Skip to main content
Download PDF
  • Systematic Review
  • Open access
  • Published:

The role of piRNAs in predicting and prognosing in cancer: a focus on piRNA-823 (a systematic review and meta-analysis)

BMC Cancer volume 24, Article number: 484 (2024) Cite this article

Abstract

Introduction

This article examines the potential of using liquid biopsy with piRNAs to study cancer survival outcomes. While previous studies have explored the relationship between piRNA expression and cancer patient outcomes, a comprehensive investigation is still lacking. To address this gap, we conducted a systematic review and meta-analysis of existing literature.

Methods

We searched major online databases up to February 2024 to identify articles reporting on the role of piRNA in cancer patient survival outcomes. Our meta-analysis used a random-effects model to pool hazard ratios with 95% confidence intervals (CI) and assess the prognostic value of deregulated piRNA-823. For survival analysis, the Kaplan–Meier method and COX analysis were used.

Results

Out of 6104 articles screened, 20 met our inclusion criteria. Our analysis revealed that dysregulated piRNA expression is associated with cancer patient survival outcomes. Specifically, our meta-analysis found that overexpression of piR-823 is significantly linked with poorer overall survival in patients with colorectal cancer and renal cell cancer (HR: 3.82, 95% CI = [1.81, 8.04], I2 = 70%).

Conclusion

Our findings suggest that various piRNAs may play a role in cancer survival outcomes and that piRNA-823 in particular holds promise as a prognostic biomarker for multiple human cancers.

Implications for cancer survivors

Our systematic review and meta-analysis of piRNA-823 has important implications for cancer survivors. Our findings suggest that piRNA-823 can be used as a prognostic biomarker for predicting cancer recurrence and survival rates. This information can help clinicians develop personalized treatment plans for cancer survivors, which can improve their quality of life and reduce the risk of recurrence.

Peer Review reports

Introduction

Cancer is a complex disease that is influenced by multiple factors, and its intricate pathology poses challenges to the prevention, diagnosis, treatment, and survival of the disease [1]. Although significant progress has been made in cancer diagnosis, such as Next-Generation sequencing (NGS) [2] and treatment options like neoadjuvant chemotherapy (NAC), targeted therapy, and gene therapy. The complete treatment of cancer remains a significant challenge. Despite these advancements, there are still numerous obstacles to overcome before achieving a cure for all types of cancer [3]. Early detection of cancer is vital as it significantly increases survival rates. Unfortunately, nearly half of all cancers are diagnosed at an advanced stage, which can limit treatment options and reduce the chances of cure [4].

Recent studies have shown that liquid biopsy is a highly promising and non-invasive approach for cancer diagnosis and monitoring [5,6,7]. This technique offers minimal invasiveness and repeated sampling, making it an attractive option for monitoring tumor occurrence and recurrence in real-time, as well as evaluating prognosis and treatment response [8]. In addition, it could be a valuable tool for advancing early detection efforts [9]. The idea of a liquid biopsy involves various methods used to find biomarkers in the body fluids like blood, plasma/serum, urine, CSF, saliva, ascites, or pleural effusion of people with cancer [10, 11]. The analysis of liquid biopsy is a growing area in translational cancer research with the potential to transform cancer treatment. Liquid biopsies can identify a range of circulating tumor products, including circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating messenger RNA (mRNA), circulating non-coding RNA, circulating extracellular vesicles (EVs), and tumor-educated platelets (TEPs) [10, 12].

Among the various biomarkers that can be detected by liquid biopsy, Piwi-interacting RNAs (piRNAs) are a novel class of small non-coding RNAs that play a crucial role in maintaining genome stability [13, 14]. They are typically 24–31 nucleotides in length and interact with PIWI proteins to form the piRNA silencing complex (piRISC) [15]. piRNAs are found in highly conserved clusters throughout the genome, but they are only present at a limited number of loci [16, 17]. Similar to miRNAs and siRNAs, the complexes of piRNAs and Piwi proteins may be involved in post-transcriptional gene silencing [18]. Particularly in the silencing of retrotransposons, this is partly because most piRNAs are antisense to retrotransposon sequences [19].

New research has demonstrated that piRNAs are expressed aberrantly in various cancer cell types and contribute to the development, progression, metastasis, and resistance to treatment of tumors [20]. Despite this, the potential clinical significance of their role is not yet fully understood [21]. There are several types of piRNAs that affect cancers [22, 23]. For example, piR-54265 is an oncogenic piRNA in colorectal cancer (CRC) and has elucidated its underlying molecular mechanism for driving malignant phenotypes in CRC cells [24]. piR-651 is associated with the progression of non-small cell lung carcinoma (NSCLC). A study found that high expression of piR-651 was associated with a higher risk of death in patients with NSCLC [25]. piR-823 is down-regulated in tumor tissue but positively correlated with worse outcomes, indicating its complex role in renal cell carcinoma (RCC) pathogenesis [26].

Among the piRNAs extensively investigated in cancer research, piRNA-823 stands out as a prominent subject. Recent researches indicate that increased expression of piRNA-823 is linked to the survival outcomes of cancer patients [26, 27]. Although there are many related records on the role of piRNA-823 in cancers, these records are scattered and lack systematic organization and summary. Therefore, this study summarizes and analyzes the role of expression patterns of piRNA-823 and the prognosis of cancer patients.

Based on the various roles of piRNAs in different types of cancer, to date, there is no literature that evaluates the correlation between dysregulation of piRNA expression levels and cancer prognosis. The results of some studies are inconsistent, and a single study may have insufficient data. Therefore, the objective of this systematic review and meta-analysis is to assess the impact of dysregulated expression of piRNAs on the survival outcome of cancer patients. Specifically, we will focus on the role of piRNA-823 and its prognostic significance for patient survival across different types of cancers. Our goal is to clarify the clinical relevance of piRNA-823 as a potential biomarker for cancer diagnosis and prognosis and to provide insights for future research in this area.

Methods

Systematic review

This report follows the PRISMA 2020 structure.

Eligibility criteria

We examined the correlation between the role of piRNAs and any of the following types of survival analysis: Overall/cumulative survival (OS), Progression-free survival (PFS), Disease-free survival (DFS), Recurrence-free survival (RFS), Event-free survival (EFS), and Metastasis-free survival (MFS). OS measures the time from diagnosis to death from any cause [28]; PFS measures the time from first treatment to the identification of cancer progression or death from any cause [28]; DFS measures of time after treatment during which no sign of cancer is found [29]; RFS measures the time from cancer cure to the identification of cancer progression/recurrence [30]; EFS measures of time after treatment that a group of people in a clinical trial has not had cancer come back or get worse [31]; and finally MFS measures the time from diagnosis to the identification of a metastatic event [32].

We utilized MeSH terms and added extra search terms to broaden the scope of our search and include a wider range of relevant studies. This approach was intended to make our analysis more comprehensive and ensure that we capture all relevant literature on the topic.

Exclusion criteria

We implemented the following criteria to exclude articles from our analysis: (i) articles that were not accessible in full-text electronically; (ii) articles published in languages other than English; (iii) comments, letters, editorials, protocols, guidelines, case reports, and review articles; (iv) in vitro or preclinical studies; (v) studies lacking sufficient outcome data; (vi) studies that solely investigated genetic alterations of piRNAs, such as polymorphisms or methylation patterns.

We retained studies that reported hazard ratios (HR) and standard error (SE) or confidence interval (CI) or provided life tables for comparing two groups of high- and low-expression levels of various piRNAs [33, 34]. Hence, those studies with more than two groups (e.g. comparing quartiles of expression levels) were excluded from review and meta-analysis [35, 36].

Inclusion criteria

Our inclusion criteria for articles were as follows: (i) studies that examined piRNA expression among cancer patients and control groups; (ii) studies that included data on survival outcomes of patients; (iii) any study that involved quantitative analysis of piRNAs using methods such as quantitative PCR (qPCR), in situ hybridization (ISH), microarray, or sequencing was eligible for inclusion; iii) Studies with sufficient data to generate HR and 95% CI or Kaplan–Meier curves, and (iv) studies published in the English language.

Information sources

We conducted a systematic search of four databases (PubMed, Scopus, Web of Science, and Wiley Online Library) spanning 8 years from 2015 to 2024. Two independent authors (MT and AK) performed the search to identify potentially eligible articles published in the English language. Our analysis focused on investigating the associations between expression levels of piRNAs and prognosis in human cancer. We used a combination of MeSH terms and subsequent keywords to refine our search strategy., including (“Overall Survival”, “Disease-Free Survival”, “Progression-Free Survival”, “Recurrence-free survival”, “Event-free Survival”, “Survival Analysis”, “Kaplan–Meier Estimate”) AND (“piRNA”, “piRNAs”, “PIWI-interacting ribonucleic acid”, “PIWI-interacting RNA”) AND (“cancer”, “carcinoma”, “tumor”, “neoplas*”, “tumor”, “malignan*”, “metastat*”, “metastas*”, “leukemia”, “leukemia”, “lymphoma”) were used to search the literature. Moreover, search terms that had no available MeSH terms included (“Liquid Biopsy Analysis”, “Prognostic Value”, “Prognostic Factor”, “Prognostic Indicator”, “Prognostic and Predictive Biomarkers”, “Recurrence Risk”, “Predictive for Outcome”, “recurrence*”,). The search was last updated to include articles published through February 1, 2024.

Study selection

We used the EndNote 21 to remove duplicate records. We screened the titles and abstracts of articles to identify those that were relevant to our study. The full manuscript of each relevant article was then screened against our eligibility criteria. Any uncertainties were resolved. by two of the authors (MT and AK) The data was collected and saved in Microsoft Excel Spreadsheet Software 2021. If the information was unclear or confusing, like when a study mentioned different piRNA quantification methods without specifying which one was used for the survival analysis, we moved it to a separate sheet and marked it as "unclear." To ensure thorough and transparent data collection, we gathered the following details from each study: basic study information such as the title, authors, publication year, number of patients, piRNA quantification methods, follow-up duration, cancer type, and journal of publication. We also collected additional details from eligible articles, including the study cohort's country of origin, sample size, and the total number of piRNAs analyzed.

Data synthesis and statistical analysis

In this study, the survival outcome was determined by utilizing data from various metrics to characterize the findings, including HR, Relative Risk (RR), Odds Ratio (OR), p-value, or Cox Regression, the type of analysis conducted (Univariable or Multivariable), the SE, and the number of censored participants during the follow-up period. HRs were aggregated using I2 statistics to assess the heterogeneity among the relevant studies.

We conducted a meta-analysis on piRNA-823 to evaluate univariate OS. SE was calculated using the formula (upper limit of CI/lower limit of CI) / (2 × 1.96). Estimates were synthesized using a random-effects model and estimated via the restricted maximum-likelihood ratio method. Heterogeneity was evaluated using Q and I2 statistics, with the 95% CI of I2 also computed [37, 38].

To calculate the effect size and 95% CI, it was imperative for the included studies to provide adequate information. Studies were required to report the HR and its 95% CI or present survival curves that could be digitized using GetData Graph Digitizer software to extract the HR value. In cases where articles only presented survival curves without HR and its 95% CI, digitization was conducted to obtain the necessary data. Forest plots were utilized to display the meta-analysis outcomes, with these analyses performed using Review Manager (RevMan) [39] and for publication bias used Comprehensive Meta-Analysis (CMA) [40]. Data for each type of piRNA were pooled irrespective of cancer type.

Results

Literature search and description of studies

The flowchart illustrates the search and selection strategy employed in the study (Fig. 1). We performed a systematic search for studies that investigated the potential role of piRNAs in the prognostic significance of cancer patients and identified 6,104 records using a detailed list of search terms (Fig. 1). After applying the exclusion criteria, a total of 1886 studies were excluded, resulting in 378 articles selected for further evaluation. Furthermore, 331 articles were excluded because of title and abstract screening criteria. Then 47 records remained eligible for full-text assessment. After full-text assessments, 20 studies meeting the search criteria were eligible for final review. Tables 1 and 2 provide an overview of the studies having eligible time-to-event analysis data and their main clinical characteristics.

Fig. 1
figure 1

In this study, we utilized the ‘PRISMA 2020 flow diagram’ template, which is a visual representation of the flow of information through the different phases of a systematic review. The template includes searches of only databases and registers. Please refer to the PRISMA flow diagram of the study for more details

* The trial registers including clinicaltrials.gov and trial search.who.int/AdvSearch.aspx were used in our search strategies

** The titles and abstracts of articles are not relevant to our research questions

*** Reasons:

Reason 1. There were no K-M survival curves indicating the specific event/endpoints

Reason 2. The risk sets in time intervals were not included in the HR (Hazard ratio). Also, asking the authors for raw data was not successful

Reason 3. Animal experiment included

Reason 4. Not available data

Full size image
Table 1 The main characteristics of the included studies
Full size table
Table 2 Main outcomes of the included studies
Full size table

Mapping of piRNA(s) prognostic data

After full-text assessments, 20 studies meeting the search criteria were eligible for final review. An overview of the studies having eligible overall survival analysis data and their main clinical characteristics are shown in Tables 1 and 2.

Out of the 20 studies analyzed, four focused primarily on RCC [26, 44, 50, 55], four on CRC [13, 24, 27, 53], three on Head and neck squamous carcinoma (HNSCC) [48, 49, 54], two on Gastric cancer (GC) [46, 47], one on Breast cancer (BC) [51], one on Diffuse large B cell lymphoma (DLBCL) [41], one on Ovarian cancer (OC) [42], one on Multiple myeloma (MM) [43], one on Glioblastoma (GBM) [45], one on NSCLC [25], and one on Hodgkin lymphoma (HL) [52].

Out of these 20 studies, nine records utilized high-throughput assays of RNA sequencing [13, 45, 47,48,49, 51, 53,54,55], seven used qRT-PCR [24,25,26, 43, 46, 50, 52], three used Microarray analysis [27, 41, 44], and one used ISH [42] for quantification of piRNAs in patients biological specimen. The majority of these studies were conducted in China (nine studies) [13, 24, 25, 27, 41,42,43, 46, 55], three studies followed by the USA [48, 49, 53], three in Canada [47, 51, 54], two in Czech Republic [26, 45], two in Germany [44, 50], and one in Spain [52].

The biological samples collected for analysis included serum (n = 5) [13, 24, 26, 43, 50], tissue (n = 11) [24,25,26,27, 47,48,49,50, 53,54,55], Formalin-Fixed Paraffin-Embedded (FFPE) samples (n = 4) [41, 42, 51, 52], blood samples (n = 1) [43], fresh-frozen tissues (n = 2) [44, 45], gastric juice samples (n = 1) [46], bone marrow aspirates (n = 1) [43], and urine samples (n = 1) [26].

The selected articles were published between 2015 and 2023. The median follow-up time ranged from 25 to 250 months. We have summarized this information in Tables 1 and 2.

Evaluation of the role of piRNA expression levels as potential prognostic markers in cancer patients

We conducted an analysis of twenty eligible studies to investigate the role of piRNA expression levels as prognostic markers in different cancer patients. Our findings indicate that six piRNAs, including piRNA-57125 [44], piRNA-651 [25, 52], FR237180 [54], piRNA-017724 [13], piRNA-34536 [50], and piRNA-51810 [50], have a significant prognostic impact on survival prediction. Conversely, the expression levels of twelve different piRNAs, such as piRNA-823 [26, 27, 43], piRNA-1245 [46, 53], piRNA-30924 [44], piRNA-38756 [44], piRNA-30473 [41], (piRNA-009051 and piRNA-021032) [51], piRNA-54265 [24], piRNA-1742 [55], FR-222326 [47], piRNA-58510 [49], and piRNA-3537 [49], were found to be lower and associated with poor survival outcomes. It is important to note that only univariate OS analysis was conducted in this study which is illustrated in Fig. 2. Moreover, we refrained from performing a pooled analysis due to the potential for misleading interpretations when combining various piRNA data.

Fig. 2
figure 2

Forest plot on the association between the expression levels of different piRNAs and survival in patients with different types of cancer

Full size image

The role of piRNA expression levels in the prognostic impact of renal cancer

Figure 3 depicts the forest plot of HR for seven piRNAs studied across four records of patients with renal RCC [26, 44, 50, 55]. Our analysis revealed that higher expression levels of three piRNAs, namely piRNA-57125 [44], piRNA-51810 [50], and piRNA-34536 [50], were significantly associated with better OS in RCC patients. Conversely, the overexpression levels of four other piRNAs, including piRNA-823 [26], piRNA-1742 [55], piRNA-38756 [44], and piRNA-30924 [44], were significantly linked to poorer outcomes in patients with RCC.

Fig. 3
figure 3

Forest plot on the association between the expression levels of different piRNAs and survival in patients with RCC

Full size image

The role of piRNA expression levels in the prognostic impact of colorectal cancer

We also investigated the prognostic impact of piRNAs on survival prediction in CRC [13, 24, 27, 53] patients based on four records. We found that a higher expression level of only one piRNA, piRNA-017724 [13], was significantly associated with better survival and outcome. On the other hand, the higher expression levels of three different piRNAs: piRNA-823 [27], piRNA-54265 [24], and piRNA-1245 [53], were significantly associated with poorer survival outcomes (Fig. 4).

Fig. 4
figure 4

Forest plot on the association between the expression levels of different piRNAs on survival in patients with CRC

Full size image

The role of piRNA-823 expression as a prognostic and predictive marker for piRNA-mediated regulation in CRC, MM and RCC

We performed a meta-analysis of three records in CRC [27], MM [43], and RCC [26] involving 836 patients to explore the association between overall survival and piRNA-823 expression. Pooled HRs showed that overexpression levels of piRNA-823 expression were associated with poorer OS (HR = 3.82; 95% CI, [1.81, 8.04]; P = 0.0004,) (Fig. 5). Our meta-analysis has attempted to combine multiple studies that are known to be heterogeneous in terms of cancer type. Our estimates of heterogeneity metrics have wide 95% CI indicating heterogeneity (I2 = 70%).

Fig. 5
figure 5

Forest plot on the association between piRNA-823 expression levels and survival outcome in CRC, MM and RCC patients

Full size image

Sensitivity analysis and publication bias

Sensitivity analyses were conducted to investigate methodological heterogeneity in these studies and assess the impact of individual study data on the overall outcome. No significant influence of any single study on the overall outcome was identified (as shown in Fig. 6). Additionally, a potential publication bias was examined using the Funnel plot, revealing no apparent publication bias in the included studies.

Fig. 6
figure 6

Funnel plot for the OS meta-analysis of piRNA-823 in different cancer types

Full size image

Discussion

Great improvements have been achieved in cancer detection and treatment. However, the 5-year survival rate remains relatively low for most cancers. Human health is seriously threatened by cancer [56]. piRNAs are a class of small non-coding RNA molecules that are expressed in the germline of many species [57]. They differ from other RNA molecules in their size and function [58]. Recent studies have suggested that piRNAs hold promise as biomarkers for early detection, prognosis, and novel biomarkers for progression and chemoresistance in cancer patients. Additionally, they may be useful in monitoring different cancer patients following treatment [59,60,61,62]. For example, the levels of piR-5937 and piR-28876 in the serum of CRC patients decreased significantly with the advanced clinical stage. However, both piRNA levels significantly increased in serum samples taken 1 month after surgery, indicating that their levels are linked to the presence of the tumor. These findings suggest that piRNAs could potentially serve as valuable tools for monitoring CRC progression and treatment response [63].

Recent research has highlighted inconsistencies in the role of piRNAs for monitoring patients, predicting relapse/recurrence, assessing treatment response, and determining patient prognosis. The potential role of piRNAs in contributing to survival prognosis remains uncertain. So, we collected different studies on piRNAs and how they affect the survival of cancer patients. Our goal is to do a meta-analysis to understand better how increased piRNA-823 expression relates to patient survival.

After conducting a systematic analysis of articles reporting on survival and piRNA data, we found that elevated expression levels of 12 specific piRNAs (piRNA-823, piRNA-1245, piRNA-30924, piRNA-38756, piRNA-30473, piRNA-009051, piRNA-021032, piRNA-54265, piRNA-1742, FR-222326, piRNA-58510, and piRNA-3537) are significantly associated with a higher risk of reduced OS in cancer patients. These piRNAs may play a proto-oncogenic role by promoting tumorigenicity when their expression is increased. Conversely, increased expression levels of 6 distinct piRNAs (piRNA-57125, piRNA-651, FR237180, piRNA-017724, piRNA-34536, and piRNA-51810) are significantly associated with improved OS in these patients. Increasing the expression of these piRNAs leads to decreased tumorigenicity.

Considering the larger amount of data for CRC and RCC compared to other cancer types, we concentrated our systematic analysis on these two types of cancer. This approach allowed us to conduct a more in-depth and comprehensive analysis of the available data, and to draw meaningful conclusions about the research question at hand. We acknowledge that this limitation may affect the generalizability of our findings to other types of cancer, and we have taken care to discuss this issue in the limitations section of our study.

The systematic analysis of data for RCC revealed that increasing the expression of three piRNAs (piRNA-57125, piRNA-51810, and piRNA-34536) is significantly associated with better OS. Conversely, increasing the expression of four different piRNAs (piRNA-823, piRNA-1742, piRNA-38756, and piRNA-30924) is significantly associated with decreasing OS. In the case of CRC, increased expression of piRNA-017724 is associated with increased OS in patients, while increased expression of three different piRNAs (piRNA-823, piRNA-54265, and piRNA-1245) is associated with decreased OS in patients. These findings suggest a crucial role for piRNAs in predicting patient outcomes in these specific cancer types.

We conducted a targeted meta-analysis on piRNA-823, given its significant role and the abundance of available records compared to other piRNAs. Our systematic review and meta-analysis revealed that overexpression levels of piRNA-823 are associated with poor prognosis across various types of cancer.

This meta-analysis included three studies that explained the functional roles of piRNA-823 in cancer prognosis. According to Ai et al., granulocytic-myeloid-derived suppressor cells (G-MDSCs) regulate cancer stemness in MM patients by activating piRNA-823, which leads to more DNA methylation and higher tumorigenic potential of MM cells [43]. Liev et al. demonstrated that piRNA-823 may play a role in preserving genomic stability, and the frequent loss of piRNA-823 in RCC tumor tissues could be associated with genomic instability, a common characteristic of malignant tumors They found that patients with RCC had significantly higher levels of piRNA-823 in their serum compared to healthy individuals. Furthermore, the up-regulation of piRNA-823 in the serum of RCC patients was associated with unfavorable clinical outcomes [26]. Junlan Feng et al. illustrated Knock-down of piRNA-823 inhibits the malignant characteristics of CRC cells and patients with CRC who have high levels of piRNA-823 expression are at a greater risk of experiencing poor OS and are less likely to respond positively to adjuvant chemotherapy [27].

Our findings suggest that seven specific piRNAs in RCC and four piRNAs in CRC could be useful for monitoring cancer patients and conducting survival analysis.

We suggest creating a test kit for piRNA-823 to assess the prognosis and monitoring of cancer patients. Additionally, we propose the creation of a panel consisting of 18 piRNAs for follow-up and treatment monitoring of cancer patients. Implementing these recommendations has the potential to improve patient outcomes and enhance the efficacy of cancer management strategies.

Strengths

Our meta-analysis offers several advantages. 1) It is the first study to systematically evaluate the correlation between piRNA expression and survival outcomes in cancer patients. 2) Our findings hold significant value, indicating that the sample size included in this meta-analysis was adequate. 3) This study showed high expression levels of piRNA-823 expression in cancer patients cause a greater risk of experiencing poor OS. 4) The included studies varied in the cancer pathological type that were analyzed, which often resulted in substantial heterogeneity between studies in the strength of the predictive effect.

Limitations

Although our meta-analysis has several strengths, it is important to acknowledge some of its limitations, which include: 1) This meta-analysis was limited to the evaluation of univariate OS due to the available studies that could be included in the analysis. 2) Survival analysis requires suitable data for PFS, DFS, and RFS. However, the lack of such data poses a significant challenge in this regard. 3) Obtaining raw data is crucial for survival prediction and analysis. However, the lack of access to such data poses a significant challenge, and researchers often have to rely on data obtained from the K-M chart. 4) The insufficiency of adequate data has resulted in the absence of meta-analyses on other piRNAs in various types of cancers. 5) Due to the potential for misleading interpretations when combining various piRNA data, a pooled analysis was not performed.

We detected 33 piRNAs in this study, mostly in serum or plasma. This may be because blood samples from cancer patients are more accessible. On the other hand, saliva, urine, and other body fluids are seldom used in research.

Conclusion

In conclusion, we have gathered a substantial amount of prognostic data on the association of various piRNAs with survival. This meta-analysis and systematic review provided evidence of a correlation between dysregulated piRNA expression levels and survival outcomes in patients with different types of cancer. Our findings indicated that altered expression of piRNA-823 was significantly associated with survival outcomes in cancer patients. Overexpression of piRNA-823 was linked to decreased OS in various cancer patients, suggesting its potential as a promising biomarker for predicting the prognosis of human cancers. In the future, further large-scale studies should be conducted to verify the clinical applications of altered piRNAs in assessing the prognosis of different cancers.

Availability of data and materials

Not applicable.

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.

    Article  PubMed  Google Scholar 

  2. Wu X, Zhu L, Ma PC. Next-generation novel noninvasive cancer molecular diagnostics platforms beyond tissues. Am Soc Clin Oncol Educ Book. 2018;38:964–77.

    Article  PubMed  Google Scholar 

  3. De Silva F, Alcorn J. A tale of two cancers: A current concise overview of breast and prostate cancer. Cancers (Basel). 2022;14(12):2954.

  4. Crosby D, Bhatia S, Brindle KM, Coussens LM, Dive C, Emberton M, et al. Early detection of cancer. Science. 2022;375(6586):eaay9040.

    Article  CAS  PubMed  Google Scholar 

  5. Castro-Giner F, Gkountela S, Donato C, Alborelli I, Quagliata L, Ng CKY, et al. Cancer diagnosis using a liquid biopsy: challenges and expectations. Diagnostics (Basel). 2018;8(2)31.

  6. Tivey A, Church M, Rothwell D, Dive C, Cook N. Circulating tumour DNA — looking beyond the blood. Nat Rev Clin Oncol. 2022;19(9):600–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ilié M, Hofman P. Pros: can tissue biopsy be replaced by liquid biopsy? Transl Lung Cancer Res. 2016;5(4):420–3.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Zhang Z, Wu H, Chong W, Shang L, Jing C, Li L. Liquid biopsy in gastric cancer: predictive and prognostic biomarkers. Cell Death Dis. 2022;13(10):903.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Tanos R, Thierry AR. Clinical relevance of liquid biopsy for cancer screening. Vol. 7, Translational cancer research. AME Publishing Company; 2018. p. S105–29.

  10. Lone SN, Nisar S, Masoodi T, Singh M, Rizwan A, Hashem S, et al. Liquid biopsy: a step closer to transform diagnosis, prognosis and future of cancer treatments. Mol Cancer. 2022;21(1):79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Pantel K, Alix-Panabières C. Liquid biopsy: Potential and challenges. Mol Oncol. 2016;10(3):371–3.

  12. Zhu J, Strickler JH. Clinical applications of liquid biopsies in gastrointestinal oncology. J Gastrointest Oncol. 2016;7(5):675–86.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Qu A, Wang W, Yang Y, Zhang X, Dong Y, Zheng G, et al. A serum piRNA signature as promising non-invasive diagnostic and prognostic biomarkers for colorectal cancer. Cancer Manag Res. 2019;11:3703–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lin Y, Zheng J, Lin D. PIWI-interacting RNAs in human cancer. Semin Cancer Biol. 2021;75:15–28.

    Article  CAS  PubMed  Google Scholar 

  15. Ghaseminezhad Z, Sharifi M, Bahreini A, Mehrzad V. Investigation of the expression of P-element-induced wimpy testis-interacting RNAs in human acute myeloid leukemia. Meta Gene. 2022;31:100998.

    Article  CAS  Google Scholar 

  16. Zhou T. Chapter 3 - Small non-coding RNAs as epigenetic regulators. In: Ferguson BS, editor. Nutritional epigenomics. 14: Academic Press; 2019. p. 37–47.

  17. O’Donnell KA, Boeke JD. Mighty Piwis defend the germline against genome intruders. Cell. 2007;129(1):37–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Klattenhoff C, Theurkauf W. Biogenesis and germline functions of piRNAs. Development. 2008;135(1):3–9.

    Article  CAS  PubMed  Google Scholar 

  19. Malone CD, Hannon GJ. Small RNAs as guardians of the genome. Cell. 2009;136(4):656–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Mokarram P, Niknam M, Sadeghdoust M, Aligolighasemabadi F, Siri M, Dastghaib S, et al. PIWI interacting RNAs perspectives: a new avenues in future cancer investigations. Bioengineered. 2021;12(2):10401–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hashim A, Rizzo F, Marchese G, Ravo M, Tarallo R, Nassa G, et al. RNA sequencing identifies specific PIWI-interacting small non-coding RNA expression patterns in breast cancer. Oncotarget. 2014;5(20):9901–10.

  22. Cheng Y, Wang Q, Jiang W, Bian Y, Zhou Y, Gou A, et al. Emerging roles of piRNAs in cancer: challenges and prospects. Aging (Albany NY). 2019;11(21):9932–46.

    Article  CAS  PubMed  Google Scholar 

  23. Weng W, Li H, Goel A. Piwi-interacting RNAs (piRNAs) and cancer: emerging biological concepts and potential clinical implications. Biochim Biophys Acta Rev Cancer. 2019;1871(1):160–9.

    Article  CAS  PubMed  Google Scholar 

  24. Mai D, Ding P, Tan L, Zhang J, Pan Z, Bai R, et al. PIWI-interacting RNA-54265 is oncogenic and a potential therapeutic target in colorectal adenocarcinoma. Theranostics. 2018;8(19):5213–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Li D, Luo Y, Gao Y, Yang Y, Wang Y, Xu Y, et al. piR-651 promotes tumor formation in non-small cell lung carcinoma through the upregulation of cyclin D1 and CDK4. Int J Mol Med. 2016;38(3):927–36.

    Article  CAS  PubMed  Google Scholar 

  26. Iliev R, Fedorko M, Machackova T, Mlcochova H, Svoboda M, Pacik D, et al. Expression levels of PIWI-interacting RNA, piR-823, are deregulated in tumor tissue, blood serum and urine of patients with renal cell carcinoma. Anticancer Res. 2016;36(12):6419–23.

    Article  CAS  PubMed  Google Scholar 

  27. Feng J, Yang M, Wei Q, Song F, Zhang Y, Wang X, et al. Novel evidence for oncogenic piRNA-823 as a promising prognostic biomarker and a potential therapeutic target in colorectal cancer. J Cell Mol Med. 2020;24(16):9028–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Delgado A, Guddati AK. Clinical endpoints in oncology - a primer. Am J Cancer Res. 2021;11(4):1121–31.

    PubMed  PubMed Central  Google Scholar 

  29. Robinson AG, Booth CM, Eisenhauer EA. Disease-free survival as an end-point in the treatment of solid tumours – perspectives from clinical trials and clinical practice. Eur J Cancer. 2014;50(13):2298–302.

    Article  PubMed  Google Scholar 

  30. Rodriguez-Pascual J, Alvarez-Gallego R, Munoz C, Ugidos L, Sanz E, Ceniceros L, et al. Relapse-free survival (RFS) after surgical resection to predict survival after relapse (SAR) and overall survival (OS) in patients with pancreatic ductal adenocarcinoma (PDA). J Clin Oncol. 2019;37(15_suppl):e15735-e.

    Article  Google Scholar 

  31. Anderton J, Moroz V, Marec-Bérard P, Gaspar N, Laurence V, Martín-Broto J, et al. International randomised controlled trial for the treatment of newly diagnosed EWING sarcoma family of tumours - EURO EWING 2012 Protocol. Trials. 2020;21(1):96.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Yu Y, Carey M, Pollett W, Green J, Dicks E, Parfrey P, et al. The long-term survival characteristics of a cohort of colorectal cancer patients and baseline variables associated with survival outcomes with or without time-varying effects. BMC Med. 2019;17(1):150.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Parmar MK, Torri V, Stewart L. Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Stat Med. 1998;17(24):2815–34.

    Article  CAS  PubMed  Google Scholar 

  34. Altman DG. Systematic reviews of evaluations of prognostic variables. BMJ. 2001;323(7306):224–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kärkkäinen E, Heikkinen S, Tengström M, Kosma VM, Mannermaa A, Hartikainen JM. The debatable presence of PIWI-interacting RNAs in invasive breast cancer. Cancer Med. 2021;10(11):3593–603.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Li W, Gonzalez-Gonzalez M, Sanz-Criado L, Garcia-Carbonero N, Celdran A, Villarejo-Campos P, et al. A novel PiRNA enhances CA19–9 sensitivity for pancreatic cancer identification by liquid biopsy. J Clin Med. 2022;11(24):7310.

  37. Thorlund K, Imberger G, Johnston BC, Walsh M, Awad T, Thabane L, et al. Evolution of heterogeneity (I2) estimates and their 95% confidence intervals in large meta-analyses. PLoS ONE. 2012;7(7):e39471.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ioannidis JP, Patsopoulos NA, Evangelou E. Uncertainty in heterogeneity estimates in meta-analyses. BMJ. 2007;335(7626):914–6.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Review Manager (RevMan) Software5.4. Available from: https://training.cochrane.org/online-learning/core-software/revman. Accessed Aug 2023.

  40. Brüggemann P, Rajguru K. Comprehensive Meta-Analysis (CMA) 3.0: a software review. J Mark Anal. 2022;10(4):425–9.

    Article  Google Scholar 

  41. Han H, Fan G, Song S, Jiang Y, Qian C, Zhang W, et al. piRNA-30473 contributes to tumorigenesis and poor prognosis by regulating m6A RNA methylation in DLBCL. Blood. 2021;137(12):1603–14.

    Article  CAS  PubMed  Google Scholar 

  42. Li G, Yi X, Du S, Gong L, Wu Q, Cai J, et al. Tumour-derived exosomal piR-25783 promotes omental metastasis of ovarian carcinoma by inducing the fibroblast to myofibroblast transition. Oncogene. 2023;42(6):421–33.

    Article  CAS  PubMed  Google Scholar 

  43. Ai L, Mu S, Sun C, Fan F, Yan H, Qin Y, et al. Myeloid-derived suppressor cells endow stem-like qualities to multiple myeloma cells by inducing piRNA-823 expression and DNMT3B activation. Mol Cancer. 2019;18(1):88.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Busch J, Ralla B, Jung M, Wotschofsky Z, Trujillo-Arribas E, Schwabe P, et al. Piwi-interacting RNAs as novel prognostic markers in clear cell renal cell carcinomas. J Exp Clin Cancer Res. 2015;34(1):61.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Bartos M, Siegl F, Kopkova A, Radova L, Oppelt J, Vecera M, et al. Small RNA sequencing identifies PIWI-interacting RNAs deregulated in Glioblastoma-piR-9491 and piR-12488 reduce tumor cell colonies in vitro. Front Oncol. 2021;11:707017.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Zhou X, Liu J, Meng A, Zhang L, Wang M, Fan H, et al. Gastric juice piR-1245: a promising prognostic biomarker for gastric cancer. J Clin Lab Anal. 2020;34(4):e23131.

    Article  CAS  PubMed  Google Scholar 

  47. Martinez VD, Enfield KSS, Rowbotham DA, Lam WL. An atlas of gastric PIWI-interacting RNA transcriptomes and their utility for identifying signatures of gastric cancer recurrence. Gastric Cancer. 2016;19(2):660–5.

    Article  CAS  PubMed  Google Scholar 

  48. Krishnan AR, Korrapati A, Zou AE, Qu Y, Wang XQ, Califano JA, et al. Smoking status regulates a novel panel of PIWI-interacting RNAs in head and neck squamous cell carcinoma. Oral Oncol. 2017;65:68–75.

    Article  CAS  PubMed  Google Scholar 

  49. Saad MA, Ku J, Kuo SZ, Li PX, Zheng H, Yu MA, et al. Identification and characterization of dysregulated P-element induced wimpy testis-interacting RNAs in head and neck squamous cell carcinoma. Oncol Lett. 2019;17(3):2615–22.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Zhao C, Tolkach Y, Schmidt D, Toma M, Muders MH, Kristiansen G, et al. Mitochondrial PIWI-interacting RNAs are novel biomarkers for clear cell renal cell carcinoma. World J Urol. 2019;37(8):1639–47.

    Article  CAS  PubMed  Google Scholar 

  51. Krishnan P, Ghosh S, Graham K, Mackey JR, Kovalchuk O, Damaraju S. Piwi-interacting RNAs and PIWI genes as novel prognostic markers for breast cancer. Oncotarget. 2016;7(25):37944–56.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Cordeiro A, Navarro A, Gaya A, Díaz-Beyá M, Gonzalez-Farré B, Castellano JJ, et al. PiwiRNA-651 as marker of treatment response and survival in classical Hodgkin lymphoma. Oncotarget. 2016;7(29):46002–13.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Weng W, Liu N, Toiyama Y, Kusunoki M, Nagasaka T, Fujiwara T, et al. Novel evidence for a PIWI-interacting RNA (piRNA) as an oncogenic mediator of disease progression, and a potential prognostic biomarker in colorectal cancer. Mol Cancer. 2018;17(1):16.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Firmino N, Martinez VD, Rowbotham DA, Enfield KSS, Bennewith KL, Lam WL. HPV status is associated with altered PIWI-interacting RNA expression pattern in head and neck cancer. Oral Oncol. 2016;55:43–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Zhang W, Zheng Z, Wang K, Mao W, Li X, Wang G, et al. piRNA-1742 promotes renal cell carcinoma malignancy by regulating USP8 stability through binding to hnRNPU and thereby inhibiting MUC12 ubiquitination. Exp Mol Med. 2023;55(6):1258–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Wang Y, Jiang X, Zhang D, Zhao Y, Han X, Zhu L, et al. LncRNA DUXAP8 as a prognostic biomarker for various cancers: a meta-analysis and bioinformatics analysis. Front Genet. 2022;13:907774.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Fu Q, Wang PJ. Mammalian piRNAs: biogenesis, function, and mysteries. Spermatogenesis. 2014;4:e27889.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Rayford KJ, Cooley A, Rumph JT, Arun A, Rachakonda G, Villalta F, et al. piRNAs as modulators of disease pathogenesis. Int J Mol Sci. 2021;22(5):2373.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Liu Y, Dou M, Song X, Dong Y, Liu S, Liu H, et al. The emerging role of the piRNA/piwi complex in cancer. Mol Cancer. 2019;18(1):123.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Ameli Mojarad M, Ameli Mojarad M, Shojaee B, Nazemalhosseini-Mojarad E. piRNA: a promising biomarker in early detection of gastrointestinal cancer. Pathol Res Pract. 2022;230: 153757.

    Article  CAS  PubMed  Google Scholar 

  61. Hanusek K, Poletajew S, Kryst P, Piekiełko-Witkowska A, Bogusławska J. piRNAs and PIWI proteins as diagnostic and prognostic markers of genitourinary cancers. Biomolecules. 2022;12(2):186.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Assumpção CB, Calcagno DQ, Araújo TMT, dos Santos Batista SE, dos Santos Ribeiro ÂKC, Riggins GJ, et al. The role of piRNA and its potential clinical implications in cancer. Epigenomics. 2015;7(6):975–84.

    Article  PubMed  Google Scholar 

  63. Vychytilova-Faltejskova P, Stitkovcova K, Radova L, Sachlova M, Kosarova Z, Slaba K, et al. Circulating PIWI-interacting RNAs piR-5937 and piR-28876 are promising diagnostic biomarkers of colon cancer. Cancer Epidemiol Biomarkers Prev. 2018;27(9):1019–28.

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

This work is part of the Ph.D. project and was supported by a grant awarded by the Tabriz University of Medical Sciences (Grant no. 71187). Daniel Sur was supported by a grant from the Ministry of Research, Innovation and Digitization, CNCS—UEFISCDI, project number PN-III-P1-1.1-PD-2021-0669, within PNCDI III.

Author information

Authors and Affiliations

  1. Department of Molecular Medicine, Faculty of Advanced Medical School, Tabriz University of Medical Sciences, Tabriz, 5166614756, Iran

    Mohammad Taghizadeh & Abbas Karimi

  2. Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, 5166616471, Iran

    Tohid Jafari-Koshki

  3. Cancer Institute of Imam Khomeini Hospital, Tehran University of Medical Science, Tehran, 1419733141, Iran

    Vahid Jafarlou

  4. Hematology and Oncology Research Center, Tabriz University of Medical Sciences, Tabriz, 5166616471, Iran

    Mortaza Raeisi

  5. Gastroenterology and Liver Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, 5166616471, Iran

    Leila Alizadeh

  6. Department of Internal Medicine, School of Medicine, Urmia University of Medical Sciences, Urmia, 5714783734, Iran

    Yousef Roosta

  7. Solid Tumor Research Center, Cellular and Molecular Medicine Research Institute, Urmia University of Medical Sciences, Urmia, 5714783734, Iran

    Yousef Roosta

  8. Hematology, Immune Cell Therapy, and Stem Cells Transplantation Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia, 5714783734, Iran

    Yousef Roosta

  9. Department of Internal Medicine, School of Medicine, Ardabil University of Medical Sciences, Ardabil, 8599156189, Iran

    Somaieh Matin

  10. Department of Oncology, The Oncology Institute “Prof. Dr. Ion Chiricu¸tă”, Cluj-Napoca, 400015, Romania

    Daniel Sur

  11. Department of Medical Oncology, The Oncology Institute “Prof. Dr. Ion Chiricu ̧t ̆a”, 400015 Str. Republicii 34-36, Cluj-Napoca, 400006, Romania

    Daniel Sur

  12. Department of Nutrition Science, Faculty of Medical Science, Urmia University of Medical Science, Urmia, 5714783734, Iran

    Rahele Jabari

  13. Department of Molecular Medicine, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Golgasht St., Tabriz, East Azerbaijan, 5166614756, Iran

    Abbas Karimi

Authors
  1. Mohammad Taghizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tohid Jafari-Koshki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vahid Jafarlou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mortaza Raeisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Leila Alizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yousef Roosta
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Somaieh Matin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Rahele Jabari
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Daniel Sur
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Abbas Karimi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Mohammad Taghizadeh: Conceptualization (equal); data curation (equal); investigation (equal); methodology (equal); project administration (equal); supervision (equal); writing – original draft (equal); writing – review and editing (equal). Abbas Karimi: Conceptualization (equal); investigation (equal); methodology (equal); supervision (equal); writing – original draft (equal); writing – review and editing (equal). Vahid Jafarlou: Data curation (equal); investigation (equal). Tohid Jafari-Koshki: Data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); validation (equal); writing – review and editing (equal). Mortaza Raeisi: Data curation (equal); investigation (equal). Leila Alizadeh: Data curation (equal); investigation (equal). Yousef Roosta: Data curation (equal); investigation (equal)., Somaieh Matin: Data curation (equal); investigation (equal). Rahele Jabari: Data curation (equal); investigation (equal). Daniel Sur: Conceptualization (equal); investigation (equal); methodology (equal); supervision (equal); writing – original draft (equal); writing – review and editing (equal).

Corresponding authors

Correspondence to Daniel Sur or Abbas Karimi.

Ethics declarations

Ethics approval and consent to participate

The systematic review and meta-analysis utilized published data from indexed journals. The analysis was conducted under ethical approval IR.TBZMED.REC.1401.1054.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Taghizadeh, M., Jafari-Koshki, T., Jafarlou, V. et al. The role of piRNAs in predicting and prognosing in cancer: a focus on piRNA-823 (a systematic review and meta-analysis). BMC Cancer 24, 484 (2024). https://doi.org/10.1186/s12885-024-12180-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12885-024-12180-2

Keywords

BMC Cancer

ISSN: 1471-2407

Contact us