Role and screening potential of let-7b-5p, miR-15a-5p, and miR-15b-5p in human prostate cancer

doi:10.21203/rs.3.rs-4698836/v1

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Role and screening potential of let-7b-5p, miR-15a-5p, and miR-15b-5p in human prostate cancer

https://doi.org/10.21203/rs.3.rs-4698836/v1

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Prostate cancer is the fifth leading cause of male cancer mortality and poses a serious threat to men's health worldwide. PSA testing is widely used in prostate cancer screening, but its high false-positive rate leads to unnecessarily high subsequent testing costs, mental anguish and potential physical harm to patients. Therefore, there is an urgent need to develop a convenient, cost-effective and non-invasive diagnostic method to assist in reducing the false-positive rate of PSA screening. The aim of this study was to evaluate the diagnostic value of serum MicroRNA expression in patients with prostate cancer. We selected 10 miRNAs in the literature that were associated with prostate cancer. Afterwards, we measured the expression levels of these miRNAs in serum of 112 prostate cancer patients and healthy controls through a training phase and a validation phase. By plotting receiver operating characteristic curve, the miRNAs with the highest diagnosis value were chosen. Then, a set of miRNAs with the top diagnostic value was identified using stepwise logistic regression. The findings showed that 5 kinds of miRNAs (let-7b-5p, miR-15a-5p, miR-133a-3p, miR-15b-5p, miR-144-3p) were abnormally expressed in the serum of prostate cancer patients. The diagnostic panel constructed with these 3 miRNAs including let-7b-5p, miR-15a-5p miR-15b-5p and which have high specificity and sensitivity in detecting prostate cancer (area under the curve (AUC) = 0.899). Our study illustrates the potential of a three-microRNA panel in the diagnosis of prostate cancer, which can help in the early detection of prostate cancer and may even assist in reducing the false-positive rate of PSA screening.

Biological sciences/Cancer

Biological sciences/Genetics

Biological sciences/Molecular biology

Health sciences/Biomarkers

Health sciences/Diseases

Health sciences/Medical research

Health sciences/Molecular medicine

Health sciences/Oncology

Health sciences/Urology

prostate cancer

microRNA

PSA

False-positive rate

biomarker

bioinformatics

Prostate cancer is one of the most common malignant tumors, the second most common cancer worldwide in 2022, and the fifth leading cause of cancer deaths in men¹. According to data, prostate cancer (PC) accounted for 14.1% of all malignancies in men of all ages globally in 2020, second only to lung cancer, with approximately 1.5 million new cases and 397,000 new deaths globally in 2022 ^1,2.Although the incidence of prostate cancer is low in Asia, it has been increasing in recent years³. Many men with prostate cancer are screened and diagnosed using prostate biopsy, PSA testing, DRE and MRI. However, in the absence of prostate cancer, both DRE and PSA tests may be abnormal (false positive)^4-6. It is evident that prostate cancer is a serious threat to human health and affects millions of men worldwide.

PSA screening is widely used in prostate cancer screening,but false-positive results are prevalent in PSA-based prostate cancer screening studies⁷. A significant proportion of positive PSA test results are false-positive in both the ERSPC and PLCO trials, with ERSPC sites reporting false-positive rates of approximately 1 in 6 or more men⁸, and more than two-thirds of positive test results in the PLCO trial being false-positive⁹. The proportion of false-positive results is even higher when using a low PSA threshold (3.0 ng/mL), such as the Swedish site in the ERSPC that reported over 45% false-positive results⁸.False-positive results for prostate cancer mean that patients are faced with further diagnosis by puncture biopsy or MRI¹⁰.This could result in unneeded psychological suffering, excessive testing costs, and even physical injury^11,12. Therefore, it is unknown if the advantages of PSA screening outweigh the disadvantages¹³.Thus, there is a pressing need to create a practical, affordable, and non-invasive diagnostic technique to assist in reducing the false-positive rate of PSA screening for prostate cancer

MiRNAs, act as post-transcriptional gene regulators, are short (18-22 nucleotides [nt]) non-coding single-stranded RNA molecules¹⁴, which can affect multiple mRNAs. Overwhelming evidence suggests that miRNAs are of fundamental importance in normal development, differentiation, growth control and cancer development and progression ¹⁵. A number of miRNAs have been pinpointed to serve as oncogenes, tumor suppressors, or even modulators of cancer stem cells and metastases¹⁶. In addition to tissue-based studies, serum and plasma hold a large number of unvarying miRNAs from a variety of tissues or apparatuses¹⁷. Circulating miRNAs can be obtained without invasive biopsy¹⁸ and can be convenient detected and quantified with precision by various widely used standard techniques, such as qRT-PCR ,and small RNA sequencing¹⁹. In recent years, numerous researches have manifested that miRNAs in serum can be used as a potential marker for cancer such as pancreatic ductal adenocarcinoma²⁰ and breast cancer²¹. In conclusion, circulating miRNAs hold promise as a new biomarker for screening prostate cancer and a new method to assist in reducing the false-positive rate of PSA screening for prostate cancer

In our study, we conducted a 3-stage study in which the expression levels of different miRNAs in serum of PC patients and healthy controls were measured by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) to screen for miRNAs with diagnostic value. Then we constructed a prostate cancer diagnostic panel by using one of these miRNAs. In addition, we have used bioinformatics to predict these miRNAs-related target genes and analyze their possible pathogenesis and therapeutic targets.

Participants and Ethics Statement

From January 2018 to September 2020, with the approval of the Ethics Committee of Peking University Shenzhen Hospital (2019-043), we recruited 224 partakes at Peking University Shenzhen Hospital, including 112 patients with PC and 112 normal controls (NCs). Informed consent was obtained from all subjects and/or their legal guardian(s), and the serum sampling procedure was in compliance with the relevant rules issued by the commission. PC patients was first diagnosed and had not received any treatment prior to the collection of serum samples. Healthy controls were free of relevant underlying diseases and history of cancer. Confirmation that all methodologies are in accordance with the Declaration of Helsinki and related regulations.

Study design

In the screening phase, The Te Gene Expression Omnibus and PubMed databases were searched for the miRNAs that were significantly expressed in PC, and these miRNAs were chosen as potential miRNAs. Potential miRNAs were then chosen using the Encyclopedia of RNA Interactomes (ENCORI) database.These miRNAs are abnormally expressed in PC tissues or are connected with the development of prostate cancer. Subsequently, corresponding levels of expression of these miRNAs were gauged in the serum of 30 PC patients and 30 HCs during the training phase. MiRNAs with diagnostic value were selected to enter the validation stage under the criterion of p-value less than 0.05. During the validation phase, we expanded the sample size and identified levels of expression of these miRNAs in the serums of 82 PC patients and 82 HCs. To verify the diagnostic value of these miRNAs, we calculated their p-values, plotted the receiver operating characteristic curve (ROC), and computed the area under the curve (AUC). Lastly, a diagnostic miRNA panel was built according to stepwise logistic regression. In the bioinformatic analysis section, we used mirwalk 3.0 for target gene prediction and the DAVID database^22,23 for GO annotation and KEGG enrichment analysis.

Obtaining serum specimens from patients and healthy controls

The study collected serum samples from a total of 224 men from January 2018 to September 2020, including 112 patients with pathologically confirmed prostate cancer after surgical resection and 112 healthy check-up men. 10 ml serum sample was taken from each partaker and centrifuged at 3000g for 10 min at 4ºC for 2h. Serum samples were stored at -80℃ and frozen for RNA extraction. The clinical characteristics of the participants are shown in Table 1.

Table 1

Clinical and demographic information of 224 participants
	Training stage (n = 60)		Validation stage (n = 164)
	Cases (%)	Controls (%)	Cases (%)	Controls (%)
Total number	30	30	82	82
Age (Mean ± SD)	67.7 ± 8.5	68.2 ± 8.0	68.0 ± 8.6	68.5 ± 8.1
Tumor stage
<=T2	17 (56.7)		53 (64.6)
>=T2	13(43.3)		29 (35.4)
Lymph node metastasis
Positive	2 (6.67)		6(73.2)
Negative	28(93.3)		78(92.7)
Bone metastasis
Positive	3 (10.0)		7(8.5)
Negative	27 (90.0)		75 (91.5)
Gleason grade
Well differentiation	0		0
Moderate differentiation	20 (66.7)		58 (70.7)
Poor differentiation	10 (33.3)		24 (29.3)

RNA extraction and RT‑qPCR

We added 2ul cel-miR-54-5p (10 nm/L, RiboBio, China) to each sample, which used for normalizing the variability in the extraction process as an internal reference. Then, total RNA from samples were extracted by TRIzol LS isolation kit (Thermo Fisher Scientific, Waltham, MA, United States) and RNA concentration and purity was gauged by NanoDrop 2000c spectrophotometer (Thermo Scientific, United States). RT-qPCR was used to determine the expression level of these miRNAs. The miRNAs were amplified by the reverse transcription-specific primers of the Bulge-Loop miRNA qRT-PCR primer set (RiboBio, Guangzhou, China) in reverse transcription. Then, by the qPCR with the Taqman probe, the expression level of miRNAs was detected on the LightCycler480 Real-Time PCR system (Roche Diagnostics, Mannheim, Germany). The qRT-PCR was performed with 40 cycles following at 95°C for 20s, 95°C for 10s, 60°C for 20s, and 70°C for 10s.

tatistical analyses

We used 2 − ΔΔCq method to compute the expression levels of the miRNAs²⁴. The categorical variables between the different groups were reported as numbers and percentages. Continuous variables were presented as the average value ± standard deviation. Kruskal-Wallis rank test was used to compare several distinct phases. Different expression levels of candidate miRNAs in PC and HC samples were anatomized by using Students’ T-test or Mann–Whitney test. The diagnostic miRNA panel was developed using multiple logistic regression analysis. The ROC curve and AUC value were estimated to be the diagnostic value of miRNAs. P-value < 0.05 was set as statistically significant. SPSS software (SPSS 26.0 Inc, Chicago, IL) GraphPad Prism 8 (GraphPad Prism 9.4.1.681) and Medcalc (Version 19) were used for the statistical processing.

Bioinformatic analysis

We use MiRWalk3.0 database to find target genes for candidate miRNAs. Genes forecasted by over two candidate miRNAs were chosen as target genes, then placed into the David database^22,23 for Gene Ontology (GO) functional annotation and pathway analysis, and visualized by using ImageGP²⁵. Through GEPIA database²⁶analysis, we investigated two target genes whose expression was significantly different in PC patients compared with NCs, which may be related to tumorigenesis and progression of PC.

Demographic and clinical features of participants

In total,112 PC patients and 112 healthy check-ups were included in our study. The health controls were medical examiners who did not have an underlying disease or a history of cancer. PC patients were diagnosed by post-surgical pathological biopsy, none had received any treatment prior to the collection of serum samples. They also had no history of other tumors. PC patients were assigned to the same control group based on age. Table 1 provides populations and clinical outcomes for 224 participants. The difference in age distribution between PC patients and HCs was not statistically significant, with a p-value > 0.05.

Selection of miRNAs related to PC in the screening phase

We looked through the Gene Expression Omnibus and PubMed databases to identify ten potential miRNAs linked to prostate cancer. then examined in more detail with the ENCORI database. As shown in Fig. 1, miRNAs in PC tissues or body fluids are abnormally regulated. The meaningful miRNAs entering the training phase included: let-7b-5p, miR-15a-5p, miR-133a-3p, miR-15b-5p, miR-144-3p, miR-26a-5p, miR-151a-3p, miR-490-3p, miR-100a-5p, miR-615-3p.

Testing candidate miRNAs at the training phase

During the training phase, we measured the expression levels of the corresponding miRNAs in the sera of 30 PC patients and 30 HCs by using qRT-PCR. The expression levels of these 10 miRNAs in PC patients and HCs are shown in Fig. 2. Obviously, at a standard of p-value < 0.05, we can see that five of the miRNAs, including miR-144-3p, miR-15b-5p, miR-15a-5p, miR-133a-3p, let-7b-5p, miR-15a-5p, are expressed differently between cancer patients and healthy controls anyway. We put these five miRNAs into the validation phase for further examination.

Further validation of candidate miRNAs

During the validation phase, we expanded the sample size by collecting and testing serum expression levels from 82 PCs and 82 HCs. The results are shown in Fig. 3, which shows that these five miRNAs are dysregulated in prostate cancer expression. The results are as follows: the expression level of miR-15b-5p was significantly upregulated (p-value < 0.001) and the remaining four miRNAs expression levels were all downregulated. We built the ROC curves of these miRNAs to assess their diagnostic value, and the outcomes are shown in Fig. 3. The results of AUC values are as follows: 0.832(95% confidence interval[CI]:0.766–0.886; Fig. 3B) for let-7b-5p, 0.781(95% CI: 0.710– 0.842; Fig. 3D) for miR-15a-5p, 0.564(95% CI:0.485–0.641; Fig. 3F) for miR-133a-3p, 0.693 (95% CI: 0.616–0.762; Fig. 3H) for miR-15b-5p and 0.797 (95% CI: 0.727– 0.855; Fig. 3J) for miR-144-3p. Next, in Table 2, Youden index was used to compute the best cut- off value and specify the best specificity and sensitivity of these five miRNAs to diagnose PC.

* Represents p < 0.05, ** represents p < 0.01, ***represents p < 0. 001.The red curve represents the receiver- operating characteristic (ROC) curve. The green curve represents 95% ROC confidence interval. The blue line represents diagonal.

Building a diagnostic panel to screen for PC

A single miRNA for diagnosing PC can be unilateral, so we built a diagnostic panel of multiple patterns using data from the validation phase. Through a stepwise logistic regression model, we selected three miRNAs including let-7b-5p,15a-5p, miR-15b-5p to create a panel that has the greatest diagnostic value. The latest logistic regression model consists of the following equation: [logit p = 4.777+(2.421×miR-15b-5p) +(-3.443×miR-15a-5p) +(-5.545×let-7b-5p)]. The roc of this three miRNAs panel is shown in Fig. 4, with an auc value of 0.899 (95% CI: 0.843–0.941; sensitivity = 85.37%, specificity = 82.93%; Table 2)

Table 2

Outcomes of receiver operating characteristic curves and Youden index for 5 candidate miRNAs and the panel. AUC: area under curve; CI: confidence interval
	AUC	P value	95% CI	Associated criterion	Sensitivity (%)	Specificity (%)
Let-7b-5p	0.832	< 0.001	0.766–0.886	≤ 0.766	74.39	80.49
miR-15a-5p	0.781	< 0.001	0.710–0.842	≤ 0.829	75.61	74.39
miR-133a-3p	0.564	< 0.05	0.485–0.641	≤ 1.072	70.73	47.56
miR-15b-5p	0.693	< 0.001	0.616–0.762	> 1.362	41.46	90.24
miR-144-3p	0.797	< 0.001	0.727–0.855	≤ 0.655	56.10	91.46
three-miRNA panel	0.899	< 0.001	0.843–0.941	> 0.513	85.37	82.93

Bioinformatics analysis of three miRNAs

We used hsa-let-7b-5p, hsa-mir-15a-5p, has-mir-15b-5p for target gene prediction on miRWalk 2.0. We selected genes that were predicted by more than two miRNAs simultaneously as target genes. As revealed in the Venn Diagram in Fig. 5A, there are a total of 995 target genes. We searched genes that were simultaneously predicted by 3 miRNAs target genes in the GEPIA²⁷ database and found that two genes including FAM107A and TAF1C were differentially expressed in prostate cancer at the criterion of p-value < 0.01 and |log2FC| Cutoff > 2. These two genes could be potential target genes for the diagnosis of prostate cancer(Fig. 5B-C).

Then, these 995 target genes were added to the DAVID database^22,23 for GO annotation and KEGG enrichment analysis. GO functional annotation consisting of biological process (BP), cellular component (CC), and molecular function (MF). Figure 6 lists the first five components of the GO function annotation, including signal transduction(GO:0007165), proteasome − mediated ubiquitin − dependent protein catabolic process (GO:0043161), protein polyubiquitination(GO:0000209), positive regulation of dendritic spine morphogenesis(GO:0061003), negative regulation of tyrosine phosphorylation of STAT protein(GO:0042532) in the BP category; cytoplasm (GO:0005737), nucleus (GO:0005634), cytosol (GO:0005829), nucleoplasm (GO:0005654), centrosome (GO:0005813) in the CC category; protein binding (GO:0005515), ATP binding (GO:0005524), protein kinase binding (GO:0019901), protein serine/threonine kinase activity (GO:0004674), protein kinase activity (GO:0004672). The enriched KEGG pathway contains pathways in Oxytocin signaling pathway, Adrenergic signaling in cardiomyocytes, Oocyte meiosis, Ubiquitin mediated proteolysis, Apelin signaling pathway.

PC remains a major public health problem worldwide, posing a serious threat to the health of men all over the world²⁸. PC continues to be the number one cause of cancer-related morbidity in many countries²⁹. As a result, early diagnosis and timely treatment are especially important in reducing prostate cancer mortality. Recently ,miRNAs have been affirmed to be dysregulated in expression in many tumor tissues³⁰, while playing an important role in the growth, proliferation and metastasis of various tumors ³¹. At the same time, miRNA has been found to be a potential biological marker for various cancers due to its stable presence in the blood, its availability by non-invasive examination only, and its easily detectable expression level¹⁹. In our study, to find the serum mir with the highest diagnostic value in PC, we designed a 3-stage study. A diagnostic panel consisting of 3 miRNAs was constructed by expanding the sample step by step through screening and validation. It allows for better screening and diagnosis of prostate cancer (AUC = 0.899; 95% CI: 0.843–0.941; sensitivity = 85.37%, specificity = 82.93%).

In our diagnostic panel, let-7b-5p has the highest diagnostic value (AUC = 0.832; 95% CI: 0.766 to 0.886). Even before us, let-7b-5p was shown to be remarkable regulated in cancerous tissues compared to the adjacent normal prostate epithelium, confirming the ability of mir to be a potential biomarker³². let-7b-5p can also regulate M2 polarization through the SOCS1/STAT pathway and reverse M2 differentiation to enhance macrophage phagocytosis. Ultimately, it inhibits the proliferation of PC cells³³. Therefore, let-7b-5p has the potential to be a therapeutic target for PC. A study by Jentai Lin et al. discovered that the expression level of let-7b-5p were significantly higher in the urine of patients with bladder cancer and could be a promising non-invasive biomarker to diagnose bladder cancer³⁴. For let-7b-5p in the urine of PC patients, this is also worth exploring.

Another miRNA in our panel is miR-15a-5p. A Study Shows miR-15a-5p reverses the effects of PVT1 Interference and Inhibits the Effects of KIF23 Knockdown to affect the progression of PC³⁵. So miR-15a-5p may be a new biomarker for the treatment of PC. Early studies have demonstrated the potential of circulating miR-15a-5p as a diagnostic biomarker for endometrial cancer³⁶ and non-small cell lung cancer³⁷. Our research refines the diagnostic value of miR-15a-5p in prostate cancer.

Numerous researchers have revealed that mir-15b-5p not only promotes the progression of PC cells by targeting RECK³⁸, but also exacerbates the progression of prostate cancer by inhibiting LATS2³⁹. Thus, mir-15b-5p usually plays an oncogenic role in PC, and the expression level is remarkable upregulated in cancer tissues. This is consistent with the expression level of serum mir-15b-5p in our experiments.

To look into the potential mechanism functions of let-7b-5p, miR-15a-5p, and mir-15b-5p, we selected target genes from miRWalk3.0 that were predicted by more than two miRNAs for bioinformatic analysis. The conclusions showed that these targets of aberrantly regulated miRNAs were related to numerous important cancer-related biological processes and pathways, such as oxytocin signaling pathway, Ubiquitin mediated proteolysis, and apelin signaling pathway. A recent study showed that oxytocin can cause PC cells to migrate⁴⁰. In pancreatic cancer, upregulated apelin signaling activates oncogenic signaling pathways to promote tumor development. Apelinergic system appears as a promising target for cancer therapy⁴¹. Therefore, upregulated apelin signaling also appears to be a worthwhile therapeutic target in PC.

Under the criterion of p-value < 0.01 and |log2FC| Cutoff > 2 based on the expression level, we identified FAM107A and TAF1C through GEPIA database as attainable target genes for three-miRNA panel from genes predicted by three miRNAs simultaneously. FAM107A is a protein-coding gene that inhibits proliferation and induces apoptosis in kidney cancer cells⁴².AS reported by Yunfei Ma et al. ,FAM107A was an independent risk factor for progression-free survival in PC, showing its value in the diagnosis and prognosis of prostate cancer⁴³. Many investigations have demonstrated the potential of FAM107A to be a therapeutic target for colon cancer and pancreatic cancer^44,45, and its value in prostate cancer remains to be explored. Currently, there is a lack of research on TAF1C in prostate cancer. A study shows that TAF1C is associated with gastric and colorectal carcinogenesis through a combination of somatic frameshift mutations and intratumorally heterogeneity⁴⁶. As to whether it is related to in the development of prostate cancer there is a lack of reliable experiments.

Although the miRNA panel constructed in our study is meaningful, there are still some shortcomings. It is one-side for us to select only 10 miRNAs in the screening phase and might lead us to overlook other meaningful mir in the serum. Our study sample consisted of only 112 prostate cancer patients and was confined to one region, which may have made a difference.

Our study constructed a three-miRNA panel that can be used as a potential diagnostic biomarker for prostate cancer. This helps in the early detection and treatment of prostate cancer, as well as aids in reducing the false-positive rate of PSA screening for prostate cancer, thereby reducing unnecessary prostate biopsies and overtreatment. And targets genes FAM107A and TAF1C might be the potential therapeutic target for prostate cancer.

Acknowledgements

This study was supported by Shenzhen High-level Hospital Construction Fund, Clinical Research Project of Peking University Shenzhen Hospital (no. LCYJ2017001, LCYJ2020002, LCYJ2020015, LCYJ2020020), Science and Technology Development Fund Project of Shenzhen (no. JCYJ20180507183102747).

Authors’ contributions

Conception:Chong Lu, Zhenyu Wen, Zhenjian Ge, Wenkang Chen, Yingqi Li, Wuping Wang, Siwei Chen, Hang Li

Interpretation or analysis of data: Huimei Zhou, Yutong Wu, Shengjie Lin, Pengwu zhang

Preparation of the manuscript: Chen Sun

Revision for important intellectual content: Xutai Li

Supervision: Xionghui Wu ,Yongqing Lai

Disclosure

The authors declare that there are no conflicts of interest.

Data availability

Te datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

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Role and screening potential of let-7b-5p, miR-15a-5p, and miR-15b-5p in human prostate cancer

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Version 1

Abstract

Figures

Introduction

Methods

Participants and Ethics Statement

Study design

Obtaining serum specimens from patients and healthy controls

RNA extraction and RT‑qPCR

tatistical analyses

Bioinformatic analysis

Results

Demographic and clinical features of participants

Selection of miRNAs related to PC in the screening phase

Testing candidate miRNAs at the training phase

Further validation of candidate miRNAs

Building a diagnostic panel to screen for PC

Bioinformatics analysis of three miRNAs

Discussion

CONCLUSIONS

Declarations

References

Additional Declarations

Status:

Version 1