Altered Piwi-RNA Profiles of Peripheral Blood in Bone Metastasis of Non-Small Cell Lung Cancer

doi:10.21203/rs.3.rs-3123817/v1

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Altered Piwi-RNA Profiles of Peripheral Blood in Bone Metastasis of Non-Small Cell Lung Cancer

https://doi.org/10.21203/rs.3.rs-3123817/v1

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Background

PIWI-interacting RNAs (piRNA) are a novel class of non-coding RNAs. They are involved in various pathophysiological activities. However, the expression profiles and functions of piRNAs in the bone metastasis of non-small cell lung cancer are still largely unknown.

Methods

Blood samples were collected from patients with non-small cell lung cancer (NSCLC), NSCLC with bone metastasis (BoM) and from healthy volunteers. To evaluate piRNA expression profiles in these samples, high-throughput piRNA microarray was performed. RStudio was used for PCA analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate significantly dysregulated piRNAs. To predict the potential functions of dysregulated piRNAs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. The mRNA targets of the piRNAs were predicted using TargetScan and miRanda software. The interaction networks between the piRNAs and their targets were constructed using Cytoscape software.

Results

A total of 11026 differentially expressed piRNAs were identified, including 134 up-regulated piRNAs and 45 down-regulated piRNAs in NSCLC with bone metastasis (LCBM) vs. non-small cell lung cancer (LC) group and 760 up-regulated piRNAs and 975 down-regulated piRNAs in LCBM vs. healthy volunteers (Ctrl) group with P ≤0.05, fold change ≥1.0. Twenty-four dysregulated piRNA genes with statistically significant differences among all three groups were selected to be validated by qPCR. The qRT-PCR validation confirmed the reliability of the microarray data. The potential targets were predicted by combining with the miRanda and TargetScan software analyses. Through GO and KEGG analysis, these dysregulated piRNAs were found to be potentially involved in NSCLC with BoM pathogenesis. In particular, these pi-RNA might be involved in the transcriptional misregulation and osteoclast differentiation in lung cancer and the bone metastasis.

Conclusions

In conclusion, we have identified novel piRNAs that play important functional roles in initiating and progressing of NSCLC with BoM. Bioinformatics analyses suggested that these dysregulated piRNAs may play an important functional role in the tumorigenesis of NSCLC, especially in the BoM. We identified that piR-hsa-16644 is a potential biomarkers and therapeutic target for BoM, and piR-hsa-11510 is strongly associated with EGFR tyrosine kinase inhibitor resistance.

non-small cell lung cancer

bone metastasis

piwi-RNA

Peripheral blood

biomarker

NSCLC Approximately 30–60% of lung cancer patients develop bone metastases(Santini et al., 2015). Bone metastases are associated with decreased survival, bone pain, and skeletal-related events (SRE)(Riihimäki et al., 2014). In order to make progress in patients with metastatic lung cancer with bone metastases, it is essential to understand the molecular pathogenesis of bone metastases. Many studies have shown that to prepare a "pre-metastatic niche" for incoming cancer cells, the primary tumor can send signals to the future metastatic site(Peinado et al., 2017). During this process, cancer-derived soluble factors and extracellular vesicles disturb the bone homeostasis. Osteoclast differentiation is induced by macrophage colony stimulating factor (M-CSF) as well as receptor-activator of nuclear kappa-B ligand (RANKL). Cancer cell can secret these factors, simulate the osteoclasts mature to demineralize bone(Clézardin et al., 2021).

Although the basic principles of physiological and pathological bone remodeling are well established, novel regulatory molecules are still being revealed. The discovery of non-coding RNAs as key regulators of various biological processes raised the question whether they are also implicated in bone homeostasis. Indeed, during the last decade short and long non-coding RNAs have emerged as key regulators of bone development, homeostasis, and pathology(Feng et al., 2023). The best characterized non-coding RNAs are microRNAs (miRNAs), miRNAs are dysregulated in various bone disorders and individual miRNAs functionally maintain/disturb bone remodeling in disease conditions, such as bone metastases. Several miRNAs have been shown to be up- or downregulated in primary breast cancer and/or bone metastases. Long noncoding RNAs (lncRNAs) are operationally defined as RNA transcripts longer than 200 nucleotides, and there is no evidence that they encode peptides while initially described only as intergenic transcripts. Many studies were indicated that lncRNA play roles in cancer metastases progress. lncRNA PCA3 has been identified as a non-invasive upfront diagnostic marker of prostate cancer with reliable test characteristics and is used in clinical practice(Prensner et al., 2014). Overexpression of lncRNA SChLAP1 is independently prognostic of prostate cancer metastasis within 10 years following prostatectomy based on a microarray analysis of 1,008 patients(Liu, Dang, Lim, Feng, & Maher, 2021)

Piwi-interacting RNAs (piRNAs) are expressed in the germline, but recent studies have demonstrated that piRNAs are also expressed in cancer cells, where piRNAs play crucial role in repression of transposable elements cleaving, deadenylation and decay(Das et al., 2008). Tens of thousands of piwi-RNAs have been identified by high-throughput RNA sequencing, but only a small percentage of these have been functionally characterized(Parker, Roe, & Barford, 2005). To determine the expression pattern of piwi-RNAs in NSCLC bone metastases and to identify biomarkers that reflect disease staging will be useful in diagnosing the disease, predicting disease progression, and developing new targets for therapeutic. Here, we undertake an analysis of normal/NSCLC local cancer/ NSCLC local cancer bone metastases samples to define RNA biomarkers associated with metastatic progression. Through differential expression analysis and comparative transcriptomic studies of cancer specimens, various piwi-RNAs have been prioritized for functional studies, revealing a diversity of phenotypes and mechanisms.

Study Design and Tissue Samples

This study is a prospective observational study designed to investigate the correlation between piwi-RNA expression and NSCLC with bone metastases. The inclusion criteria of volunteers were between 18 and 80 years old without fundamental disease; NSCLC with or without bone metastatic progression (Suppl. Table S1.). The current study includes three groups, healthy volunteers (Ctrl), non-small cell lung cancer (LC), NSCLC with bone metastases (LCBM) (n = 5). Peripheral blood was collected from patients enrolled under informed consent protocols. Patients received no adjuvant or neo-adjuvant therapy within recent 3 months while blood sample collection. The study is approved by the institutional review boards at the Huazhong University of Science and Technology Union Shenzhen Hospital (IRB number: KY-046-01).

Microarray Hybridization and gene expression data analysis

RNA was extracted from peripheral blood samples, then piRNA sample labeling was performed using Quick Amp Labeling Kit, One-Color (Agilent). Agilent Array platform was employed. The labeled samples were hybridized onto Arraystar Human piRNA Array in Agilent’s SureHyb Hybridization Chambers according to the manufacturer’s standard protocols (Agilent Technologies) for microarray analysis. piRNAs with good match are selected on this array probes of about 23000 piRNAs. Quantile normalization and subsequent data processing were performed using the GeneSpring GX v12.1 software package (Agilent Technologies). After quantile normalization of the raw data, piRNAs had flags in Present or Marginal were chosen for differentially expressed piRNAs screening. Differential expression analysis between groups was performed with a fold change (FC) ≥ 1.5 and FDR < 0.05 were considered statistically significant. The student's t-test or the nonparametric Mann–Whitney U test was used to compare participants’ demographic and clinical characteristics between groups (p < 0.05).

Principal Component Analysis (PCA)

PCA reduces data by geometrically projecting them onto lower dimensions called principal components (PCs), with the goal of finding the best summary of the data using a limited number of PCs. The first PC is chosen to minimize the total distance between the data and their projection onto the PC. By minimizing this distance, we also maximize the variance of the projected points, σ2. The second (and subsequent) PCs are selected similarly, with the additional requirement that they be uncorrelated with all previous PCs(Welsh & Gardini, 2023).

Quantitative PCR

Total RNA was isolated from the samples using QIAzol Lysis Reagent (Qiagen, USA). For quantification of mRNA and piRNA, cDNA was synthesized with PrimeScript™ RT reagent kit (TaKaRa, Dalian, China). Real-time PCR analysis was performed using TB Green™ Premix Ex Taq™ II (TaKaRa, Dalian, China) and a Roche LC96 real-time PCR system (Roche, Switzerland). As an internal control, 18S rRNA was used. The relative expression of the RNAs was calculated by using the 2^−ΔΔCt method. All primers were synthesized by Sangon Biotech (Shanghai, China) (listed in Table S2).

Analyses of piRNA-target gene interaction

We used appropriate algorithms for the biotypes of the differentially expressed piRNAs. Since miRNAs were well studied, we combined two experimentally validated databases (Mirtarbase, Tarbase). TargetScan and Miranda software were used to predict the target mRNAs of piRNA. Detailed miRanda software setting parameters were: Score threshold: ≥80; Energy threshold: ≤100 kcal/mol. To annotate and classify all genes of species and target genes of differentially expressed piRNAs, Gene Ontology (GO) functional analysis was performed. Kyoto Encyclopedia of Genes and Genomes (KEGG) is the major public pathway database. KEGG was used to analyze the pathways associated with differentially expressed piRNAs. The significance of enrichment of target genes of differentially expressed piRNAs in each pathway entry was calculated using a hypergeometric distribution test. Cytoscape software was used to construct the piRNA-miRNA interaction network maps (Su, Morris, Demchak, & Bader, 2014).

Statistical Analyses

SPSS 23.0 (IBM, SPSS, Chicago, IL, USA) was used for statistics and GraphPad Prism 9.5.1 (GraphPad Software, Inc. La Jolla, CA, USA) for graphs. Comparisons between groups were analyzed by Student's t-test, Wilcoxon signed rank test, and one-way analysis of variance (one-way ANOVA), as appropriate. The chi-squared test was used to analyze categorical data. Statistical significance was defined as a P value < 0.05.

Sequence characteristics of piRNAs

A total of 11,026 differential expressed piRNAs were detected in the whole blood samples by using the piRNA microarray chip (Attaystar, Aksomics, Ltd., China). The length of the piRNAs ranged from 26 to 32 nt, with a peak at 30 nt. Their first bases in sequences had strong U preference, 76.25% (Table 1, Fig. 1A). The average of G-C content of the piRNA is 50.13% (Table 2, Fig. 1B).

Table 1

Initiation base of piRNA
Length (nt)	Initiation base
Length (nt)	Adenine	Cytosine	Guanine	Uracil	Total
26	89	87	101	862	1139
27	70	105	113	834	1122
28	80	88	126	1078	1372
29	167	156	252	1964	2539
30	174	201	305	2180	2860
31	103	115	146	1166	1530
32	39	50	52	323	464
Total (Percentage, %)	722 (6.55%)	802 (7.27%)	1095 (9.93%)	8407 (76.25%)	11026 (100.00%)

Table 2

Base content of piRNA
Length (nt)	Base content (%)
Length (nt)	A	U	G	C	G-C
26	23.98	25.71	29.64	20.68	50.32
27	24.97	24.91	28.51	21.61	50.12
28	24.88	25.19	29.47	20.46	49.93
29	24.01	25.98	29.06	20.95	50.00
30	24.13	25.87	27.71	22.3	50.01
31	24.27	25.63	26.08	24.01	50.10
32	24.03	24.52	26.00	25.45	51.45
Average	24.27	25.60	28.08	22.05	50.13

Differential expression analysis of piRNAs and Multivariate analyses

PCA analysis showed that piRNA expression differed among the three groups (Fig. 1C). We evaluated the Pearson's correlation coefficient of the significantly dysregulated piRNA expression level among different groups, and the heat map of the inter-sample correlation showed that there was an obvious difference in the transcript expression levels between the LCMB vs. LC groups and LCMB vs. Ctrl groups. The differentially expressed piRNAs were screened by fold-change filtering (|log2(fold change)| > 1) and false discovery rate (FDR, ≤ 0.1). In the LCBM vs. LC, LCBM vs. Ctrl, and LC vs. Ctrl groups, 102, 345, and 35 differentially expressed piRNAs were identified, respectively. Twenty-four of these piRNAs were both differentially expressed in LCBM vs. LC and LCBM vs. Ctrl. Groups. They are listed in Table 3. Volcano plots showed the significantly differentially expressed piRNAs in these three groups (Fig. 2).

Table 3

The information of common differentially expressed piRNAs in LCBM vs LC.
piRBase	logFC	P.Value	FDR	Sequence	piRNA length
piR-hsa-385	2.369287	2.17E-05	0.071189	AAGAAGTGAAAATAGCCTTAACTGATGACATT	32
piR-hsa-2389	2.454675	0.001057	0.083821	TATCCAGGTCACACTGATGCAAGAGGTGG	29
piR-hsa-3789	1.988082	7.55E-05	0.071189	TCAGCATCATCGGCAACATCAACACGAA	28
piR-hsa-4734	2.484733	0.001109	0.084941	TCCAGACACATCTGAACATCTGAAGGAAC	29
piR-hsa-6584	1.753677	0.000742	0.079531	TCCTGAAGAACAGCCCCTTGGTGAGCCGCC	30
piR-hsa-7536	2.413625	0.000424	0.077187	TCTACGTCTGATTGATGTACCTGAAAGTGA	30
piR-hsa-11510	1.949472	0.000236	0.074689	TGAGTGACCTACAAAGAGACTTAGACTCCCA	31
piR-hsa-17027	2.09611	0.000264	0.074863	TGGAATGCCACCTTATTGTGGAATGGTC	28
piR-hsa-16644	-1.87486	0.00035	0.077187	TGGAGGGGTCAGATCCATCTGTGCAAGC	28
piR-hsa-17503	2.083701	0.00071	0.079531	TGGATAAAGAGTCAAGACCCGTCAGTGTGC	30
piR-hsa-17553	2.10327	0.001343	0.092952	TGGATAGATGCTACACACAGCTCAGCCCAGC	31
piR-hsa-18246	2.266402	0.000122	0.071189	TGGCCTGTTCCTGCCTTAACTGATGATGA	29
piR-hsa-19424	2.270623	0.000218	0.074689	TGGGCTCGCTCTGTGGCTCTCAGGGAGGC	29
piR-hsa-20039	2.651422	0.000273	0.075317	TGGTACCAGCCACTGCAAAAACATACC	27
piR-hsa-22035	1.536176	0.000492	0.077187	TGTGCTGTGTCCACTCAGGGTTAAATGGA	29
piR-hsa-23741	3.196955	0.000694	0.079531	CTACATCTGATTGGTGTACCTGAAAGTGA	29
piR-hsa-25207	1.627884	0.001246	0.090409	TTGAGAAATCGGATGGTTGCCGTGTCTGTGTA	32
piR-hsa-27059	1.783763	0.000667	0.079531	GATCCATCTGTGCAAGCCCACAACCAC	27
piR-hsa-27081	2.966723	0.000581	0.077187	GATGAAGGCCGCCGAATGACCTGGAAGC	28
piR-hsa-29004	3.506726	0.000635	0.079531	TAAACCACGCGAAACTCCTGCAGTCA	26
piR-hsa-29260	2.921601	1.58E-06	0.017432	TAAATGTAATCCAGCATATAAACAGAGCCA	30
piR-hsa-30507	3.158682	0.000129	0.071189	TACCTGAAAGTAATGGGTAGAATGGAACCAA	31
piR-hsa-30696	3.206813	0.000167	0.071565	TACTATGCAGCCATAAAAAAATGATGAGTTC	31
piR-hsa-31170	2.362911	0.000564	0.077187	TAGACCAATAACAGGATCTGAAGTTGTGG	29

Validation of differential expressed piRNAs

To verify the microarray data, qRT-PCR was performed for 24 dysregulated piRNA genes with statistically significant differences among all three groups. The expression levels of these piRNAs were measured in 10 samples per group. Confirming the reliability of the array results, the qRT-PCR results were highly consistent with the microarray data (Table 4, Fig. 3).

Table 4

Fold Change of twenty-four differentially expressed piRNAs
	LCBM vs Ctrl		LCBM vs LC
	Array	qPCR	Array	qPCR
piR-hsa-385	1.57	1.29	1.92	0.85
piR-hsa-2389	1.89	3.97	1.70	3.24
piR-hsa-3789	1.72	1.59	1.84	1.16
piR-hsa-4734	1.42	1.49	1.63	1.64
piR-hsa-6584	1.20	1.16	1.22	1.07
piR-hsa-7536	1.73	1.58	1.62	1.59
piR-hsa-11510	1.32	1.88	1.34	2.01
piR-hsa-17027	1.59	1.24	1.63	2.00
piR-hsa-17503	1.38	1.93	1.36	1.90
piR-hsa-17553	1.47	1.10	1.75	1.32
piR-hsa-18246	1.45	1.24	1.71	0.71
piR-hsa-19424	1.15	2.85	1.22	2.49
piR-hsa-20039	1.53	1.29	1.74	1.93
piR-hsa-22035	1.43	2.03	1.37	1.52
piR-hsa-23741	1.97	1.25	1.83	1.49
piR-hsa-25207	1.35	2.15	1.31	2.71
piR-hsa-27059	1.58	0.77	1.71	1.76
piR-hsa-27081	1.90	1.86	1.76	1.43
piR-hsa-29004	2.87	1.18	2.04	1.33
piR-hsa-29260	1.58	1.80	2.07	1.92
piR-hsa-30507	1.74	1.69	1.96	1.61
piR-hsa-30696	1.70	2.14	1.91	2.01
piR-hsa-31170	2.10	1.35	1.88	1.69
piR-hsa-16644	0.66	1.12	0.62	0.64

Go and KEGG analysis

We predicted the targets of these 24 dysregulated piRNA genes. To predict piRNA functions and molecular interactions among genes, GO and KEGG analyses of differentially expressed piRNAs were performed. GO analysis covered three areas: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). The enriched GO terms in biological process, cellular component, and molecular function are shown in Fig. 4. The most significantly enriched GO terms in biological process, cellular component, and molecular function categories were regulation of RNA polymerase II promoter, cell membrane, and RNA polymerase II transcription factor activity, respectively. In particular, the RNA polymerase II-related was enriched several times.

The results of KEGG pathway analysis are shown in Fig. 5. The target genes of differentially expressed piRNAs were mainly associated with ErbB signaling pathway, EGFR tyrosine kinase inhibitor resistance, FoxO signaling pathway, Ras signaling pathway, and so on. Notably, the target genes of differentially expressed piRNAs were associated with osteoclast differentiating pathways. This result indicated that these dysregulated piRNAs highly correlated with lung cancer bone metastasis.

Target prediction and functional interaction analysis

We set the threshold of score ≥ 80 and energy ≤ 100 kcal/mol of selected 24 piRNAs to generate the most likely piRNA-mRNA target pairs. Cytoscape software was used to visualize the piRNA-mRNA network in this study. Prioritized piRNA-mRNA target pairs were integrated into a piRNA-mRNA network on the basis of their correlation, resulting in the LCBM-specific piRNA interactome (Fig. 6A). Twenty-four misregulated piRNAs interacted with 453 target genes, reflecting piRNAs binding to many genes. As shown in Fig. 6B, the network consists of one large cluster of interrelated piRNA-mRNA pairs, which is the downregulated piRNA piR-has-16644 (green node). The up-regulated piRNAs consist of four medium-sized clusters, which are the piR-has-25207, 19424, 2389, and 22035 (red nodes). Notably, twenty-five of these target mRNAs are involved in the EGFR tyrosine kinase inhibitor resistance pathway and forty-two are involved in the osteoclast differentiation pathway.

NSCLC is one of the most osteotropic malignancies. Approximately 30% of patients with early-stage disease will subsequently develop skeletal metastases, and up to 60% of patients with advanced tumors will develop metastases(Siegel, Miller, Fuchs, & Jemal, 2021). Metastatic NSCLC is also referred to as stage 4 NSCLC. Cancer that has spread to the bones often has a poor prognosis. It's generally not considered curable. The quality of life for people with BoM is drastically reduced. According to the ACS, the 5-year relative survival rate of NSCLC patients with metastases is 8%. Only about half of the people with metastatic NSCLC will live for more than 14 to 17 months(Knapp, Devarakonda, & Govindan, 2022). Predictive factors that have been evaluated include patient-related factors, tumor-related factors, molecular alterations and serum biomarkers. Although the development and progression of bone metastases remains challenging, accurate prediction is necessary. The development of new biomarkers and new therapeutic strategies may allow earlier intervention to prevent the development of SRE.

The detection of tumor markers in blood has been advocated for cancer screening. It is easy, affordable and non-invasive. Recent studies have shown abnormal non-coding RNA profiles in the serum/plasma of patients with non-small cell lung cancer compared to healthy individuals. In addition to lncRNAs and miRNAs, piRNAs, with a length of 26 to 30 nucleotides, are an important class of non-coding RNAs(Parker et al., 2005). Their involvement in human malignancies has been increasingly demonstrated. Dysregulated piRNA expression, which contributes to tumor cell proliferation, migration, invasion, stemness and drug resistance, has been reported in multiple tumor tissue types. piRNA into the extracellular space in the form of free, protein-bound or exosome-encapsulated molecules that may enter serum, plasma, saliva and other body fluids(J. Li et al., 2021). Due to the characteristic 2′-O-methyl modification at their 3′ ends, piRNAs in body fluids may be more resistant to oxidation and degradation than miRNAs(S. Li et al., 2018). Entry of tumor cells into the bloodstream is the main cause of BoM in cancer. Given these features, circulating piRNAs should be considered as a novel potential source of noninvasive cancer screening biomarkers, especially for NSCLC with BoM patients(Yan & Bu, 2021).

In our study, we analysed the AUCG content of piRNA including in the array. The G-C content of the piRNA is lower than 20%. GC pairs are generally more stable than AT pairs; GC-rich genomes were proposed to be more adapted to high temperatures than AT-rich genomes. Previous studies showed replication but not transcription changes concentrate in GC-poor regions(Wu et al., 2018). GC deficiency may has an impact on the stability of piRNA binding to the target, resulting in alteration of target gene function and stability. It might be related to tumorigenesis or metastasis. We selected piRNAs for validation with statistically significant differences in the comparison between the two groups. The qPCR results were highly consistent with the microarray data, indicating that the array results are credible and suitable for the screening of differentially expressed piRNAs in whole blood sample.

Epidermal growth factor receptor (EGFR) gene mutations in NSCLC are important for the selection of appropriate targeted therapies. NSCLC with BoM is highly associated with EGFR mutation. Meta-analysis showed that EGFR-targeted therapy is beneficial to BoM of NSCLC patients carrying EGFR mutation(Siegel et al., 2021). A 2020 retrospective analysis of 570 patients with NSCLC patients found increased risk of bone and lung metastases in patients with EGFR and HER2 mutations compared with patients with gene fusions, RAS mutations, or mutations without an identified oncogene driver (P < 0.001)(Patil et al., 2020). The KEGG analysis indicated that there are 22 target genes of piR-has-16644 were associated with resistance to EGFR inhibitors, and 3 target genes of piR-has-11510 that were associated with resistance to EGFR inhibitors. It suggested that these two piRNAs may interfere with the efficacy of EGFR targeted therapy. Notably, the expression of piR-has-11510 in peripheral blood is the highest in the 24 dysregulated piRNA genes. Therefore, it has the potential to become a circulating blood biomarker due to its ease of detection.

Osteoclast differentiation is a key step in BoM. The KEGG enrichment analysis revealed the dysregulation of several signaling pathways, in particular those involved in osteoclast differentiation. Our data indicated that piR-has-16644 has 37 target genes involved in osteoclast differentiation, including NFKB2, PIK3R2, CSF1R, NOTCH2, SIPRA, RELA, TGFBR2, and so on, which are the important molecules in osteoclast differentiation pathway(Knapp et al., 2022; Mun, Park, & Park-Min, 2020). Notch signaling in lung cancer cells triggers the secretion of the major osteoclastogenic factors, RANKL and CSF, which is enhanced in the presence of stromal cells(Delgado-Calle & Bellido, 2022). In turn, RANKL derived from cancer cells leads to the up-regulation of its receptor, RANK, and of Notch2 in pre-osteoclasts(Wen, Wang, Guo, & Liu, 2023). In our result showed that piR-has-16644 has the largest number of targets, it suggests that it may be highly relevant to bone metastasis pathology.

Due to the limited sample size, to improve the reliability of piRNAs as a biomarker, we will collect more samples in future studies. we will verify whether the expression of piRNAs in tumor tissues is consistent with matched peripheral blood. Meanwhile, the exact regulatory mechanism of dysregulated piRNA in LC and LCBM is still unknown. Further functional and mechanistic studies of piRNAs are needed, which are being undertaken in our laboratory while we present this study.

In conclusion, our study revealed the expression profiles and potential functions of peripheral blood piRNAs. Numerous piRNAs were found to be dysregulated in LCBM compared to LC and Ctrl samples. These piRNAs were identified by high-throughput sequencing and qRT-PCR validation. These dysregulated piRNAs are likely to play important functional roles in LC bone metastasis. Importantly, our findings may offer a potential mechanism of EGFR inhibitor resistant therapy, especially in NSCLC with BoM patients. The expression of piR-has-11510 was increased in the blood of patients with LCBM. It was associated with resistance to target therapy. piR-has-16644 may be involved in the osteoclast differentiation pathway, and may serve as a potential biomarker and therapeutic target for the diagnosis and treatment of LCBM. Our finding that piRNAs from peripheral circulating blood can discriminate NSCLC patients with BoM potential illustrates its significant translational potential as a circulating biomarker for diagnosing NSCLC. Furthermore, understanding the biological functions of piRNAs and finding other potential applications in future studies would be of great interest.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request

Author contribution

W.F. and W.C. conceived the study and wrote the main manuscript text. H.M. and F.D. collected the patients samples and information. M.L. conducted the data analyses. K.L performed the experiments. Y.Z. and S.J revised the paper. All authors reviewed the manuscript.

Conflict of interest

There is no conflict of interest to declare.

Funding declaration

This work was supported by grants from the National Natural Science Foundation of China (82104497).

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Altered Piwi-RNA Profiles of Peripheral Blood in Bone Metastasis of Non-Small Cell Lung Cancer

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