Elevated expression patterns of P-element Induced Wimpy Testis (PIWI) transcripts are potential candidate markers for Hepatocellular Carcinoma

Abstract

BACKGROUND:

P-Element-induced wimpy testis (PIWI) proteins, when in combination with PIWI-interacting RNA (piRNA), are engaged in the epigenetic regulation of gene expression in germline cells. Different types of tumour cells have been found to exhibit abnormal expression of piRNA, PIWIL-mRNAs, and proteins. We aimed to determine the mRNA expression profiles of PIWIL1, PIWIL2, PIWIL3, & PIWIL4, in hepatocellular carcinoma patients, and to associate their expression patterns with clinicopathological features.

METHODS:

The expression patterns of PIWIL1, PIWIL2, PIWIL3, PIWIL4 mRNA, was assessed via real-time quantitative polymerase chain reaction (RT-QPCR), on tissue and serum samples from HCC patients, their impact for diagnosis was evaluated by ROC curves, prognostic utility was determined, and In Silico analysis was conducted for predicted variant detection, association with HCC microRNAs and Network Analysis.

RESULTS:

Expression levels were significantly higher in both HCC tissue and serum samples than in their respective controls ( $p<$ 0.001). Additionally, the diagnostic performance was assessed, Risk determination was found to be statistically significant.

CONCLUSION:

PIWIL mRNAs are overexpressed in HCC tissue and serum samples, the expression patterns could be valuable molecular markers for HCC, due to their association with age, tumour grade and pattern. To the best of our knowledge, our study is the first to report the expression levels of all PIWIL mRNA and to suggest their remarkable values as diagnostic and prognostic biomarkers, in addition to their correlation to HCC development. Additionally, a therapeutic opportunity might be also suggested through in silico miRNA prediction for HCC and PIWIL genes through DDX4 and miR-124-3p.

Keywords

Hepatocellular Carcinoma (HCC)PIWIL1 PIWIL2 PIWIL3 PIWIL4 RT-QPCR relative expression patterns diagnosis prognosis

1. Introduction

Liver cancer continues to be a global health problem, with global rates increasing [1, 2]. By 2025, the disease is predicted to affect over 1 million people [3]. Hepatocellular Carcinoma (HCC) is the most common type of primary liver cancer, accounting for around 90% of cases. HCC is seen as a problematic health problem in Egypt for example, with the number of patients doubling over the last decade [4]. Several risk factors are recognised for the development and progression of HCC, but the most prominent ones are viral infections, i.e., Hepatitis B virus (HBV) and hepatitis C virus (HCV). Cirrhosis is also considered at a risk factor developing HCC [3]. HCC aetiology is additionally correlated with mutational changes ascribed from exposure to tobacco and aristolochic acid (AA), and non-alcoholic steatohepatitis (NASH), all of which were determined as probable pathogenetic cofactors in HCC [5]. There is a widely accepted protocol for diagnosing chronic liver disorders (CLD), which involves evaluating liver function using a series of serum-level enzyme assays and a significant tumour marker, $\alpha$ -fetoprotein (AFP); and imaging techniques such as ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI), which have advanced dramatically in recent years. However, AFP has exhibited suboptimal results in terms of therapeutic surveillance and early identification [6]. Subsequently There is a need for more accurate serum biomarkers with increased sensitivity and specificity that complement AFP and improve clinical outcomes for patients.

Recent studies have shown that P-element-induced wimpy testis (PIWI) proteins could be used as markers for their diagnostic and prognostic values [7, 8]. Early Carcinogenesis has been linked to multiple epigenetic abnormal events such as; global hypomethylation of DNA, post-transcriptional changes of histones, dysregulation of noncoding RNAs (ncRNAs), and reactivation of transposable elements (TE) [9, 10, 11, 12, 13, 14]. PiRNAs (P-element induced wimpy testis (PIWI)-interacting RNAs) are short single-stranded ncRNAs, typically 25–33 nucleotides, and interact with PIWI proteins of the Argonaute family. PIWI proteins are involved in the synthesis of piRNAs and assemble ribonucleoproteins known as PiRNA-induced silencing complexes (pi-RISCs) in the cytoplasmic perinuclear foci or ‘nuage’, the mechanism operates at the transcriptional and post-transcriptional stages, and is based on complementarity with short RNA strands (piRNAs, miRNAs, and siRNAs). PIWI proteins and piRNAs were first thought to be implicated in germline and stem cells, with involvements in development, gametogenesis, proliferation differentiation, and maintenance of its integrity and stability via the inhibition of transposable elements’ (TEs) activation [15, 16, 17, 18, 19]. Their roles were also identified for self-renewal, fertilisation, organogenesis and epigenetic activation, expression of genes and proteins, maturation and plasticity of the brain, pancreas functions, and even fat metabolism [20, 21]. Emerging evidence has found their role in carcinogenesis and are related with prominent cancer hallmarks [5]. In humans, the PIWI protein family consists of four proteins: PIWIL1/HIWI, PIWIL2/HILI, PIWIL3, and PIWIL4/HIWI2 [22], belong to the class of cancer/testis antigens (CTAs), and their dysregulation is associated with cancer cell maintenance of proliferative signalling, apoptosis, stemness, genomic integrity, activating invasion, metastasis, mediating genomic instability, and boosting cell growth, to mention a few [23]. The abnormal expression of PIWIs in cancer were first discovered in 2011 [24] and the molecular mechanisms underlying PIWI’s oncogenic actions are controversial. Studies have also supported that PIWI proteins can be utilized cancer prognosis, and in combination with piRNAs they could also be employed for diagnosis [25]. Overexpression of PIWIL1/HIWI gene is seen in various cancers, including seminoma cell hyperplasia [25], oesophageal squamous cell carcinoma, gastric cancer [26] and pancreatic adenocarcinoma [27]. PIWIL2 gene variants transcribed by intragenic promoters, and shorter mRNAs were implicated in various cancers due to their carcinogenic characteristics. Thus, altered PIWI proteins and their variations seen in somatic malignant tumours may serve as diagnostic and prognostic biomarkers and therapeutic targets [28, 29]. Due to inconsistent findings in the literature, we examined the expression levels of the four human members of the PIWI family, both RNA levels by quantitative RT-PCR, in HCC patients ( $n=$ 50) and associated them with their clinicopathological characteristics.

2. Methods

2.1 Patients and sampling

The present study was conducted at Theodor Bilharz Research Institute (TBRI), Egypt. Patients who were diagnosed with HCC by multi-slice triphasic CT and increased alpha fetoprotein levels were selected. Institutional Approval was acquired from the Research Institute Board office (IRB) (NHTMRI-IRB) (Serial: 2-2019), and Theodor Bilharz Research Institute (TBRI-IRB). The research was conducted according to the declaration of Helsinki for human subject research guidelines (2013). Prior to enrolment, all patients and volunteers signed an informed consent form. Participants’ information was collected in strict confidence. 50 patients undergoing liver resection were sampled for tumour and tumour-adjacent samples, the surgery for liver resection was conducted within the department of surgery NHTMRI Hospital, Egypt, matching blood samples were also collected, and blood samples from 25 healthy volunteers were used as controls. Individuals suffering from other liver diseases (e.g., Autoimmune hepatitis, Hemochromatosis, Schistosoma), or diseases such as HIV, and ischemic heart diseases were excluded. Patients with HCV who were taking immunomodulatory interferon therapy were also excluded.

2.2 Sample processing

Blood samples were allowed to clot, centrifuged at 500 xg for 10 minutes, serum was collected, centrifuged, aliquoted, and stored at $-$ 80^∘C. One notable advantage of serum is the simplicity of collection and storage. In addition, serum is commonly used in the diagnostic tests as serological testing and biomarker discovery, making it a familiar and standardized choice for the present research [30]. Liver sections (tumorous and non-tumorous) were stored in lysis buffer at $-$ 80^∘C until use.

2.3 Biochemical parameters

Laboratory tests including alanine aminotransferase (ALT), aspartate aminotransferase (AST), Bilirubin, albumin (ALB) and alpha-fetoprotein (AFP) were performed for all subjects as routine tests upon admission, AFP was also measured by ELISA using a commercially available kit (ABCAM, AB79801, Cambridge, UK).

2.4 RNA extraction and cDNA synthesis

Total RNA was extracted using the miRNeasy extraction kit (Qiagen, Valencia, CA) was used to according to the manufacturer’s instructions for both tissue and serum samples. Samples were extracted in duplicates, then the quality and concentration of the samples were measured using a NanoDrop-1000c spectrophotometer (Thermo-Fisher Scientific, Cinisello Balsamo, Italy). For transcription of the mRNA samples into cDNA The QuantiTect Reverse Transcription Kit (Qiagen, Valencia, CA), was used according the manufacturer’s instructions, with 1 $\mu$ g of total RNA used.

2.5 Real-time quantitative polymerase chain reaction (RT-QPCR)

The selected primers included the four isoforms PIWIL1/HIWI; PIWIL2/HILI; PIWIL3, PIWIL4 and all assays were acquired from Qiagen, and were performed according to the manufacturer’s instructors. Quantitative values were respective to the Cycle number (C_t Value), where fluorescence was directly proportional to growth of PCR products, this was performed by QuantiTect SYBR Green PCR Kits (Qiagen, Valencia, CA). Glyceraldehyde 3-phosphate dehydrogenase, (GAPDH), was also used, as an endogenous control because of its transcripts’ prevalence, to normalize each sample. All reactions were run in duplicates. Finally, the $\Delta\Delta$ CT method was used for the relative quantification of mRNAs in all samples [31], where the $\Delta$ Ct of the sample was determined by subtracting the average Ct value of the target gene from the average Ct value of the housekeeping gene, results of 3 or higher were considered overexpression, and $<$ 1 was considered downregulation.

2.6 In Silico variant detection for PIWIL genes and Network Analysis

Hepatocellular Carcinoma primary cancer database ( $n=$ 1273) used for detection of possible mutation variants analysis for PIWIL1, PIWIL2, PIWIL3, PIWIL4 from the publicly available database cBioPortal for Cancer Genomics and the cancer genome atlas project (https://www.cbioportal.org); (https://portal.gdc.cancer. gov) to predict most prevalent variants implicated in HCC, mIR- GeneMANIA (http://genemania.org) is an online resource that gives extensive information regarding gene, protein, and pathway interactions, among others. We compared the connection between DEGs in PIWIL genes and neighbouring related genes using GeneMANIA. In addition, functional annotation and enrichment analysis based on Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes, and Metascape (http://metascape.org/gp/index.html#/main/step1) were performed (KEGG). The study was conducted using a threshold value of 0.01, a minimum overlap of 3, and an enrichment of 1.5. From the PPI data source, all protein-protein interactions between input genes were removed to construct a PPI network. The network was subjected to GO enrichment analysis for biological significance. The MCODE algorithm was then applied to this network to find linked proteins [32].

2.7 Statistical

All statistical analyses were performed using statistical software SPSS (Statistical Package for Social Science) statistical program version 21.0. A Power test, indicated that the standard deviation of control is 0.8 and the standard deviation for the regression errors will be 1.9. Regression was at 1.1, and that 50 study subjects and 25 normal controls will be an appropriate representation via regression with a probability of 85%, and Type I error was 0.05. as adapted from HCC molecular marker research [33]. Normality tests determined continuous variables, described as mean $\pm$ standard deviation (SD), or median and interquartile range (IQR) according to their distribution. Frequencies and percentages were used for categorical variables. A $p$ value of $<$ 0.05 was considered statistically significant. Mann-Whitney U was used, to compare means, Fisher’s exact test was used to determine the distribution of categorical variables between groups. Receiver operating characteristic (ROC) curve was used to generate the Cut-off values, area under the curve (AUC), sensitivity, and specificity for diagnostic potential. Odd ratios (OR), and 95% confidence intervals (CIs) were calculated by logistic regression to assess the relative risk for PIWIL mRNA.

Table 1
Demographics and Clinico-pathological characteristics for HCC patients

Clinico-pathological characteristics	Total number of patients $N=$ 50 (%)
Age (Mean $\pm$ SD)	57.2 $\pm$ 8.1
Sex
Female	22 (44.0)
Male	28 (56.0)
ALT
7–55 U/L	61.4 $\pm$ 15.5
AST
8–33 U/L	65.8 $\pm$ 16.6
Alb
3.4–5.4 g/dL	2.3 $\pm$ 1.0
Bilirubin
0.3–1.2 mg/dL	2.9 $\pm$ 1.1
AFP
0 ng/mL–40 ng/mL	75.0 (40.0–150.0)
$<$ 400 ng/mL
S. Creatinine
M: 0.74–1.35 mg/dL	0.9 $\pm$ 0.4
F: 0.59–1.04 mg/dL
No. of masses	1.1 $\pm$ 0.2
Tumour size	2.25 (0.75–4.25)
Tumour grade
I	29 (38.7)
II	11 (14.7)
III	10 (13.3)
Pattern
Acinar	30 (60.0)
Solid	17 (34.0)
Acinar/Solid	3 (6.0)
Steatosis	0.02 (0.02–0.04)
Stage
Fibrosis	8 (16.0)
Cirrhosis	42 (84.0)
HAI
A1	15 (30.0)
A2	35 (70.0)
A3	0 (0.0)
Hepatomegaly
Negative	41 (82.0)
Positive	9 (18.0)
Ascites
Negative	30 (60.0)
Positive	20 (40.0)
Splenomegaly
Negative	21 (42.0)
Positive	29 (58.0)
Oedema lower limbs
Negative	37 (74.0)
Positive	13 (26.0)

Alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (Alb) and alpha-fetoprotein (AFP). No. of masses are represented as Mean and SD. But Alpha feto-protein, Tumour size, and Steatosis (Fatty degeneration of hepatocytes (% of cells)) are represented as Median and Interquartile Range IQR (25%–75%). While Sex, Grade, Pattern, Stage, HAI (Hepatitis Activity Index (grade of hepatitis), Hepatomegaly, Ascites, Splenomegaly, and oedema Lower Limbs are represented as Frequency and percent.

Figure 1.

Relative expression representation for PIWIL mRNA in HCC ( $N=$ 50), where: A) PIWIL1 mRNA in Serum, B) PIWIL1 mRNA in Tissue, C) PIWIL2 mRNA in Serum, D) PIWIL2 mRNA in Tissue, E) PIWIL3 mRNA in Serum, F) PIWIL3 mRNA in Tissue, G) PIWIL4 mRNA in Serum, & H) PIWIL4 mRNA in Tissue.

3. Results

3.1 Patient characteristics

A total of 50 HCC participants were recruited in this study, the patients included 28 males (56%) and 22 females (44%), with a mean age of 57.2 $\pm$ 8.1 and 25 healthy individuals with no history of liver disease or alcohol consumption were included as controls. Individual demographic and clinical data of the studied groups are shown in (Table 1). The results for biochemical parameters were represented as mean $\pm$ SD for these tests, and had values of 61.4 $\pm$ 15.5, 65.8 $\pm$ 16.6, 2.3 $\pm$ 1.0, 2.9 $\pm$ 1.1, 0.9 $\pm$ 0.4 respectively, AFP however was 75.0 (40.0–150.0). By computed tomography, the number of tumour masses was detected with mean of 1.1 $\pm$ 0.2, while tumour size and steatosis were found at 2.25 (0.75–4.25) and 0.02 (0.02–0.04) respectively. Regarding pathological diagnosis, 29 (38.7%) were diagnosed GI, 11 (14.7%) were with GII and 10 (13.3%) were GIII, 30 (60%) were with acinar pattern tumour, 17 (34%) with solid tumour and only 3 patients (6%) were with acinar/solid tumour. For tumour staging 8 patients (16%) were fibrotic with the predominant number for the cirrhotic patients 42 (84%). Histopathological examination was performed using METAVIR scoring system to determine the hepatitis activity index (HAI),

Table 2
Clinico-pathological characteristics for HCC patients and their association with PIWIL mRNA expression

Clinico-pathological parameters	No. of cases $N=$ 50 (%)	Tissue expression				Serum expression
		PIWIL1	PIWIL2	PIWIL3	PIWIL4	PIWIL1	PIWIL2	PIWIL3	PIWIL4
Tumour Grade		0.2	0.7	0.4	0.2	0.8	0.9	0.9	0.2
I	29 (38.7)	6.2 (4.4–13.4)	6.8 (3.5–25.8)	6.0 (3.5–9.0)	1.7 (0.8–2.9)	71.0 (18.2–100.3)	29.6 (24.8–41.6)	55.3 (29.0–122.5)	32.7 (17.0–97.8)
		$P<$ 0.05^*	0.9	0.5	0.5	0.1	0.6	0.9	0.5
II	11 (14.7)	28.0 (5.9–36.5)	6.4 (4.6–9.9)	9.0 (3.0–20.0)	1.0 (0.6–7.8)	60.1 (18.2–120.2)	29.6 (16.8–37.5)	55.3 (36.0–170.0)	22.8 (11.8–39.7)
		$P<$ 0.08	0.9	0.9	$P<$ 0.04^*	0.2	0.7	0.3	0.2
III	10 (13.3)	10.1 (5.8–30.0)	6.2 (4.6–8.3)	7.0 (2.8–20.0)	0.7 (0.4–2.6)	100.3 (49.3–128.2)	30.1 (22.9–39.5)	189.7 (32.5–236.3)	39.7 (15.7–182.9)
Pattern		0.3	0.6	1	0.5	0.6	0.3	0.6	0.7
Acinar	30 (60.0)	7.9 (5.1–13.5)	6.4 (3.8–25.8)	6.0 (3.0–11.8)	1.7 (0.6–2.2)	65.6 (18.2–90.4)	30.1 (24.9–39.5)	55.3 (37.5–179.8)	34.9 (17.0–84.9)
		0.3	0.3	0.5	0.4	0.7	0.5	0.9	0.8
Solid	17 (34.0)	7.9 (4.1–28.8)	6.1 (4.1–6.7)	9.0 (3.0–20.0)	1.0 (0.5–5.9)	71.5 (18.2–152.2)	29.6 (8.9–35.3)	55.3 (22.0–189.7)	22.8 (11.8–55.9)
		0.4	$P<$ 0.03^*	0.2	0.2	0.6	0.1	0.3	0.1
Acinar/Solid	3 (6.0)	31.3 (6.2–31.3)	10.9 (9.9–10.9)	20.0 (7.0–20.0)	6.8 (1.2–6.8)	80.4 (3.1–80.4)	37.5 (0.9–37.5)	75.0 (22.0–75.0)	32.7 (11.8–32.7)
Stage
Fibrosis	8 (16.0)	0.7	0.5	0.4	0.8	$P<$ 0.02^*	0.8	0.7	0.9
Cirrhosis	42 (84.0)	7.9 (5.3–12.9)	8.3 (3.5–25.8)	6.0 (3.3–9.0)	1.9 (0.5–3.5)	8.0 (4.9–28.0)	6.2 (4.2–14.1)	8.0 (3.0–20.0)	1.4 (0.6–3.1)

The studied genes are represented as Median and Interquartile Range IQR (25%–75%); the data were analysed by Mann-Whitney U test. ^{*p <} 0.05, ^**p< 0.001. Grade, Pattern, Stage, Hepatomegaly, Ascites, Splenomegaly, and oedema Lower Limbs are represented as Frequency and percent.

Table 3

Clinico-pathological characteristics for HCC patients and their correlation with PIWIL mRNA expression

Clinico-pathological carameters	No. of cases $N=$ 50 (%)	Serum								Tissue
		PIWIL1		PIWIL2		PIWIL3		PIWIL4		PIWIL1		PIWIL2		PIWIL3		PIWIL4
		$r$	$p$ . value	$r$	$p$ . value	$r$	$p$ . value	$r$	$p$ . value	$r$	$p$ . value	$r$	$p$ . value	$r$	$p$ . value	$r$	$p$ . value
Age (Mean $\pm$ SD)	57.2 $\pm$ 8.1	0.178	0.514	$-$ 0.289	0.042^*	0.35	0.41	0.132	0.204	0.934	0.01^*	0.89	0.031^*	0.93	0.615	0.385	0.006^*
Sex
Female	22 (44.0)	0.031	0.15	0.047	0.741	0.207	0.173	0.027	0.852	0.172	0.233	0.142	0.441	0.155	0.427	0.085	0.061
Male	28 (56.0)	0.053	0.24	0.072	0.35	$-$ 0.253	0.106	0.099	0.506	$-$ 0.173	0.245	$-$ 0.074	0.631	$-$ 0.129	0.387	$-$ 0.093	0.545
ALT
7–55 U/L	61.4 $\pm$ 15.5	0.03	0.838	$-$ 0.158	0.274	$-$ 0.078	0.611	0.087	0.546	$-$ 0.04	0.784	$-$ 0.02	0.891	$-$ 0.051	0.725	$-$ 0.085	0.558
AST
8–33 U/L	65.8 $\pm$ 16.6	0.015	0.917	$-$ 0.118	0.414	$-$ 0.118	0.439	0.022	0.88	$-$ 0.135	0.348	$-$ 0.058	0.688	$-$ 0.179	0.213	$-$ 0.193	0.179
Alb
3.4–5.4 g/dL	2.3 $\pm$ 1.0	0.048	0.742	0.076	0.601	0.049	0.748	$-$ 0.061	0.672	0.091	0.528	0.097	0.503	0.088	0.542	0.151	0.296
Bilirubin
0.3–1.2 mg/dL	2.9 $\pm$ 1.1	0.031	0.829	0.047	0.745	0.207	0.173	0.027	0.852	0.172	0.233	0.142	0.324	0.115	0.428	0.165	0.252
AFP
0 ng/mL–40 ng/mL	75.0 (40.0–150.0)	$-$ 0.057	0.703	$-$ 0.027	0.86	$-$ 0.253	0.106	0.099	0.506	$-$ 0.173	0.245	$-$ 0.074	0.623	$-$ 0.129	0.387	$-$ 0.093	0.534
$<$ 400 ng/mL
S. Creatinine
M: 0.74–1.35 mg/dL	0.9 $\pm$ 0.4	$-$ 0.078	0.591	$-$ 0.072	0.62	0.037	0.81	0.183	0.204	0.034	0.814	0.03	0.835	$-$ 0.073	0.616	$-$ 0.134	0.352
F: 0.59–1.04 mg/dL
No. of masses	1.1 $\pm$ 0.2	$-$ 0.043	0.767	$-$ 0.102	0.481	$-$ 0.124	0.415	0.136	0.348	$-$ 0.131	0.366	$-$ 0.022	0.879	$-$ 0.128	0.377	$-$ 0.081	0.578
Tumour size	2.25 (0.75–4.25)	$-$ 0.043	0.782	$-$ 0.091	0.556	$-$ 0.067	0.685	0.064	0.682	$-$ 0.143	0.354	$-$ 0.126	0.416	$-$ 0.166	0.281	$-$ 0.205	0.182
Steatosis	0.02 (0.02–0.04)	$-$ 0.158	0.273	$-$ 0.028	0.848	$-$ 0.139	0.336	$-$ 0.189	0.189	$-$ 0.058	0.69	$-$ 0.049	0.738	$-$ 0.085	0.559	$-$ 0.086	0.554

Alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (Alb) and alpha-fetoprotein (AFP). No. of masses are represented as Mean and SD. But Alpha feto-protein, Tumour size, and Steatosis (Fatty degeneration of hepatocytes (% of cells)) are represented as Median and Interquartile Range IQR (25%–75%). Sex, represented as Frequency and percent $r=$ Pearson Correlation (2-tailed); ^{*p <} 0.05, ^**p< 0.001.

15 (30%) patients were rated as AI, 35 (70%) were A2 while none of the patients were A3. Abdominal ultrasounds detected hepatomegaly in 9 (18%) patients, ascites in 20 (40%) patients and splenomegaly in 29 (58%) patients and finally 13 (26%) out of the 50 HCC patients were detected with oedema lower limbs (Table 1).

Figure 2.

ROC curves of the studied PIWIL mRNA in (a) serum and (b) tissue samples. (C & D) are combined curves for serum and tissue, respectively. (E) ROC curve for AFP measured by ELISA in serum samples of HCC patients. Graphs depict the diagnostic performance of all PIWIL mRNAs in terms of specificity & sensitivity.

Table 4

ROC curve analysis for diagnostic performance for each of the studied PIWIL mRNAs in serum and tissue samples

	Groups	Cut-off	Sensitivity	Specificity	AUC	95% C.I		$p$ value
						Lower bound	Upper bound
Serum	PIWIL1	$<$ 1.6	100.0	100.0	1.0	1.0	1.0	$<$ 0.001^**
	PIWIL2	$<$ 0.56	100.0	100.0	1.0	1.0	1.0	$<$ 0.001^**
	PIWIL3	$<$ 8.87	100.0	100.0	1.0	1.0	1.0	$<$ 0.001^**
	PIWIL4	$<$ 1.02	100.0	100.0	1.0	1.0	1.0	$<$ 0.001^**
	ELISA/AFP	$>$ 0.97	84.0	64.0	0.828	0.746	0.910	$<$ 0.001^**
	Serum	Combined	100.0	100.0	1.0	1.0	1.0	$<$ 0.001^**
Tissue	PIWIL1	$<$ 4.76	80.0	72.0	0.80	0.71	0.89	$<$ 0.001^**
	PIWIL2	$<$ 3.69	84.0	64.0	0.80	0.71	0.89	$<$ 0.001^**
	PIWIL3	$<$ 4.5	68.0	84.0	0.79	0.70	0.88	$<$ 0.001^**
	PIWIL4	$<$ 0.94	64.0	68.0	0.66	0.56	0.77	$<$ 0.01^*
	Tissue	Combined	70.7	68.0	0.733	0.683	0.783	$<$ 0.001^**

Sn: Sensitivity, Sp: Specificity, AUC Area under curve and C.I: 95% Confidence Interval. ^{* p<} 0.05, ^** p< 0.001.

3.2 Expression of PIWIL mRNA transcripts in serum & tissue

RT-QPCR results showed mRNA levels were upregulated for PIWIL1, PIWIL2, PIWIL3 and PIWIL4 in serum & tissue samples at ( $p<$ 0.001), and ( $p<$ 0.01) (Fig. 1), expression for PIWIL2 was also confirmed via immunohistochemistry and ELISA as a preliminary approach and this confirmed the results of the mRNA expression data (Supplementary Figs 1 and 2, and Supplementary Table 1).

3.3 Association of clinicopathological features with the expression PIWIL mRNA transcripts

Tumour grades of patients were associated with expression of PIWIL1-4. No association was found for serum, but for tissue samples, a significant association for PIWIL1 ( $p<$ 0.05) and PIWIL4, ( $p<$ 0.05) (Supplementary Table 1). For tumour pattern and PIWIL mRNA, no significant difference was detected in serum, and PIWIL2 mRNA expression was significantly associated ( $p<$ 0.05) in Tissue. For Tumour stages PIWIL1 mRNA expression was significantly associated ( $p<$ 0.05) in serum samples, and no association was determined for tissue. Other investigated parameters hepatomegaly, ascites, splenomegaly, oedema lower limbs, had no significant difference (Table 2). Pearson correlation was used for Age, Sex, and the biochemical assessments AST, ALT, Alb, Bilirubin, AFP, S-creatinine, in addition to the number of tumour masses, tumour size, and steatosis. Age was found to be significantly correlated with tissue expression for PIWIL1, and PIWIL4 ( $p<$ 0.05), no significant difference was observed for other parameters (Table 3).

Table 5
HCC risk results of PIWIL mRNA in HCC patients

HCC risk determination (logistic regression)	Serum groups				Tissue groups
	PIWIL1	PIWIL2	PIWIL3	PIWIL4	PIWIL1	PIWIL2	PIWIL3	PIWIL4
OR (95% C.I)	3.87 (1.23–8.36)	4.21 (1.04–7.23)	3.26 (1.14–8.21)	2.99 (1.57–9.23)	2.35 (0.17–5.32)	2.21 (0.15–4.24)	2.46 (0.23–6.27)	1.65 (0.19–3.14)
$p$ value	$<$ 0.001^**	$<$ 0.001^**	$<$ 0.001^**	$<$ 0.001^**	0.01^*	0.01^*	0.01^*	0.01^*

OR: Odds Ratio; C.I: Confidence Interval $p<$ 0.05, ^** p< 0.001.

3.4 Diagnostic performance

Receiver Operating characteristic (ROC) analysis of PIWIL1, PIWIL2, PIWIL3 and PIWIL4 in both serum, and tissue samples of HCC patients. They showed a sensitivity of 100%, specificity of 100%, area under curve (AUC) of 1.00 ( $p<$ 0.001, 95% C.I: 1.0–1.0) for serum. While In HCC tissue, PIWIL1 was with sensitivity of 80.0%, specificity of 72.0%, and an area under curve (AUC) of 0.80 ( $p<$ 0.001, 95% C.I: 0.71–0.89). For PIWIL2, it was with sensitivity of 84.0% and specificity of 64.0% with AUC of 0.80 ( $p<$ 0.001, 95% C.I: 0.71–0.89). While for PIWIL3, it was with sensitivity of 68.0% and specificity of 84.0% with AUC of 0.79 ( $p<$ 0.001, 95% C.I: 0.70–0.88) and finally statistical significance was observed for PIWIL4, with sensitivity of 64.0% and specificity of 68.0% with AUC of 0.66 ( $p<$ 0.01, 95% C.I: 0.56–0.77), indicating that PIWIL mRNA are overexpressed in tumorous liver tissue samples (Table 4; Fig. 2). A combined ROC curve was prepared to show an inclusive diagnostic performance, serum had an overall sensitivity and specificity of 100%, and an AUC of 1.0. Tissue samples exhibited sensitivity of 70.70%, specificity of 68%, and AUC of 0.73. Both of which were significant $p<$ 0.001. AFP was measured using ELISA, the values had sensitivity of 84%, Specificity of 64% with AUC of 0.83 ( $p<$ 0.001).

3.5 Logistic regression analysis of PIWIL’s

To assess the relative risk of HCC presented from PIWIL mRNA, logistic regression analysis model was performed (Table 5). It revealed that PIWIL1-4 is significantly associated with increased risk for HCC in serum ( $p<$ 0.001) of HCC patients, and in tissue samples of patients ( $p<$ 0.05).

3.6 In Silico analysis for PIWIL genes

In Fig. 3B the role of PIWIL genes in HCC progression is indicated, where the highest implication being for PIWIL2 at 4% structural variant detection with an association for HCC; Fig. 3B shows the frequency of PIWI mutations according to HCC types; miRNet prediction was also employed for HCC association with PIWIL genes (Fig. 3A) and a direct association was detected between PIWIL2, PIWIL4 and MiR-124-3p.1, a known potential tumour suppressor associated with diverse processes including proliferation, apoptosis, and metastasis. The Frequency of PIWIL mutations were caried out through TCGA; via BioPortal (Fig. 3C), the results indicated that the highest frequency for reported mutations were for PIWIL2 at 4%, and the type of reported alterations were deep deleted deletions, remaining PIWIL variants had 1.4% which also had missense mutations as alterations. We identified the top 24 neighbouring genes with the highest frequency association with differential expressed PIWIL targets. The functions of these related were predicted using Metascape. The top 29 GO enrichment were described in (Fig. 4), which mainly included gene silencing by RNA, lncRNA, siRNA biogenesis and RISC complex assembly; Pathway enrichment analysis represented pathways a strong associated between PIWIL2, PIWIL4 and DDX4, DEAD-box helicase 4 (DDX4) and DDX39 was previously found to be upregulated in HCC, and that knocking down the DDX4 significantly decreased tumour formation in vivo and in vitro, as well as reduces tumour metastasis in vivo [34, 35] (Fig. 4F); indicating a relationship with HCC progression. The PPI network and MCODE components are shown in (Fig. 4C–F).

4. Discussion

PIWIL1, PIWIL2, PIWIL3 and PIWIL4, are designated as catalytic elements of the pi-RISCs complexes, the have implications in piRNAs’ biogenesis on the basis of complementarity [35, 36, 37]. Their roles vary at transcriptional and post-transcriptional epigenetic regulation. PIWIL1-2-3-4 RNAs were strongly expressed in HCC tissue, and circulating sera compared to the controls. The unexpected role of the PIWI-piRNA pathway has led to distinct functions of human PIWI proteins and mRNA in various cancer types [38, 39, 40]. Their involvement in multiple cancer hallmarks, has led to a possible representation as diagnostic and prognostic biomarkers [41, 42]. There are conflicting statements on the expression patterns of PIWIL1/PIWIL2/PIWIL3/PIWIL4, their prognostic, and predictive values, in addition to a complete absence for hepatocellular carcinoma in terms of mRNA transcript accumulation, and associated piRNAs, with recent studies on identifying novel piRNAs [43, 44, 45].

In terms of demographic data for the patients of the current study, the mean age was 57.2 $\pm$ 8.1, 44% of which are females and 56% males, typical for the risk factors associated with HCC (Table 1) and all typical parameters are represented within the table. We found a strong correlation between age and tissue expression for PIWIL1, 2, and 4, but not PIWIL3, and other clinical data HAI, hepatomegaly, ascites, splenomegaly, oedema lower limbs, number of masses was not significant. The aforementioned findings were in alignment with Taubert et al. and Al-Janab et al. who observed similar outcomes for no association with clinicopathological features apart from age for colorectal cancer and renal cell carcinoma respectively [46, 47], but this was refuted by Zhang et al. and Li et al. in breast and colorectal cancer respectively [41, 48]. For tumour grade, PIWIL1

Figure 3.

(A) miRNet prediction of PIWIL and HCC. (B) Reported cases with mutated PWIL genes in Liver cancer (C) Frequency of mutated PIWIL genes and their predicted variants’ implication for HCC through TCGA; via BioPortal for Cancer Genomics (https://www.cbioportal.org).

Figure 4.

(A) Genemania generated Network for PIWIL1, PIWIL2, PIWIL3, PIWIL4 and function predictions (B) Metascape results for Enriched ontology clusters. (C) PIWIL Subset network with force-directed clustering (D) Enriched network according to statistical significance, the darker the color, the more statistically significant the node is. (E) protein-protein interactions’ network from PIWIL genes. (F) Results for MCODE algorithm to identify neighborhoods where proteins are densely connected.

& PIWIL4 had significant difference with tissue expression, which was confirmed in a study by Litwin et al. for PIWIL1, & PIWIL2 in breast cancer [49].

Recently, several reports have indicated that aberrant expression of PIWI at the mRNA and protein levels occurs in various types of tumours [50, 51]. PIWIL1/HIWI was previously linked with several types of cancers, with a pattern of overexpression, moreover, it was correlated with tumour grading and staging [51, 52]. Our findings confirmed the apparent role of PIWIL mRNAs for HCC, with a pattern observed of overexpression for all PIWIL isoforms. Previously, both PIWIL1 & PIWIL2 was identified as overly expressed in colorectal, prostate, breast, cervical, gastric and bladder cancer [51], PIWIL1, 2 & 4 was observed as downregulated in renal cell carcinoma [50]. Meseure et al. conducted an investigation on the bio-pathological significance of the PIWI-PiRNA pathway through PIWIL1-2-3-4 mRNA expression levels in a panel of normal tissues and corresponding malignant tumours, and detected variable levels of expression across malignancies [53]. In addition, the relative expression of PIWIL2 mRNA was previously found to be higher in HCC tissues compared with adjacent normal liver tissues. A positive correlation was found between PIWIL2 expression and piR-Hep1 level according to Pearson’s correlation analysis [37, 38]. PIWIL2 acts as an oncogene by activating the STAT3/Bcl-xl cell signalling pathway through endogenous RNAi mechanism, hence inhibiting cell apoptosis and promoting cell proliferation. To the best of our knowledge, our study is the first to report the expression levels of all PIWIL mRNAs in serum and tissue, and to suggest their possible roles as diagnostic and prognostic biomarkers, in addition to their correlation to HCC development.

The involvement of PIWI proteins was linked with multiple hallmarks of cancer including invasion, apoptosis evasion, metastasis and cell proliferation, as such they possess prospective diagnostic factors and biomarkers for cancer prognosis [25, 54]. Significant increased level of PIWIL1 was reported for colon, bladder, and hepatocellular carcinoma. Expression of the four members of the PIWI proteins was viewed as distinct in tumour tissue when compared with the adjacent non-tumorous tissue [22, 55]. PIWIL3 and PIWIL1 were assessed for relative expression levels by [56] in colorectal cancer, and had non-significant expression statistically. Among all PIWIL genes, those assessed for expression and correlated with overall survival and recurrence-free survival were PIWIL3 & PIWIL4, in invasive urothelial bladder cancer [49]. Erber et al. reported a limitation of their study was not assessing the expression of PIWIL1 and PIWIL2 at the mRNA level, and depending on Immunohistochemistry, but they reported higher expression levels [57]. In the current study, expression of all PIWIL was assessed using real-time PCR in HCC patients. The levels of mRNA transcripts expression are reported for the first time for PIWIL1, PIWIL2, PIWIL3, PIWIL4 in tumour and nontumorous adjacent tissue, and matching serum samples HCC patients and healthy controls, the expression was correlated with clinical data. When compared to adjacent non-cancerous tissues, we found a significantly elevated expression for PIWIL1, PIWIL2, PIWIL3, &, PIWIL4 in HCC samples ( $p<$ 0.001).

These findings are consistent with prior findings in colorectal cancer, in which PIWIL1 mRNA levels in non-cancerous tissue were low or undetectable, but were dramatically raised in malignant tissue [58]. Additionally, PIWIL1/HIWI was reported to have marked expression levels in HCC tissue, for patients who had undergone curative resection [42]. In breast cancer, PIWIL1 and PIWIL3 gene expressions were reported to be upregulated, whereas PIWIL2 and PIWIL4 were downregulated compared with normal breast tissue [53]. It is noteworthy to state that our research detected significant association between PIWIL1 & PIWIL4 expression with increasing tumour grade ( $p<$ 0.05). PIWIL1 mRNA expression in serum was associated with tumour stage ( $p<$ 0.05). PIWIL2 expression in tissue was associated with tumour pattern ( $p<$ 0.05). These findings are similar to those of previous studies, which found aberrant mRNA expression in the varying stages of multiple cancer types, inferring the role of PIWIL-mRNAs in cancer development [54, 55, 51, 59].

In terms of diagnostic performance, our findings showed that serum had an overall sensitivity and specificity of 100%, and an AUC of 1.0, in comparison to normal serum, which shows an indication of disease prevalence but should be adapted for clinical settings i.e., patients with chronic liver diseases. Tissue samples exhibited sensitivity of 70%, specificity of 68%, and AUC of 0.733. Both of which were significant $p<$ 0.001. Referring to impact on survival, PIWIL1, PIWIL4, PIWIL2 were reported for survival data, but not PIWIL3 (Krishnan et al., 2016). Research on the prognostic implications of PIWIL and PiRNAs was determined with higher sensitivity e.g., 83.3% sensitivity and 89.3% specificity for colorectal cancer (CRC) tissue expression in CRC patients [60, 61, 62, 63]. To assess the HCC risk level logistic regression was done and had a statistical significance ( $p<$ 0.001) in serum, and ( $p<$ 0.05) in tissue, these results are in agreement with Mai et al. for CRC cases diagnosed over the course of 3 years at 7.23, 2.80, 2.45, and 1.24, respectively [61] and similar findings were also reported [59]. Tosun et al. evaluated the predictive value of serum expression level of PIWIL2 mRNA and proteins in prostate cancer, with a strong correlation [64]. The reactivation of PIWI expression in cancer clearly suggests that these proteins are involved in the growth and differentiation of tumours [65]. According to Table 2, there was no clear correlation between the values of AFP and the expression of the PIWIL at the mRNA level for tissue and serum, the literature indicates that, serum AFP has a specificity of 76%–94%, and a sensitivity of 39%–65% for HCC [66]. Our ROC curve for ELISA/AFP was significant ( $p<$ 0.001), with sensitivity of 84%, Specificity of 64% (Table 4 and Fig. 2E), indicating better performance for PIWIL mRNA, in spite of a lack of disease specificity, and suggesting a panel use. Disruptions in PIWI-piRNAs pathway regulation has an effect on biological processes involving cancer progression, they comprise apoptosis, migration, proliferation and metastasis, which represents an indication that PIWI proteins and piRNAs can be used as diagnostic biomarkers, or novel therapeutic targets for the treatment of hepatocellular carcinoma [65], and that manipulation of gene expression for PIWI and PiRNAs could lead to more pronounced management in cancer progression, and an enhanced patient recovery [67].

PIWIL genes have several transcripts, some of which appear to be transcribed by putative intragenic promoters rather than a canonical promoter, which was associated with tumorigenesis [65]. PIWIL expression was revealed to have a direct predicted influence on HCC progression, through PIWIL2, and PIWIL4, this was found through two novel associations DDX4 (Fig. 4E), and miR-124-3p.1 (Fig. 3A), Recent studies have shown that downregulation of miR-124-3p.1 was associated with poor survival, early recurrence and sorafenib sensitivity in HCC patients [42], and our findings have demonstrated a novel direct interplay between PIWIL2, PIWIL4 and miR-124-3p.1, the pathway additionally indicates a several target miRNAs which could be used as possible therapeutic targets for PIWIL2 miR-33b-3p and miR-519d-3p were identified, while for PIWIL4, miR-129-2-3p, miR-200b-3p, and miR-212-3p were found, all of which are related to miR-124-3p.1, a microRNA which when upregulated, negative affects proliferation and migration in hepatocellular carcinoma via a phosphoinositide 3-kinase catalytic subunit alpha (PIK3CA) pathway [68]. and miR-101-3p was additionally detected for PIWIL4, and was reported to negatively influence HCC proliferation and metastasis through the HGF/c-Met pathway [70]. Thus, aberrant expression of PIWIL2 and PIWIL4 is a possible mechanism of tumour suppressor inactivation Fig. 3A, and the associated miRNAs could represent attractive therapeutic targets for combined therapies where a specific antibody for PIWIL2 or the PIWI/miRNA RISC complex could be targeted [71]. PIWIL expression is also shown have a direct predicted influence on HCC progression, through PIWIL2, and PIWIL4, through association with DDX4 (Fig. 4). Studies have shown that elevated levels of DDX4, indicates its ability to promote the stemness of breast cancer stem cells by regulating the expression of proteins such as Oct3/4 and Sox-2 and promoting disease progression [72]. In this regard, its upregulation or overexpression promotes proliferation, suggesting an oncogenic role, its association through the PPI network (Fig. 4), shows a novel interaction with PIWIL2 and PIWIL4 and further analysis into PIWIL2 and PIWIL4 expression and silencing is recommended.

5. Conclusion

Finally, PIWI mRNA, PIWI proteins, and piRNAs were identified as germline markers. DNA methylation, histone methylation, histone acetylation, and histone ubiquitination not only play significant transcriptional regulatory roles, but PIWI family proteins can break mRNA under the supervision of piRNA, suggesting a post-transcriptional regulatory function. However, the method of control of PIWI/piRNAs in cancer appears to be unique. Most studies have established that PIWI mRNA, proteins, and piRNA appear to govern tumours as two distinct entities rather than as a unified entity. As a result, it is critical to investigate how the PIWI protein regulates tumours independently of piRNA. The specific molecular biological mechanism behind the effect of PIWIL on the occurrence, progression, and prognosis of HCC is currently unknown and deserves additional investigation. Our findings provide a unique viewpoint on the activities of PIWIL at the mRNA level in HCC development, as well as a different pattern of overexpression that provides potential candidates for HCC disease progression and risk assessment. PIWIL mRNAs are overexpressed in HCC tissue and serum samples, the expression patterns could be valuable molecular markers for HCC, due to their association with age, tumour grade and pattern. To the best of our knowledge, our study is the first to report the expression levels of all PIWIL mRNA and to suggest their remarkable values as diagnostic and prognostic biomarkers, in addition to their correlation to HCC development. Additionally, a therapeutic opportunity might be also suggested through in silico miRNA prediction for HCC and PIWIL genes through DDX4 and miR-124-3p. The epigenetic regulation of the identified changes in PIWIL1, PIWIL2, PIWIL3, and PIWIL4 at the transcriptional and protein levels warrants additional investigation, which could have significant clinical relevance, and should be examined for therapeutic roles. A large sample size investigation will aid in analysing the link between PIWIL expression/co-expression and HCC prognosis. This can serve as the preliminary foundation for PIWIL as molecular markers of early-stage diagnostic and prognostic evaluation, as well as targeted cancer therapies.

Footnotes

Supplementary data

The supplementary files are available to download from https://dx-doi-org.web.bisu.edu.cn/10.3233/CBM-230134.

References

Rumgay

Ferlay

de Martel

Georges

Ibrahim

A.S.

Zheng

Wei

Lemmens

and Soerjomataram

, Global, regional and national burden of primary liver cancer by subtype, Eur J Cancer 161 (2022), 108–118.

Villanueva

, Hepatocellular carcinoma, N Engl J Med 380 (2019), 1450–1462.

Llovet

J.M.

Kelley

R.K.

Villanueva

Singal

A.G.

Pikarsky

Roayaie

Lencioni

Koike

Zucman-Rossi

and Finn

R.S.

, Hepatocellular carcinoma, Nat Rev Dis Primers 7 (2021), 6.

Rashed

W.M.

Kandeil

M.A.M.

Mahmoud

M.O.

and Ezzat

, Hepatocellular Carcinoma (HCC) in Egypt: A comprehensive overview, J Egypt Natl Canc Inst 32 (2020), 5.

Schulze

Imbeaud

Letouze

Alexandrov

L.B.

Calderaro

Rebouissou

Couchy

Meiller

Shinde

Soysouvanh

Calatayud

A.L.

Pinyol

Pelletier

Balabaud

Laurent

Blanc

J.F.

Mazzaferro

Calvo

Villanueva

Nault

J.C.

Bioulac-Sage

Stratton

M.R.

Llovet

J.M.

and Zucman-Rossi

, Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets, Nat Genet 47 (2015), 505–511.

Wang

Nie

Dong

Yang

Liu

and Wang

, Diagnostic Value of CEUS LI-RADS Version 2017 in Differentiating AFP-Negative Hepatocellular Carcinoma from Other Primary Malignancies of the Liver, Diagnostics (Basel) 11 (2021).

Hanusek

Poletajew

Kryst

Piekielko-Witkowska

and Boguslawska

, piRNAs and PIWI proteins as diagnostic and prognostic markers of genitourinary cancers, Biomolecules 12 (2022).

Jiang

Tang

Chen

Sun

and Sun

, Elevated p-element-induced wimpy-testis-like protein 1 expression predicts unfavorable prognosis for patients with various cancers, J Oncol 2021 (2021), 9982192.

Cox

D.N.

Chao

Baker

Chang

Qiao

and Lin

, A novel class of evolutionarily conserved genes defined by piwi are essential for stem cell self-renewal, Genes Dev 12 (1998), 3715–3727.

10.

Farazi

T.A.

Juranek

S.A.

and Tuschl

, The growing catalog of small RNAs and their association with distinct Argonaute/Piwi family members, Development 135 (2008), 1201–1214.

11.

Girard

Sachidanandam

Hannon

G.J.

and Carmell

M.A.

, A germline-specific class of small RNAs binds mammalian Piwi proteins, Nature 442 (2006), 199–202.

12.

Klein

J.D.

Yang

Fan

Tang

and Peng

J.C.

, c-fos repression by piwi regulates drosophila ovarian germline formation and tissue morphogenesis, PLoS Genet 12 (2016), e1006281.

13.

Stuwe

Toth

K.F.

and Aravin

A.A.

, Small but sturdy: Small RNAs in cellular memory and epigenetics, Genes Dev 28 (2014), 423–431.

14.

Voller

Linck

Bruckmann

Hauptmann

Deutzmann

Meister

and Bosserhoff

A.K.

, Argonaute family protein expression in normal tissue and cancer entities, PLoS One 11 (2016), e0161165.

15.

Gou

L.T.

Kang

J.Y.

Dai

Wang

Zhao

Zhang

Hua

M.M.

Zhu

Chen

L.G.

X.D.

Shi

H.J.

and Liu

M.F.

, Ubiquitination-deficient mutations in human piwi cause male infertility by impairing histone-to-protamine exchange during spermiogenesis, Cell 169 (2017), 1090–1104 e13.

16.

Kamaliyan

Pouriamanesh

Amin-Beidokhti

Rezagholizadeh

and Mirfakhraie

, HIWI2 rs508485 polymorphism is associated with non-obstructive azoospermia in iranian patients, Rep Biochem Mol Biol 5 (2017), 108–111.

17.

Malone

C.D.

Brennecke

Dus

Stark

McCombie

W.R.

Sachidanandam

and Hannon

G.J.

, Specialized piRNA pathways act in germline and somatic tissues of the Drosophila ovary, Cell 137 (2009), 522–535.

18.

Roovers

E.F.

Rosenkranz

Mahdipour

Han

C.T.

Chuva de Sousa Lopes

S.M.

van der Westerlaken

L.A.

Zischler

Butter

Roelen

B.A.

and Ketting

R.F.

, Piwi proteins and piRNAs in mammalian oocytes and early embryos, Cell Rep 10 (2015), 2069–2082.

19.

Yin

D.T.

Wang

Chen

Liu

M.Y.

Han

Yan

Shen

Duan

J.J.

Wani

and Gao

J.X.

, Germline stem cell gene PIWIL2 mediates DNA repair through relaxation of chromatin, PLoS One 6 (2011), e27154.

20.

Kuramochi-Miyagawa

Kimura

Yomogida

Kuroiwa

Tadokoro

Fujita

Sato

Matsuda

and Nakano

, Two mouse piwi-related genes: Miwi and mili, Mech Dev 108 (2001), 121–133.

21.

Lingel

and Sattler

, Novel modes of protein-RNA recognition in the RNAi pathway, Curr Opin Struct Biol 15 (2005), 107–115.

22.

Xiao

and Hann

S.S.

, The emerging roles of PIWI-interacting RNA in human cancers, Cancer Manag Res 11 (2019), 5895–5909.

23.

Quinn

J.F.

Patel

Wong

Das

Freedman

J.E.

Laurent

L.C.

Carter

B.S.

Hochberg

Van Keuren-Jensen

Huentelman

Spetzler

Kalani

M.Y.

Arango

Adelson

P.D.

Weiner

H.L.

Gandhi

Goilav

Putterman

and Saugstad

J.A.

, Extracellular RNAs: Development as biomarkers of human disease, J Extracell Vesicles 4 (2015), 27495.

24.

Esteller

, Non-coding RNAs in human disease, Nat Rev Genet 12 (2011), 861–874.

25.

Han

E.C.

Ryoo

S.B.

Park

J.W.

H.K.

Choe

E.K.

H.K.

Park

B.K.

Moon

S.H.

Jeong

S.Y.

and Park

K.J.

, Oncologic and surgical outcomes in colorectal cancer patients with liver cirrhosis: A propensity-matched study, PLoS One 12 (2017), e0178920.

26.

Liu

, Serum proteomic pattern analysis for early cancer detection, Technol Cancer Res Treat 5 (2006), 61–66.

27.

Grochola

L.F.

Greither

Taubert

Moller

Knippschild

Udelnow

Henne-Bruns

and Wurl

, The stem cell-associated Hiwi gene in human adenocarcinoma of the pancreas: Expression and risk of tumour-related death, Br J Cancer 99 (2008), 1083–1088.

28.

Lee

E.J.

Banerjee

Zhou

Jammalamadaka

Arcila

Manjunath

B.S.

and Kosik

K.S.

, Identification of piRNAs in the central nervous system, RNA 17 (2011), 1090–1099.

29.

Sivagurunathan

Palanisamy

Arunachalam

J.P.

and Chidambaram

, Possible role of HIWI2 in modulating tight junction proteins in retinal pigment epithelial cells through Akt signaling pathway, Mol Cell Biochem 427 (2017), 145–156.

30.

O’Neill

R.S.

and Stoita

, Biomarkers in the diagnosis of pancreatic cancer: Are we closer to finding the golden ticket? World J Gastroenterol 27 (2021), 4045–4087.

31.

Wong

M.L.

and Medrano

J.F.

, Real-time PCR for mRNA quantitation, Biotechniques 39 (2005), 75–85.

32.

Zhou

Pache

Chang

Khodabakhshi

A.H.

Tanaseichuk

Benner

and Chanda

S.K.

, Metascape provides a biologist-oriented resource for the analysis of systems-level datasets, Nat Commun 10 (2019), 1523.

33.

Xie

and Zhou

, Plasma HULC as a promising novel biomarker for the detection of hepatocellular carcinoma, Biomed Res Int 2013 (2013), 136106.

34.

Liu

and Lu

, A novel RNA binding protein-associated prognostic model to predict overall survival in hepatocellular carcinoma patients, Medicine 100 (2021), e26491.

35.

Noyes

Kitajima

Suita

Miriyala

Isaac

Ahsan

Knelson

Vajdi

Tani

Thai

T.C.

Murai

Tapinos

Takahashi

Barbie

D.A.

and Yajima

, The germline factor DDX4 contributes to the chemoresistance of small cell lung cancer cells, Commun Biol 6 (2023), 65.

36.

Czech

Munafo

Ciabrelli

Eastwood

E.L.

Fabry

M.H.

Kneuss

and Hannon

G.J.

, piRNA-guided genome defense: From biogenesis to silencing, Annu Rev Genet 52 (2018), 131–157.

37.

Zeng

Zhang

Liu

Kang

Tang

Guo

Zhang

Wei

and He

, Co-expression of Piwil2/Piwil4 in nucleus indicates poor prognosis of hepatocellular carcinoma, Oncotarget 8 (2017), 4607–4617.

38.

Yuan

Qin

Ponnusamy

Chen

and Lin

, PIWI-interacting RNA in cancer: Molecular mechanisms and possible clinical implications (Review), Oncol Rep 46 (2021), 209.

39.

Liu

Zhong

and Zhang

, PIWIL2 promotes progression of non-small cell lung cancer by inducing CDK2 and Cyclin A expression, J Transl Med 13 (2015), 301.

40.

Ren

Z.J.

Wang

Liu

and Tang

, PIWIL4 regulates cervical cancer cell line growth and is involved in down-regulating the expression of p14ARF and p53, FEBS Lett 586 (2012), 1356–1362.

41.

Zhang

Ren

Pang

Duan

and Liu

, The expression of stem cell protein Piwil2 and piR-932 in breast cancer, Surg Oncol 22 (2013), 217–223.

42.

Zhao

Y.M.

Zhou

J.M.

Wang

L.R.

H.W.

Wang

X.L.

Tao

Z.H.

Sun

H.C.

W.Z.

Fan

Tang

Z.Y.

and Wang

, HIWI is associated with prognosis in patients with hepatocellular carcinoma after curative resection, Cancer 118 (2012), 2708–2717.

43.

Jacobs

D.I.

Qin

Lerro

M.C.

Dubrow

Claus

E.B.

DeWan

A.T.

Wang

Lin

and Zhu

, PIWI-Interacting RNAs in Gliomagenesis: Evidence from Post-GWAS and Functional Analyses, Cancer Epidemiol Biomarkers Prev 25 (2016), 1073–1080.

44.

Gao

Jin

J.M.

A.X.

Kim

Y.S.

Pal

S.K.

Nelson

R.A.

Lau

C.M.

Guo

Wang

Zhao

Chen

Rossi

J.J.

Weiss

L.M.

and Wu

, Piwi-Interacting RNAs (piRNAs) Are Dysregulated in Renal Cell Carcinoma and Associated with Tumor Metastasis and Cancer-Specific Survival, Mol Med 21 (2015), 381–388.

45.

Liu

S.S.

Liu

M.Y.

Sun

Xia

W.Y.

H.M.

Y.J.

Yang

G.L.

J.J.

Liu

X.X.

Feng

L.F.

and Gao

J.X.

, An unusual intragenic promoter of PIWIL2 contributes to aberrant activation of oncogenic PL2L60, Oncotarget 8 (2017), 46104–46120.

46.

Taubert

Wach

Jung

Pugia

Keck

Bertz

Nolte

Stoehr

Lehmann

Ohlmann

C.H.

Stockle

Wullich

and Hartmann

, Piwil 2 expression is correlated with disease-specific and progression-free survival of chemotherapy-treated bladder cancer patients, Mol Med 21 (2015), 371–380.

47.

Al-Janabi

Wach

Nolte

Weigelt

Rau

T.T.

Stohr

Legal

Schick

Greither

Hartmann

Wullich

and Taubert

, Piwi-like 1 and 4 gene transcript levels are associated with clinicopathological parameters in renal cell carcinomas, Biochim Biophys Acta 1842 (2014), 686–690.

48.

Sun

Yan

Huang

Luo

Tang

and Peng

, Piwil2 modulates the proliferation and metastasis of colon cancer via regulation of matrix metallopeptidase 9 transcriptional activity, Exp Biol Med (Maywood) 237 (2012), 1231–1240.

49.

Litwin

Szczepanska-Buda

Michalowska

Grzegrzolka

Piotrowska

Gomulkiewicz

Wojnar

Dziegiel

and Witkiewicz

, Aberrant expression of PIWIL1 and PIWIL2 and their clinical significance in ductal breast carcinoma, Anticancer Res 38 (2018), 2021–2030.

50.

Suzuki

Honda

and Kirino

, PIWI expression and function in cancer, Front Genet 3 (2012), 204.

51.

Dong

Xiong

Konno

Ihira

Kobayashi

Yue

and Watari

, Critical roles of PIWIL1 in human tumors: Expression, functions, mechanisms, and potential clinical implications, Front Cell Dev Biol 9 (2021), 656993.

52.

Yang

and Li

, WHO classification of tumors of the breast, Zhonghua Wai Ke Za Zhi 52 (2014), 1–3.

53.

Krishnan

Ghosh

Graham

Mackey

J.R.

Kovalchuk

and Damaraju

, Piwi-interacting RNAs and PIWI genes as novel prognostic markers for breast cancer, Oncotarget 7 (2016), 37944–37956.

54.

Meseure

Vacher

Boudjemaa

Lae

Nicolas

Leclere

Chemlali

Champenois

Schnitzler

Lesage

Dubois

and Bieche

, Biopathological significance of PIWI-piRNA pathway deregulation in invasive breast carcinomas, Cancers (Basel) 12 (2020).

55.

Liu

Dou

Song

Dong

Liu

Tao

Yin

and Xu

, The emerging role of the piRNA/piwi complex in cancer, Mol Cancer 18 (2019), 123.

56.

Cai

Zhou

Dong

Chen

and Wang

, PIWI-Interacting RNAs (piRNAs): Promising applications as emerging biomarkers for digestive system cancer, Front Mol Biosci 9 (2022), 848105.

57.

Feng

Yang

Wei

Song

Zhang

Wang

Liu

and Li

, Novel evidence for oncogenic piRNA-823 as a promising prognostic biomarker and a potential therapeutic target in colorectal cancer, J Cell Mol Med 24 (2020), 9028–9040.

58.

Erber

Meyer

Taubert

Fasching

P.A.

Wach

Haberle

Gass

Schulz-Wendtland

Landgraf

Olbricht

Jung

Beckmann

M.W.

Hartmann

and Ruebner

, PIWI-Like 1 and PIWI-Like 2 Expression in Breast Cancer, Cancers (Basel) 12 (2020).

59.

Zeng

L.K.

Meng

Liu

C.Y.

Dong

Xing

X.F.

and Shou

C.C.

, HIWI expression profile in cancer cells and its prognostic value for patients with colorectal cancer, Chin Med J (Engl) 124 (2011), 2144–2149.

60.

Jacobs

D.I.

Hoffman

A.E.

Zheng

and Zhu

, PIWI-interacting RNA 021285 is involved in breast tumorigenesis possibly by remodeling the cancer epigenome, Carcinogenesis 36 (2015), 1094–1102.

61.

Wang

Deng

Qing

Liu

and Xue

, Exosomal piRNA profiling revealed unique circulating piRNA signatures of cholangiocarcinoma and gallbladder carcinoma, Acta Biochim Biophys Sin (Shanghai) 52 (2020), 475–484.

62.

Mai

Zheng

Guo

Ding

Bai

Zhang

Huang

Liu

Sui

Pan

Deng

Bai

Ligu

Tan

Zheng

and Lin

, Serum piRNA-54265 is a New Biomarker for early detection and clinical surveillance of Human Colorectal Cancer, Theranostics 10 (2020), 8468–8478.

63.

Wang

Yang

Zhang

Dong

Zheng

Zou

Wang

and Wang

, A serum piRNA signature as promising non-invasive diagnostic and prognostic biomarkers for colorectal cancer, Cancer Manag Res 11 (2019), 3703–3720.

64.

Sabbah

N.A.

Abdalla

W.M.

Mawla

W.A.

AbdAlMonem

Gharib

A.F.

Abdul-Saboor

Abdelazem

A.S.

and Raafat

, piRNA-823 is a unique potential diagnostic non-invasive biomarker in colorectal cancer patients, Genes (Basel) 12 (2021).

65.

Tosun

Demirtas

Sonmez

Tombul

S.T.

Akalin

and Ozkul

, Can the expression level of PIWIL 2 gene be a serum marker for prostate cancer? A single-center prospective study, Turk J Urol 45 (2019), S22–S25.

66.

Rizzo

Rinaldi

Marchese

Coviello

Sellitto

Cordella

Giurato

Nassa

Ravo

Tarallo

Milanesi

Destro

Torzilli

Roncalli

Di Tommaso

and Weisz

, Specific patterns of PIWI-interacting small noncoding RNA expression in dysplastic liver nodules and hepatocellular carcinoma, Oncotarget 7 (2016), 54650–54661.

67.

Muscari

and Maulat

, Preoperative alpha-fetoprotein (AFP) in hepatocellular carcinoma (HCC): Is this 50-year biomarker still up-to-date? Transl Gastroenterol Hepatol 5 (2020), 46.

68.

Riquelme

Perez-Moreno

Letelier

Brebi

and Roa

J.C.

, The Emerging Role of PIWI-Interacting RNAs (piRNAs) in Gastrointestinal Cancers: An updated perspective, Cancers (Basel) 14 (2021).

69.

Dong

Huang

Weng

Liang

and Chen

, MiRNA-124-3p.1 sensitizes hepatocellular carcinoma cells to sorafenib by regulating FOXO3a by targeting AKT2 and SIRT1, Cell Death Dis 13 (2022), 35.

70.

Liu

Tan

Chen

and Chen

, MicroRNA-101-3p suppresses proliferation and migration in hepatocellular carcinoma by targeting the HGF/c-Met pathway, Invest New Drugs 38 (2020), 60–69.

71.

Xiao

Shen

Zhang

and Cho

, Therapeutic targeting of noncoding RNAs in hepatocellular carcinoma: Recent progress and future prospects (Review), Oncol Lett, 2018.

72.

Apostolou

Toloudi

and Papasotiriou

, Identification of genes involved in breast cancer and breast cancer stem cells, Breast Cancer: Targets and Therapy 7 (2015), 183.

Elevated expression patterns of P-element Induced Wimpy Testis (PIWI) transcripts are potential candidate markers for Hepatocellular Carcinoma

Abstract

BACKGROUND:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Methods

2.1 Patients and sampling

2.2 Sample processing

2.3 Biochemical parameters

2.4 RNA extraction and cDNA synthesis

2.5 Real-time quantitative polymerase chain reaction (RT-QPCR)

2.6 In Silico variant detection for PIWIL genes and Network Analysis

2.7 Statistical

Table 1 Demographics and Clinico-pathological characteristics for HCC patients

3.1 Patient characteristics

Table 2 Clinico-pathological characteristics for HCC patients and their association with PIWIL mRNA expression

3.3 Association of clinicopathological features with the expression PIWIL mRNA transcripts

Table 5 HCC risk results of PIWIL mRNA in HCC patients

3.5 Logistic regression analysis of PIWIL’s

3.6 In Silico analysis for PIWIL genes

4. Discussion

Footnotes

Supplementary data

References

Table 1
Demographics and Clinico-pathological characteristics for HCC patients

Table 2
Clinico-pathological characteristics for HCC patients and their association with PIWIL mRNA expression

Table 5
HCC risk results of PIWIL mRNA in HCC patients