003c/li\u003e\n\u003cli\u003eZhao X, Cai Y, Xu J: Circular RNAs: Biogenesis, Mechanism, and Function in Human Cancers. International journal of molecular sciences 2019, 20.\u003c/li\u003e\n\u003cli\u003eYin X, Lin H, Lin L, Miao L, He J, Zhuo Z: LncRNAs and CircRNAs in cancer. MedComm (2020) 2022, 3:e141.\u003c/li\u003e\n\u003cli\u003eJia DD, Jiang H, Zhang YF, Zhang Y, Qian LL, Zhang YF: The regulatory function of piRNA/PIWI complex in cancer and other human diseases: The role of DNA methylation. International journal of biological sciences 2022, 18:3358-73.\u003c/li\u003e\n\u003cli\u003eYin X, Lin H, Lin L, Miao L, He J, Zhuo Z: LncRNAs and CircRNAs in cancer. MedComm (2020) 2022, 3:e141.\u003c/li\u003e\n\u003cli\u003eKalluri R, LeBleu VS: The biology, function, and biomedical applications of exosomes. Science 2020, 367.\u003c/li\u003e\n\u003cli\u003eZhou Q, Ju LL, Ji X, Cao YL, Shao JG, Chen L: Plasma circRNAs as Biomarkers in Cancer. Cancer management and research 2021, 13:7325-37.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Clear cell renal cell carcinoma, piRNAs, circular RNAs, high-throughput sequencing, diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-4681552/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4681552/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003eThe discovery of diverse functions and mechanisms in cancer has underscored the significance of emerging non-coding RNAs (ncRNAs), such as PIWI-interacting RNAs (piRNAs) and circular RNAs (circRNAs), within the clinical context of cancer. Understanding their role in clear cell renal cell carcinoma (ccRCC) is imperative and necessitates comprehensive investigation. This study aims to further explore the diagnostic potential of piRNAs and circRNAs for ccRCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eThe dysregulated piRNAs and circRNAs in ccRCC were identified using small RNA (sRNA) high-throughput sequencing technology, while their expression in clinical samples was assessed by RT-qPCR. A paired t-test was performed to compare the expression levels of piRNAs and circRNAs between ccRCC and adjacent tissues. Additionally, ROC curve analysis was conducted to evaluate the diagnostic specificity, sensitivity, and area under the curve (AUC) of piRNAs and circRNAs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eHigh-throughput sequencing revealed a significant downregulation of 17 piRNAs and 694 circRNAs in ccRCC tissues, accompanied by a significant upregulation of 5 piRNAs and 490 circRNAs. RT-qPCR analysis demonstrated markedly lower expression levels of piR-has-150997, 133872, 132556, 154502, and uniq-84737 in the ccRCC group compared to the adjacent tissue group (p \u0026lt; 0.05). When considering the combined detection of piR-hsa-150997,piR-hsa-133872,\u003c/p\u003e\n\u003cp\u003epiR-hsa-132556,piR-hsa-154502,uniq_84737,circABCC1,circNETO2_006,and circARID1B_037,\u003c/p\u003e\n\u003cp\u003ethe diagnostic AUC for ccRCC was found to be high at an approximate value of AUC=0.878.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eThe diagnostic performance of piR-has-150997, 133872, 132556, 154502, uniq-84737, circABCC1, circNETO2_006, and circARID1B_037 demonstrates promise for ccRCC. A model incorporating piR-hsa-150997, uniq_84737, circABCC1, circNETO2_006, and circARID1B_037 could serve as an ideal diagnostic marker system with significant clinical utility.\u003c/p\u003e","manuscriptTitle":"piRNAs and circRNAs acting as diagnostic biomarkers in clear cell renal cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-31 12:23:45","doi":"10.21203/rs.3.rs-4681552/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-06T05:57:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-30T05:20:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-28T20:31:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112643072880970760941223598954089785555","date":"2024-12-20T18:08:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88578094256112762227996897356132760866","date":"2024-12-20T03:15:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318836840377172603047792959840204617538","date":"2024-11-11T20:51:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201226361681710128418411747932265256899","date":"2024-09-06T04:33:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253407100257225476380506353768468896361","date":"2024-07-29T07:28:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-12T23:51:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-12T23:50:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-12T09:32:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-08T11:43:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-03T15:43:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf021014-f8d3-429c-bf30-31a9a076da94","owner":[],"postedDate":"July 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":35256878,"name":"Biological sciences/Genetics/Cancer genetics"},{"id":35256879,"name":"Biological sciences/Genetics/Gene expression"}],"tags":[],"updatedAt":"2025-03-10T19:48:31+00:00","versionOfRecord":{"articleIdentity":"rs-4681552","link":"https://doi.org/10.1038/s41598-025-90874-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-05 15:58:43","publishedOnDateReadable":"March 5th, 2025"},"versionCreatedAt":"2024-07-31 12:23:45","video":"","vorDoi":"10.1038/s41598-025-90874-8","vorDoiUrl":"https://doi.org/10.1038/s41598-025-90874-8","workflowStages":[]},"version":"v1","identity":"rs-4681552","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4681552","identity":"rs-4681552","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}