Identification of microRNA editing sites in clear cell renal cell carcinoma

Liu, Yulong; Guo, Shiyong; Xie, Wenping; Yang, Huaide; Li, Wanran; Zhou, Nan; Yang, Jun; Zhou, Guangchen; Mao, Chunyi; Zheng, Yun

doi:10.1038/s41598-023-42302-y

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Published: 13 September 2023

Identification of microRNA editing sites in clear cell renal cell carcinoma

Yulong Liu¹,
Shiyong Guo²,
Wenping Xie^2,3,
Huaide Yang^2,4,
Wanran Li^1,2,
Nan Zhou²,
Jun Yang⁵,
Guangchen Zhou²,
Chunyi Mao^1,2 &
…
Yun Zheng²

Scientific Reports volume 13, Article number: 15117 (2023) Cite this article

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Abstract

Clear cell renal cell carcinoma (ccRCC) is a malignant tumor originating from the renal tubular epithelium. Although the microRNAs (miRNAs) transcriptome of ccRCC has been extensively studied, the role of miRNAs editing in ccRCC is largely unknown. By analyzing small RNA sequencing profiles of renal tissues of 154 ccRCC patients and 22 normal controls, we identified 1025 miRNA editing sites from 246 pre-miRNAs. There were 122 editing events with significantly different editing levels in ccRCC compared to normal samples, which include two A-to-I editing events in the seed regions of hsa-mir-376a-3p and hsa-mir-376c-3p, respectively, and one C-to-U editing event in the seed region of hsa-mir-29c-3p. After comparing the targets of the original and edited miRNAs, we found that hsa-mir-376a-1_49g, hsa-mir-376c_48g and hsa-mir-29c_59u had many new targets, respectively. Many of these new targets were deregulated in ccRCC, which might be related to the different editing levels of hsa-mir-376a-3p, hsa-mir-376c-3p, hsa-mir-29c-3p in ccRCC compared to normal controls. Our study sheds new light on miRNA editing events and their potential biological functions in ccRCC.

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Introduction

Renal cell carcinoma (RCC), which is formed by malignant proliferation of tubular epithelial cells, is the most common malignant tumor of kidney¹. According to the histological classification, there are many subtypes of RCC, among which clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype, accounting for about 75% of all RCCs¹. Although some ccRCC cases can be surgically resected, the metastatic rate of ccRCC is high, and about 30% of patients who present with metastases at first screening are not candidates for surgery^2,3. Therefore, early detection and targeted therapy of ccRCC is the most effective way to reduce the number of deaths from this disease⁴.

MicroRNAs (miRNAs) are a class of single-stranded non-coding RNAs with approximately 22 nucleotides that can undergo extensive post-transcriptional modifications^5,6. As important regulatory molecules in biological processes, miRNAs are involved in almost all cellular pathways and pathological processes, including cancer initiation, progression and metastasis⁷. Mature miRNAs pair with their target mRNAs through the seed regions (the first eight nucleotides from the 5\({\prime }\) ends of miRNAs) to induce mRNA degradation or translational repression⁸. The regulation of gene expression by miRNAs is diverse and complex. A miRNA can bind to many different targets, and a target can be regulated by multiple miRNAs⁹. Therefore, even a single nucleotide change in a miRNA, especially if it occurs within the seed region, leads to severe changes of its targets and also affects the expression of its targets.

In ccRCC, miRNAs are reported as either oncomiRNAs or tumor suppressors¹⁰. For example, hsa-miR-21-5p was overexpressed in ccRCC tissues compared to normal controls and correlated with the downregulation of PPAR-\(\alpha\) in ccRCC¹¹. Ji et al. reported that hsa-miR-155-5p was upregulated in ccRCC, and inhibition of hsa-miR-155-5p could significantly suppress the proliferation, colony formation, migration and invasion and induce G1 phase arrest and apoptosis¹². In addition, hsa-miR-193a-3p and hsa-miR-224-3p as oncogenic miRNAs¹³, activated the PI3K/Akt pathway and targeted glycosylation-related enzymes to mediate cell proliferation, migration and invasion in ccRCC. hsa-miR-30a-5p¹⁴ and hsa-miR-30c-5p¹⁵ were downregulated in ccRCC, and were associated with tumor aggressiveness¹⁶. Cui et al. found that hsa-miR-99a-5p was downregulated in ccRCC and correlated with overall survival¹⁷. Moreover, hsa-miR-187-3p presents lower levels in tumor tissue and plasma of patients with ccRCC, and lower levels of hsa-miR-187-3p were associated with higher tumor grade and stage¹⁸. In summary, a large number of miRNAs play crucial roles in the initiation and progression of ccRCC.

RNA editing is a prevalent and conserved post-transcriptional mechanism that plays a crucial role in the diversification of gene expression and transcriptome complexity across various organisms. This process involves specific proteins that catalyze chemical modifications to RNA molecules during their generation processes, leading to the alterations, deletions, and/or insertions of bases^19,20. RNA is edited in a variety of ways. The most widely reported editing type is adenosine to inosine (A-to-I) editing, catalyzed by adenosine deaminase acting on RNA (ADAR) enzymes¹⁹. This is followed by editing of cytosine (C) to uracil (U), which is catalyzed by the apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) proteins^19,20. Furthermore, some RNAs are modified on their 3\({\prime }\) ends to add additional nucleotides, such as A or U^21,22,23.

It has been found that a large number of RNA editing events also exist in miRNAs^{24,25,26,27,28,29,30,31,32,33,34,35,36,37} and the A-to-I miRNA editing events in cancer have attracted some attention^38,39,40. For example, ADAR2 is responsible for editing hsa-miR-21-3p/5p, hsa-miR-221-5p and hsa-miR-222-3p, which exhibit high levels and are known to promote cancer progression in glioblastoma⁴¹. In a study on miRNA editing in pan-cancer, the edited hsa-miR-200b-3p promotes cell invasion and migration by targeting LIFR, a metastasis suppressor³⁹. A-to-I edited hsa-miR-589-3p targets ADAM12 and original hsa-miR-589-3p targets PCDH9, a tumor suppressor related to glioma progression⁴². A-to-I editing level of hsa-miR-589-3p is reduced in glioblastoma, which promotes the proliferation and invasion of brain cancer cells⁴². In addition, miRNA editing also plays an important role in neurological diseases. For example, A-to-I editing of hsa-miR-497-5p is enhanced in Parkinson’s disease (PD), which potentially promotes progressive neurodegeneration of PD patients³⁷. In summary, miRNA editing is associated with the initiation and progression of numerous cancers. However, miRNA editing events in ccRCC are largely unknown, which has become a major obstacle and blind spot for the study of ccRCC.

In this study, to comprehensively characterize miRNA editing and/or modification sites in ccRCC, we systematically analyzed small RNA sequencing profiles of 154 ccRCC patients and 22 healthy controls. We identified 1025 miRNA mutation and/or editing (M/E) sites with significant editing levels in these samples. In addition, 122 M/E sites showed significantly different editing levels in ccRCC patients compared to control samples. Among them, we focused on 3 editing events that occurred in the seed regions and predicted the target genes of their corresponding edited miRNAs. We explored the potential functions of new target genes of edited miRNAs and examined expression patterns of these new targets in ccRCC using existing gene expression data. Our results suggested that A-to-I edited hsa-mir-376a-1-3p and hsa-mir-376c-3p targeted BCAT1 and MTHFD2, respectively, and C-to-U edited hsa-mir-29c-3p targeted RASSF8. Presumably, because the A-to-I editing levels of hsa-mir-376a-1-3p and hsa-mir-376c-3p were reduced in ccRCC, BCAT1 and MTHFD2 were upregulated in ccRCC. Similarly, potentially due to increased C-to-U editing level of hsa-mir-29c-3p, RASSF8 was significantly downregulated in ccRCC. These results suggest that miRNA editing contributes to initiation and progression of ccRCC.

Results

Summary of mutation and editing sites identified in miRNAs

The MiRME pipeline⁴³ was used to identify miRNA M/E sites from the collected 176 sRNA-seq profiles. In total, 1025 M/E sites with significant editing levels were identified (as listed in Supplementary Table S2.1). As mentioned above, these M/E sites were divided into 9 categories (Fig. 1A and Supplementary Table S2.2). We found that there were more 3\({\prime }\)-addition sites than 5\({\prime }\) editing sites (Fig. 1A and Supplementary Tables S2.3, S2.4). In addition, M/E sites in the central regions of mature miRNAs include 9 (0.88%) A-to-I, 30 (2.93%) C-to-U, 5 (2.54%) Other and 2 (0.20%) SNP sites.

We further investigated the miRNA editing events that occured in the central regions of mature miRNAs (Supplementary Table S2.5), i.e., A-to-I, C-to-U and Other. We counted the base changes of these three editing types (Fig. 1B), and found 9 A-to-I, 30 C-to-U, and 5 Other editing events. One insertion and two deletion events were also found (Fig. 1B). Furthermore, we also investigated the distributions of these events within different regions of mature miRNAs, including the 5\({\prime }\) ends, 3\({\prime }\) ends, and central regions. Our results indicated that 3\({\prime }\)-addition was the most common type of RNA editing events of miRNAs. As shown in Fig. 1C, the numbers of editing events at the 3\({\prime }\) ends were significantly higher than those at the 5\({\prime }\) ends and the central regions. For the vast majority of miRNAs, only 1 or 2 editing events happened at the 5\({\prime }\) ends and the central regions.

Nine miRNA A-to-I editing sites in ccRCC

Totally, 9 A-to-I editing sites with significant editing levels were identified from the 176 samples (Fig. 2A). The editing levels of hsa-mir-376a-1 and hsa-mir-376a-2 were almost identical, probably due to their high sequence similarity. We then counted the nucleotides around these 9 A-to-I editing sites, and found that the 5\({\prime }\) and 3\({\prime }\) ends of these A-to-I editing sites had clear U and G base preference, respectively (Fig. 2B), which was consistent with results in literature^27,29,43. As examples, the detailed information of two A-to-I editing sites in hsa-mir-411 and hsa-mir-7977 in two ccRCC samples (SRR11873730 and ERR4367258, respectively) were presented in Fig. 2C and D, respectively. As shown in Fig. 2E and F, a large number of sequencing reads in these samples supported the two A-to-I editing sites.

Thirty miRNA C-to-U editing sites in ccRCC

We carefully studied the 30 C-to-U editing sites and found that their editing levels were generally low in the 176 sample (Fig. 3A). Furthermore, nucleotides on the 5\({\prime }\) sides of these C-to-U editing sites were biased toward C, while those on the 3\({\prime }\) sides were biased toward A (Fig. 3B). Examples of two C-to-U editing sites were shown in Fig. 3C and D, respectively. In addition, the detailed editing sites and editing levels of these two miRNAs were shown in Fig. 3E and F, respectively. These two C-to-U editing sites were supported by thousands of sequencing reads in the samples used, indicating good reliabilities of these two editing sites.

Two miRNA SNP sites in ccRCC

The identified M/E sites were compared with SNPs in miRNAs previously reported⁴⁴ and NCBI dbSNP (v151) according to the criteria mentioned in Materials and Methods. Two SNPs (rs11614913 and rs2155248) were shown in Supplementary Fig. S1 and Supplementary Table S2.6, and the editing levels of these two SNPs in one normal and one ccRCC sample, respectively, were shown in Supplementary Fig. S1A. As shown in Supplementary Fig. S1B and S1C, these SNPs were supported by many sequencing reads in SRR11873716 and SRR11873719, respectively. Additionally, the editing levels of these two sites reached 100% (Supplementary Fig. S1D and S1E), consistent to the fact that these sites were SNPs.

122 M/E sites with significantly different editing levels in ccRCC

We obtained 122 miRNA M/E sites with significantly different editing levels in ccRCC samples (n = 154) compared to normal controls (n = 22) by using Mann-Whitney U-tests (corrected \(p < 0.05\)) (Supplementary Table S3). The 122 miRNA M/E sites were divided into 7 categories. 112 of these 122 sites were 3\(^{\prime }\)-addition events (3\(^{\prime }\)-A, 3\(^{\prime }\)-U, and 3\(^{\prime }\)-Other). In addition, there were 5 A-to-I editing sites, 2 C-to-U editing sites, and the remaining two were 5\(^{\prime }\) end sites and one Pseudo site, i.e., potential false positive site (Fig. 4A). Most of the 122 M/E sites had increased editing levels (77 M/E sites, 63.1%) and 45 M/E sites (36.9%) had decreased editing levels in ccRCC samples when compared to normal controls (Fig. 4B). The categories of M/E sites with increased and decreased editing levels in ccRCC samples were shown in Fig. 4C. In M/E sites with increased editing levels in ccRCC, the 3\({\prime }\)-A editing events accounted for the largest portion, while the 3\({\prime }\)-U editing events accounted for the largest part in those with decreased editing levels in ccRCC compared to normal controls.

In ccRCC, the editing levels of hsa-mir-376a-1_49_A_g and hsa-mir-376c_48_A_g were significantly decreased (Fig. 4D), while those of hsa-mir-6503_59_A_g and hsa-mir-29c_59_u were significantly increased (Fig. 4D and E, respectively). We also investigated the levels of these edited miRNAs and found that hsa-mir-376a-1_49g and hsa-mir-376c_48g were significantly downregulated in ccRCC samples, while the levels of hsa-mir-6503_59g and hsa-mir-29c_59u were significantly upregulated in ccRCC (Fig. 4F and G, respectively). Figure 4H–M illustrated the details of the three edited miRNAs, hsa-mir-376a-1_49_A_g, hsa-mir-376c_48_A_g and hsa-mir-29c_59_u in one ccRCC sample (SRR11873721), one normal sample (SRR11873716) and one ccRCC sample (ERR4367209), respectively.

The expression levels of ADAR and APOBEC genes in ccRCC

As shown in Supplementary Fig. S2 and Supplementary Table S7, the expression levels of the ADAR genes that catalyzed A-to-I editing of miRNAs were examined. In the TCGA data, ADAR1 was downregulated in ccRCC compared to normal controls (Supplementary Fig. S2A) which was consistent with the decreased editing levels of hsa-mir-376a-1_49_A_g, hsa-mir-376a-2_55_A_g, and hsa-mir-376c_48_A_g. However, in the other two cohorts of gene expression profiles, ADAR1 was upregulated, suggesting that more researches were needed to reveal the role of ADAR1 in the editing of miRNAs in ccRCC. We found that ADAR2 was significantly upregulated in ccRCC samples compared to normal controls in the three selected cohorts of gene expression datasets (Supplementary Fig. S2B). Meanwhile, the editing level of hsa-mir-6503_59_A_g was significantly increased in ccRCC samples compared to normal controls (Fig. 4D), suggesting that ADAR2 mediated the editing of hsa-mir-6503 in ccRCC and contributed to its increased editing levels in ccRCC compared to normal controls. ADAR3 was downregulated in one cohort of gene expression profiles, and had no significant change in the other two datasets (Supplementary Fig. S2C).

The expression of the APOBEC genes that mediated C-to-U editing of miRNAs was also examined. As shown in Supplementary Fig. S3 and Supplementary Table S8, the expression levels of most APOBEC family members showed a significantly upward trend in ccRCC tumors, including AICDA, APOBEC3A, APOBEC3B, APOBEC3C, APOBEC3D, APOBEC3F, APOBEC3G and APOBEC3H genes (Supplementary Fig. S3A, D–J, respectively). In our results, the editing level of hsa-mir-29c_59_C_u was significantly increased in ccRCC samples (Fig. 4E). More researches are needed to clarify which APOBEC gene is the most promising enzyme that mediates the C-to-U editing of hsa-mir-29c_59_C_u and other miRNA C-to-U editing sites in ccRCC.

The expression levels of TENT genes in ccRCC

Terminal nucleotidyltransferases (TENTs) modify RNA at post-transcriptional level, which regulates the stability and activity of RNA⁴⁵. TENT2 and TENT4B proteins catalyze 3\({\prime }\) end adenylation of miRNAs^{21,46,47,48,49,50}. TUT4 and TUT7 proteins are terminal uridine transferases that belong to the TENT3 subfamily, which have the ability to catalyze the 3\({\prime }\) uridylation of miRNAs^23,51,52,53. The levels of these TENT genes in ccRCC and normal controls were examined (Supplementary Table S9). In ccRCC, the level of TENT2 was significantly higher (Supplementary Fig. S4A–C), while TENT4B was significantly downregulated (Supplementary Fig. S4A and C). There are more 3\({\prime }\)-A sites with increased editing levels in ccRCC than 3\({\prime }\)-A sites with decreased editing levels in ccRCC (Fig. 4C), suggesting that TENT2 might be the major enzyme for the 3\({\prime }\)-A editing of miRNAs in ccRCC, while the downregulation of TENT4B could be responsible for the decreased 3\({\prime }\)-A editing levels of some miRNAs in ccRCC.

The levels of TUT4 had no significantly different expression in ccRCC compared to normal controls (Supplementary Fig. S4A to C), but the expression of TUT7 was significantly upregulated in ccRCC (Supplementary Fig. S4A–C). These results suggest that some 3\({\prime }\)-U editing sites of miRNAs with increased editing levels in ccRCC (Fig. 4C, left column) are mainly catalyzed by TUT7.

Identification of targets for A-to-I and C-to-U edited miRNAs

Three M/E sites (hsa-mir-376a-1_49_A_g and hsa-mir-376c_48_A_g and hsa-mir-29c_59_C_u) in seed regions of mature miRNAs were selected for subsequent analysis, because these sites had significantly different editing levels in ccRCC and the levels of their corresponding edited miRNAs were also significantly different and in the same directions of changes when compared their editing levels in ccRCC with normal controls.

To decrease false positives and increase the reliabilities of predicted miRNA targets, we used 11 PAR-CLIP sequencing profiles. The principle of PAR-CLIP (Photoactivatable Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation) technology is used to detect the crosslinking of RNA-binding proteins (RBPs) to RNA molecules, followed by immunoprecipitation and sequencing to identify the binding sites⁵⁴. Through PAR-CLIP experiments, the interaction between RISC (including AGO proteins and miRNAs) and mRNA can be identified⁵⁴. The sequences of the miRNA binding sites were obtained through PAR-CLIP experiments (targeting one of the AGO proteins) and then were sequenced⁵⁴. Next, the MiCPAR algorithm⁵⁵ was used to identify the targets of original and edited miRNAs by analyzing 11 PAR-CLIP profiles of AGO proteins (see Methods for details). We found that the original and edited hsa-mir-376a-1-3p had 102 common target genes, and 484 new target genes of hsa-mir-376a-1_49g were found (Fig. 5A and Supplementary Tables S4.1–S4.3). The original and edited hsa-mir-376c-3p had 107 common target genes, and hsa-mir-376c_48g had 568 new target genes (Fig. 5B and Supplementary Tables S5.1–S5.3). The original and edited hsa-mir-29c-3p had 107 identical target genes, and hsa-mir-29c_59u had 480 new target genes (Fig. 5C and Supplementary Tables S6.1–S6.3). Next, we compared the new target genes of hsa-mir-376a-1_49g, hsa-mir-376c_48g and hsa-mir-29c_59u to deregulated genes in ccRCC. We found that in the three gene expression datasets selected (GSE151419, GSE126964 and TCGA), 88 and 82 target genes of hsa-mir-376a-1_49g and hsa-mir-376c_48g were commonly significantly upregulated in ccRCC (Fig. 5D,E and Supplementary Tables S4.4, S5.4), respectively. Additionally, 51 target genes of hsa-mir-29c_59u were commonly significantly downregulated in ccRCC (Fig. 5F and Supplementary Table S6.4).

The numbers of PAR-CLIP reads for the 88, 82 and 51 overlapped target genes in Fig. 5D–F were shown in Fig. 5G–I, respectively. Then, among these target genes of hsa-mir-376a-1_49g, hsa-mir-376c_48g, and hsa-mir-29c_59u, genes with more than 10 PAR-CLIP reads with T-to-C variations were retained for further analysis (Supplementary Tables S4.5, S5.5, S6.5, respectively). Among the retained target genes of hsa-mir-376a-1_49g, hsa-mir-376c_48g and hsa-mir-29c_59u, BCAT1, MTHFD2 and RASSF8 were further analyzed after examining their functional relevance in ccRCC by reading their literature, respectively.

Figure 6A–C showed the distribution of PAR-CLIP reads for BCAT1, MTHFD2 and RASSF8, respectively. These results indicated that PAR-CLIP reads were significantly accumulated at the complementary sites of hsa-mir-376a-1_49g, hsa-mir-376c_48g and hsa-mir-29c_59u, respectively. The identified miRNA complementary sites and their P\(_{s}\) values on BCAT1, MTHFD2 and RASSF8 were shown in Fig. 6D–F, respectively. The complementary sites of hsa-mir-376a-1_49g, hsa-mir-376c_48g and hsa-mir-29c_59u were located in the 3\(^{\prime }\)UTR, CDS and 3\(^{\prime }\)UTR of BCAT1, MTHFD2 and RASSF8, respectively (as shown in Fig. 6G to 6I, respectively). BCAT1 and MTHFD2 were upregulated in ccRCC, while RASSF8 was downregulated in ccRCC (Supplementary Fig. S5 and Supplementary Tables S10.1–S10.3). Figure 6J–L showed the details of complementary sites of hsa-mir-376a-1_49g, hsa-mir-376c_48g and hsa-mir-29c_59u on BCAT1 (NM_001178093.1), MTHFD2 (NM_006636.3) and RASSF8 (NM_001164746.1), respectively, as well as the PAR-CLIP reads at these sites.

Functional analysis of A-to-I and C-to-U edited miRNAs

We performed Gene Ontology (GO) and KEGG pathway enrichment analysis to investigate the functions of the new target genes that were commonly deregulated in the three gene expression datasets selected for hsa-mir-376a-1_49g, hsa-mir-376c_48g, and hsa-mir-29c_59u, respectively (Supplementary Fig. S6, Supplementary Tables S11.1–S11.3 and S12.1–S12.3). The target genes of hsa-mir-376a-1_49g were enriched in many cancer-related pathways, including “Transcriptional misregulation in cancer”, “Small cell lung cancer”, “Prostate cancer”, “Pathways in cancer”, “p53 signaling pathway”, “Non-small cell lung cancer”, “Melanoma”, “Glioma”, “Endometrial cance”, “Colorectal cancer”, “Chronic myeloid leukemia” and “Breast cancer” (Supplementary Fig. S6A). The target genes of hsa-mir-376c_48g were mainly enriched in the “Pathways in cancer” and the “Rap1 signaling pathway” (Supplementary Fig. S6C). Additionally, “Metabolic pathways” was the most enriched pathway of the 51 target genes of hsa-mir-29c_59u (Supplementary Fig. S6E). These results suggest that hsa-mir-376a-1_49g and hsa-mir-376c_48g are directly related to ccRCC by targeting genes in many cancer-related pathways, while hsa-mir-29c_59u seems to be involved in the deregulation of genes in metabolic pathways.

Discussion

We identified 1025 M/E sites with significant editing levels by analyzing 176 sRNA-seq profiles of ccRCC patients (n = 154) and healthy controls (n = 22). The M/E sites at the 3\(^{\prime }\) end accounted for the majority, which was consistent with previous work^{37,40,43,56,57,58,59}. We identified 122 editing events with significantly different editing levels in ccRCC samples compared to healthy control samples. The editing levels of 63.1% and 36.9% of these 122 miRNA editing events were increased and decreased in ccRCC patients, respectively, which may be related to the pathogenesis of ccRCC.

Subsequently, we focused on A-to-I and C-to-U miRNA editing events that occurred in the seed regions and were dysregulated in ccRCC. The editing levels of hsa-mir-376a-1_49g and hsa-mir-376c_48g were significantly lower in ccRCC than those in normal samples. In addition, their levels also showed the same significant downregulation in ccRCC samples compared to normal controls. The editing level of hsa-mir-29c_59_C_u and level of hsa-mir-29c_59u showed significant increases in ccRCC compared to normal controls. We compared the targets of original hsa-miR-376a-3p, hsa-miR-376c-3p and hsa-miR-29c-3p with the targets of edited miRNAs, hsa-mir-376a-1_49g, hsa-mir-376c_48g and hsa-mir-29c_59u. We found that hsa-mir-376a-1_49g, hsa-mir-376c_48g and hsa-mir-29c_59u could target 484, 568 and 480 additional novel targets, respectively.

C-to-U editing of miRNA was not widely reported. Interestingly, we detected 30 C-to-U editing events, much more than A-to-I editing events identified, in ccRCC. The more prevalence of C-to-U miRNA editing in ccRCC may be related to the high levels of APOBEC gene family in ccRCC (Supplementary Fig. S4). For examples, APOBEC3C, APOBEC3F and APOBEC3G were highly expressed in ccRCC samples compared to normal controls (as shown in Supplementary Figs. S4F, H and I, respectively). The fifth position of hsa-miR-100-5p was specifically edited in Treg⁶⁰. C-to-U edited hsa-miR-100-5p repressed a new target SMAD2, which reduced the fraction of Treg in peripheral blood mononuclear cells⁶⁰. The C-to-U editing of hsa-mir-29c_59_C_u in ccRCC might represent another case of functional C-to-U editing sites in miRNAs, because its editing levels significantly increased in ccRCC and the C-to-U edited hsa-mir-29c-3p potentially repressed RASSF8 in ccRCC. As 11 of these 30 C-to-U editing sites were in seed regions (Fig. 3 and Supplementary Table S2.5), more of these C-to-U editing might be functional in tissue- or time-specific manners.

In our study, BCAT1 and MTHFD2 were upregulated in ccRCC and identified as target genes of hsa-mir-376a-1_49g and hsa-mir-376c_48g in ccRCC, respectively. BCAT1 is overexpressed in many cancers and has been proposed as a marker for predicting cancer prognosis⁶¹. For example, overexpression of BCAT1 promotes tumor growth in renal clear cell carcinoma⁶², lung cancer⁶³, glioma⁶⁴, ovarian cancer⁶⁵, breast cancer⁶⁶, hepatocellular carcinoma^67,68, myeloid leukemia⁶⁹, gastric cancer^70,71, endometrial cancer⁷², non-small cell lung cancer⁷³ and pan-cancer⁷⁴. Moreover, BCAT1 and its metabolites participate in the metabolism of cancer cells through different mechanisms. For example, the PI3K/AKT/mTOR signaling pathway is activated by BCAT1 to promote the proliferation and angiogenesis of gastric cancer cells in vitro⁷⁰. At the same time, in the study of lung adenocarcinoma, BCAT1 promotes lung adenocarcinoma progression by enhancing mitochondrial function and NF-\(\kappa\)B pathway⁷⁵.

Recent reports have shown that MTHFD2 is also highly expressed in many cancers, including renal clear cell carcinoma^76,77, pancreatic cancer,⁷⁸ breast cancer⁷⁹, myeloid leukemia⁸⁰, non-small cell lung cancer^81,82, hepatocellular carcinoma⁸³, colorectal cancer⁸⁴, breast cancer^85,86, ovarian cancer⁸⁷ , bladder cancer⁸⁸, glioblastoma⁸⁹, nasopharyngeal carcinoma⁹⁰, oral squamous cell carcinoma⁹¹, and pan-cancer⁹². In addition, studies have reported the role of MTHFD2 in tumor immune evasion. MTHFD2 is induced by IFN-\(\gamma\) and promotes basal and IFN-\(\gamma\)-induced PD-L1 expression. Mechanistically, MTHFD2 drives folate circulation to maintain intracellular UDP-GlcNAc and cMYC O-GlcNAcylation, which promotes PD-L1 transcription⁹³. Huang et al. found that MTHFD2 promoted breast cancer cell proliferation through AKT signaling pathway, and high level of MTHFD2 reduced the survival rate of breast cancer patients⁷⁹.

In summary, the upregulation of BCAT1 and MTHFD2 in ccRCC may be due to the lower levels of hsa-mir-376a_49g and hsa-mir-376c_48g in ccRCC, respectively, which may contribute to the initiation and/or progression of ccRCC. Reduced A-to-I editing level of miRNAs were also noticed previously^38,39. The editing levels of hsa-mir-376a-1_49_A_g and/or hsa-mir-376c_48_A_g were also reduced in many other cancers, such as glioblastoma⁹⁴ and lung cancer⁹⁵. The increased expression of BCAT1 and MFTHD2 might be due to the reduced editing levels of hsa-mir-376a-1_49_A_g and hsa-mir-376c_48_A_g in many cancers. Furthermore, it has been shown that the higher the editing level of hsa-mir-376c_48_A_g site in ccRCC, the better the prognosis and the longer the survival time of patients³⁸. Therefore, reduced editing levels of hsa-mir-376a-1_49_A_g and hsa-mir-376c_48_A_g might represent a general mechanism in the initiation and/or progression of different cancers.

RASSF8 has been reported to be a tumor suppressor gene with lower expression in gastric cancer^96,97, colorectal cancer^98,99, cervical cancer¹⁰⁰, ovarian cancer¹⁰¹, melanoma¹⁰², esophageal squamous cell carcinoma¹⁰³, lung cancer^104,105 and osteosarcoma¹⁰⁶. In addition, RASSF8 is a potential therapeutic target for the prevention of many cancers^{97,100,103,105}. In this study, RASSF8, the target gene of hsa-mir-29c_59u, was downregulated in ccRCC. Therefore, RASSF8 may also play similar tumor suppressor function in ccRCC, and the higher level of hsa-mir-29c_59u in ccRCC may lead to the decreased expression of RASSF8 in ccRCC, which consequently promotes the initiation and/or progression of ccRCC.

As shown in Fig. 5G–I, some genes with many PAR-CLIP reads were identified as targets of hsa-mir-376a-1_49g and hsa-mir-376c_48g, and hsa-mir-29c_59u, respectively. Although there currently are limited evidences about their functions in ccRCC, our results suggest that these genes represent promising directions for revealing the mechanisms of ccRCC in the future.

In summary, we presented a comprehensive view of miRNA editing and/or modification events in ccRCC, which promoted our understanding of miRNA editing and/or modification events in ccRCC. More researches are needed in the future to clarify the functional roles of different miRNA editing patterns in the pathogenesis of ccRCC. Our results also provided new clues for the clinical treatment of ccRCC, such as to promote the editing levels of hsa-mir-376a_49_A_g and hsa-mir-376c_48_A_g and to repress the editing level of hsa-mir-29c_59_C_u.

Methods

Small RNA-Seq data used

We collected 176 small RNA-Seq (sRNA-seq) profiles of ccRCC patients and healthy controls, including 154 samples of cancer tissue and 22 normal kidney tissue samples. These 176 sRNA-seq profiles were obtained from previous studies^107,108, and their accession numbers were shown in Supplementary Table S1.1. The qualities of these sRNA-seq sequence data were evaluated with FastQC (v0.11.9)¹⁰⁹.

Genome and annotation of miRNAs used

The human genome sequence used was GRCh38, and downloaded from the UCSC Genome Browser¹¹⁰. The pri-miRNA sequences in GFF3 format and mature miRNA annotation files were derived from miRBase (v21)¹¹¹.

Identification of mutation and editing sites in miRNAs

Totally, 176 sRNA-seq profiles were analyzed using the MiRME pipeline with default settings⁴³. Briefly, the raw reads were checked to retain the qualified reads with the sequencing scores greater than 30 for the first 25 nucleotides. Then, reads of at least 18 nt were retained after removal of the 3\({\prime }\) adapters. BLASTN¹¹² was used to compare the retained reads with the pre-miRNAs and the reads mapped to pre-miRNAs were retrieved. These reads, which were mapped to pre-miRNAs, were then aligned with the genome using Bowtie (v1.0.0)¹¹³. Then, the cross-mapping correction algorithm²⁸ was used to examine the alignment of reads to the genome to calculate the weights or percentages of reads at different genomic loci. Results from different samples were then combined using separate programs in the MiRME package^43,55. According to the location of M/E sites in miRNAs and mutations status of dbSNP, the M/E sites were divided into nine different editing types, i.e., A-to-I, C-to-U, 3\(^{\prime }\)-A, 3\(^{\prime }\)-U, 3\(^{\prime }\)-Other, 5\(^{\prime }\)-editing, Other, SNP and Pseudo⁴³.

The identified M/E site was named with the pre-miRNA name, M/E site position in pre-miRNA, original nucleotide in upper case, the edited/mutated nucleotide in lower case. For example, hsa-mir-376c_48_A_g means an A-to-I editing at the 48th nucleotide of hsa-mir-376c. And edited miRNA was named by the pre-miRNA name, the M/E site position in pre-miRNA, and the edited/mutated nucleotide in lower case. For example, hsa-mir-376c_48g is the A-to-I edited miR-376c-3p.

The criteria for defining M/E sites with significant editing levels were as follows: (i) the relative levels of editing were at least 5%; (ii) editing events were supported by at least 10 reads; (iii) the threshold for sequencing reads score was 30; (iv) multiple test corrected p-values (with the Benjamini and Hochberg method¹¹⁴) were smaller than 0.05.

In order to remove M/E sites due to random sequencing errors, 1025 M/E sites that had significant editing levels in at least 10% of the 176 samples (18 samples) used in this study were retained for further analysis. The identified M/E sites were compared with known human miRNA editing sites, including the DARNED database¹¹⁵, the RADAR database¹¹⁶ and relevant literature^{27,29,34,43,95,117,118}. Finally, A-to-I, C-to-U, and Other predicted M/E sites were manually checked.

Identification of SNPs sites

The identified M/E sites were compared with the dbSNP (v151) database¹¹⁹ and previously reported SNPs in miRNAs⁴⁴. An M/E site was considered as SNP if it satisfied the following criteria: (i) the genomic position of M/E site and SNP was identical, (ii) the original and edited nucleotides had the same nucleotides as the SNP’s allele, (iii) the M/E site had editing level of 100% in at least one of the 176 samples selected, and (iv) the M/E site occurred in the center of the miRNA.

Identification of M/E sites with significantly different editing levels in ccRCC

The Mann-Whitney U-tests were used to analyze the differences between the editing levels of 1025 miRNA M/E sites in ccRCC (n = 154) and normal samples (n = 22). The p-values were corrected with the Benjamini-Hochberg correction method¹¹⁴. The M/E sites with corrected p-values smaller than 0.05 were defined as having significantly different editing levels in ccRCC.

DESeq2¹²⁰ was used to analyze different expression levels (TPTM) of edited miRNAs between ccRCC and normal control samples. The miRNAs with corrected p-values smaller than 0.05 were defined as having different expression levels in ccRCC and normal control samples.

Identification of targets for original and edited miRNAs

Among the editing sites with significant differences in editing levels between ccRCC tumors and normal tissues, a total of five editing events occurred in seed regions of mature miRNAs. We chose two A-to-I editing sites (hsa-mir-376a-1_49_A_g and hsa-mir-376c_48_A_g) and one C-to-U editing site (hsa-mir-29c_59_C_u), because their editing levels and the levels of their corresponding edited miRNAs had significant differences in ccRCC tumor tissues compared to normal controls. Then, we predicted the targets of original and edited miRNAs using the MiCPAR algorithm⁵⁵. As shown in Supplementary Table S1.2, 11 PAR-CLIP sequences were used in the identification of miRNA targets and were downloaded from the NCBI SRA database. Seven PAR-CLIP profiles of HEK293 cell lines stably expressing FLAG/HA-tagged AGO1, AGO2, AGO3, and AGO4 proteins were reported by⁵⁴. Another study included four PAR-CLIP profiles derived from HEK293 cell lines stably expressing HIS/FLAG/HA-tagged AGO1 and AGO2 proteins¹²¹. In order to obtain reads of acceptable quality, the raw reads of these 11 PAR-CLIP profiles were filtered to keep reads with sequencing scores of at least 30 for their first 25 nucleotides from 5\({\prime }\) ends. The qualified reads were combined and the targets of miRNA were identified by the MiCPAR algorithm. The targets with at least one PAR-CLIP read with T-to-C variation were retained for further analysis.

Analyzing expression of targets of edited miRNAs

In order to understand the expression of targets of edited miRNAs in ccRCC tumor tissues, we identified genes that were significantly differentially expressed in ccRCC samples compared to normal samples. We collected three batches of gene expression profiles of ccRCC and controls (Supplementary Table S1.3), of which two batches were obtained from^107,122. Another set of gene expression profiles was obtained from TCGA (https://portal.gdc.cancer.gov/). The edgeR (v3.34.1) package¹²³ was used for differential analysis of target genes. The glmFit and glmLRT functions in edgeR were used to build generalized linear models and to perform likelihood ratio tests, respectively. Genes with corrected p-values smaller than 0.05 were defined as having significantly different expression levels in ccRCC.

Functional analysis of new target genes of edited miRNAs

We kept targets with at least one PAR-CLIP read with T-to-C variation. Then, the targets of original and edited miRNAs were compared to obtain the new targets of edited miRNAs. For edited miRNAs with increased editing levels in ccRCC, we compared their targets with genes that were downregulated in ccRCC because miRNAs normally repressed their target genes, and vice versa. We also analyzed the numbers of PAR-CLIP reads for the targets of edited miRNAs. We manually examined the functional relevance of some selected targets by reading the literature of the genes in the NCBI Gene database. The targets with more PAR-CLIP reads and functional relevance in ccRCC were preferred in our analysis.

KOBAS3.0¹²⁴ was used to perform enrichment analysis of GO terms and KEGG pathways for new target genes of edited miRNAs. Significantly enriched GO terms and KEGG pathways were filtered with multiple test corrected p-values smaller than 0.05.

Data availibility

The 176 sRNA-seq sequencing profiles and 11 PAR-CLIP sequencing profiles were obtained from NCBI SRA database, and 3 gene expression profiles were obtained from NCBI GEO and TCGA database with accession numbers shown in Table S1.

References

Hsieh, J. J. et al. Renal cell carcinoma. Nat. Rev. Dis. Primers. 3, 1–19 (2017).
Google Scholar
Choueiri, T. K. & Motzer, R. J. Systemic therapy for metastatic renal-cell carcinoma. N. Engl. J. Med. 376, 354–366 (2017).
CAS PubMed Google Scholar
Motzer, R. J. et al. Kidney cancer, version 2.2017, NCCN clinical practice guidelines in oncology. J. Natl. Compr. Canc. Netw. 15, 804–834 (2017).
PubMed Google Scholar
Lai, Y. et al. The tumour microenvironment and metabolism in renal cell carcinoma targeted or immune therapy. J. Cell. Physiol. 236, 1616–1627 (2021).
CAS PubMed Google Scholar
Thomson, J. M. et al. Extensive post-transcriptional regulation of microRNAs and its implications for cancer. Genes Dev. 20, 2202–2207 (2006).
CAS PubMed PubMed Central Google Scholar
Kim, V. N., Han, J. & Siomi, M. C. Biogenesis of small RNAs in animals. Nat. Rev. Mol. Cell Biol. 10, 126–139 (2009).
CAS PubMed Google Scholar
Hussen, B. M. et al. MicroRNA: A signature for cancer progression. Biomed. Pharmacother. 138, 111528 (2021).
CAS PubMed Google Scholar
Bartel, D. P. MicroRNAs: Target recognition and regulatory functions. Cell 136, 215–233 (2009).
CAS PubMed PubMed Central Google Scholar
Ha, M. & Kim, V. N. Regulation of microRNA biogenesis. Nat. Rev. Mol. Cell Biol. 15, 509–524 (2014).
CAS PubMed Google Scholar
Braga, E. A., Fridman, M. V., Loginov, V. I., Dmitriev, A. A. & Morozov, S. G. Molecular mechanisms in clear cell renal cell carcinoma: Role of miRNAs and hypermethylated miRNA genes in crucial oncogenic pathways and processes. Front. Genet. 10, 320 (2019).
CAS PubMed PubMed Central Google Scholar
Goujon, M. et al. A double-negative feedback interaction between miR-21 and PPAR-\(\alpha\) in clear renal cell carcinoma. Cancers 14, 795 (2022).
CAS PubMed PubMed Central Google Scholar
Ji, H. et al. Overexpression of miR-155 in clear-cell renal cell carcinoma and its oncogenic effect through targeting FOXO3a. Exp. Ther. Med. 13, 2286–2292 (2017).
CAS PubMed PubMed Central Google Scholar
Pan, Y. et al. MiR-193a-3p and miR-224 mediate renal cell carcinoma progression by targeting alpha-2, 3-sialyltransferase IV and the phosphatidylinositol 3 kinase/Akt pathway. Mol. Carcinog. 57, 1067–1077 (2018).
CAS PubMed Google Scholar
Ren, Y., Zhang, L., Zhang, W. & Gao, Y. MiR-30a suppresses clear cell renal cell carcinoma proliferation and metastasis by targeting LRP6. Hum. Cell 34, 598–606 (2021).
CAS PubMed Google Scholar
Huang, J. et al. Hypoxia-induced downregulation of miR-30c promotes epithelial-mesenchymal transition in human renal cell carcinoma. Cancer Sci. 104, 1609–1617 (2013).
CAS PubMed PubMed Central Google Scholar
Outeiro-Pinho, G. et al. Epigenetically-regulated miR-30a/c-5p directly target TWF1 and hamper ccRCC cell aggressiveness. Transl. Res. 249, 110–127 (2022).
CAS PubMed Google Scholar
Cui, L. et al. MicroRNA-99a induces G1-phase cell cycle arrest and suppresses tumorigenicity in renal cell carcinoma. BMC Cancer 12, 1–11 (2012).
Google Scholar
Zhao, J. et al. MicroRNA-187, down-regulated in clear cell renal cell carcinoma and associated with lower survival, inhibits cell growth and migration though targeting B7–H3. Biochem. Biophys. Res. Commun. 438, 439–444 (2013).
CAS PubMed Google Scholar
Brennicke, A., Marchfelder, A. & Binder, S. RNA editing. FEMS Microbiol. Rev. 23, 297–316 (1999).
CAS PubMed Google Scholar
Christofi, T. & Zaravinos, A. RNA editing in the forefront of epitranscriptomics and human health. J. Transl. Med. 17, 1–15 (2019).
CAS Google Scholar
Burroughs, A. M. et al. A comprehensive survey of 3’ animal miRNA modification events and a possible role for 3’ adenylation in modulating miRNA targeting effectiveness. Genome Res. 20, 1398–1410 (2010).
CAS PubMed PubMed Central Google Scholar
Kim, Y.-K., Heo, I. & Kim, V. N. Modifications of small RNAs and their associated proteins. Cell 143, 703–709 (2010).
CAS PubMed Google Scholar
Gutiérrez-Vázquez, C. et al. 3’ Uridylation controls mature microRNA turnover during CD4 T-cell activation. RNA 23, 882–891 (2017).
PubMed PubMed Central Google Scholar
Luciano, D. J., Mirsky, H., Vendetti, N. J. & Maas, S. RNA editing of a miRNA precursor. RNA 10, 1174–1177 (2004).
CAS PubMed PubMed Central Google Scholar
Blow, M. J. et al. RNA editing of human microRNAs. Genome Biol. 7, 1–8 (2006).
Google Scholar
Landgraf, P. et al. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129, 1401–1414 (2007).
CAS PubMed PubMed Central Google Scholar
Kawahara, Y. et al. Frequency and fate of microRNA editing in human brain. Nucleic Acids Res. 36, 5270–5280 (2008).
CAS PubMed PubMed Central Google Scholar
de Hoon, M. J. et al. Cross-mapping and the identification of editing sites in mature microRNAs in high-throughput sequencing libraries. Genome Res. 20, 257–264 (2010).
PubMed PubMed Central Google Scholar
Alon, S. et al. Systematic identification of edited microRNAs in the human brain. Genome Res. 22, 1533–1540 (2012).
CAS PubMed PubMed Central Google Scholar
Ekdahl, Y., Farahani, H. S., Behm, M., Lagergren, J. & Öhman, M. A-to-I editing of microRNAs in the mammalian brain increases during development. Genome Res. 22, 1477–1487 (2012).
CAS PubMed PubMed Central Google Scholar
Mingardi, J., Musazzi, L., De Petro, G. & Barbon, A. miRNA editing: New insights into the fast control of gene expression in health and disease. Mol. Neurobiol. 55, 7717–7727 (2018).
CAS PubMed Google Scholar
Li, L. et al. The landscape of miRNA editing in animals and its impact on miRNA biogenesis and targeting. Genome Res. 28, 132–143 (2018).
CAS PubMed PubMed Central Google Scholar
Marceca, G. P. et al. Detecting and characterizing A-To-I microRNA editing in cancer. Cancers 13, 1699 (2021).
CAS PubMed PubMed Central Google Scholar
Nishikura, K. A-to-I editing of coding and non-coding RNAs by ADARs. Nat. Rev. Mol. Cell Biol. 17, 83–96 (2016).
CAS PubMed Google Scholar
Nigita, G. et al. ncRNA editing: Functional characterization and computational resources. Computational Biology of Non-Coding RNA 133–174 (2019).
Wang, Y. & Liang, H. When MicroRNAs meet RNA editing in cancer: A nucleotide change can make a difference. BioEssays 40, 1700188 (2018).
Google Scholar
Lu, C. et al. Characterizing relevant microRNA editing sites in Parkinson’s disease. Cells 12, 75 (2022).
PubMed PubMed Central Google Scholar
Pinto, Y., Buchumenski, I., Levanon, E. Y. & Eisenberg, E. Human cancer tissues exhibit reduced A-to-I editing of miRNAs coupled with elevated editing of their targets. Nucleic Acids Res. 46, 71–82 (2018).
CAS PubMed Google Scholar
Wang, Y. et al. Systematic characterization of A-to-I RNA editing hotspots in microRNAs across human cancers. Genome Res. 27, 1112–1125 (2017).
CAS PubMed PubMed Central Google Scholar
Xie, W. et al. Identification of microRNA editing sites in three subtypes of leukemia. Front. Mol. Biosci. 9, 1014288 (2022).
CAS PubMed PubMed Central Google Scholar
Tomaselli, S. et al. Modulation of microRNA editing, expression and processing by ADAR2 deaminase in glioblastoma. Genome Biol. 16, 1–19 (2015).
Google Scholar
Cesarini, V. et al. ADAR2/miR-589-3p axis controls glioblastoma cell migration/invasion. Nucleic Acids Res. 46, 2045–2059 (2018).
CAS PubMed Google Scholar
Zheng, Y. et al. Accurate detection for a wide range of mutation and editing sites of microRNAs from small RNA high-throughput sequencing profiles. Nucleic Acids Res. 44, e123–e123 (2016).
PubMed PubMed Central Google Scholar
Han, M. & Zheng, Y. Comprehensive analysis of single nucleotide polymorphisms in human microRNAs. PLoS ONE 8, e78028 (2013).
CAS PubMed PubMed Central ADS Google Scholar
Yu, S. & Kim, V. N. A tale of non-canonical tails: gene regulation by post-transcriptional RNA tailing. Nat. Rev. Mol. Cell Biol. 21, 542–556 (2020).
CAS PubMed Google Scholar
Katoh, T. et al. Selective stabilization of mammalian microRNAs by 3’ adenylation mediated by the cytoplasmic poly (A) polymerase GLD-2. Genes Dev. 23, 433–438 (2009).
CAS PubMed PubMed Central Google Scholar
D’Ambrogio, A., Gu, W., Udagawa, T., Mello, C. C. & Richter, J. D. Specific miRNA stabilization by Gld2-catalyzed monoadenylation. Cell Rep. 2, 1537–1545 (2012).
PubMed PubMed Central Google Scholar
Lee, M. et al. Adenylation of maternally inherited microRNAs by wispy. Mol. Cell 56, 696–707 (2014).
CAS PubMed PubMed Central Google Scholar
Boele, J. et al. PAPD5-mediated 3’ adenylation and subsequent degradation of miR-21 is disrupted in proliferative disease. Proc. Natl. Acad. Sci. 111, 11467–11472 (2014).
CAS PubMed PubMed Central ADS Google Scholar
Shukla, S., Bjerke, G. A., Muhlrad, D., Yi, R. & Parker, R. The RNase PARN controls the levels of specific miRNAs that contribute to p53 regulation. Mol. Cell 73, 1204–1216 (2019).
CAS PubMed PubMed Central Google Scholar
Jones, M. R. et al. Zcchc11-dependent uridylation of microRNA directs cytokine expression. Nat. Cell Biol. 11, 1157–1163 (2009).
CAS PubMed PubMed Central Google Scholar
Jones, M. R. et al. Zcchc11 uridylates mature miRNAs to enhance neonatal IGF-1 expression, growth, and survival. PLoS Genet. 8, e1003105 (2012).
CAS PubMed PubMed Central Google Scholar
Yang, A. et al. 3’ uridylation confers miRNAs with non-canonical target repertoires. Mol. Cell 75, 511–522 (2019).
CAS PubMed PubMed Central Google Scholar
Hafner, M. et al. Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141, 129–141 (2010).
CAS PubMed PubMed Central Google Scholar
Zheng, Y. Computational Non-coding RNA Biology (Academic Press, Cambridge, Massachusetts, 2018).
Google Scholar
Zheng, Y., Li, T., Ren, R., Shi, D. & Wang, S. Revealing editing and SNPs of microRNAs in colon tissues by analyzing high-throughput sequencing profiles of small RNAs. BMC Genom. 15, 1–9 (2014).
Google Scholar
Wang, Q. et al. Identifying microRNAs and their editing sites in Macaca mulatta. Cells 8, 682 (2019).
CAS PubMed PubMed Central Google Scholar
Guo, S. et al. MicroRNA editing patterns in Huntington’s disease. Sci. Rep. 12, 1–14 (2022).
Google Scholar
Wu, X. et al. Characterizing microRNA editing and mutation sites in Autism Spectrum Disorder. Front. Mol. Neurosci. 15, 1105278 (2023).
PubMed PubMed Central Google Scholar
Negi, V. et al. Altered expression and editing of miRNA-100 regulates iTreg differentiation. Nucleic Acids Res. 43, 8057–8065 (2015).
CAS PubMed PubMed Central Google Scholar
Ananieva, E. A. & Wilkinson, A. C. Branched-chain amino acid metabolism in cancer. Curr. Opin. Clin. Nutr. Metab. Care 21, 64 (2018).
CAS PubMed Google Scholar
Huang, W. et al. Bioinformatic gene analysis for possible biomarkers and therapeutic targets of hypertension-related renal cell carcinoma. Transl. Androl. Urol. 9, 2675 (2020).
PubMed PubMed Central Google Scholar
Mao, L. et al. Proteomic analysis of lung cancer cells reveals a critical role of BCAT1 in cancer cell metastasis. Theranostics 11, 9705 (2021).
CAS PubMed PubMed Central Google Scholar
Tönjes, M. et al. BCAT1 promotes cell proliferation through amino acid catabolism in gliomas carrying wild-type IDH1. Nat. Med. 19, 901–908 (2013).
PubMed PubMed Central Google Scholar
Wang, Z.-Q. et al. BCAT1 expression associates with ovarian cancer progression: Possible implications in altered disease metabolism. Oncotarget 6, 31522 (2015).
PubMed PubMed Central Google Scholar
Zhang, L. & Han, J. Branched-chain amino acid transaminase 1 (BCAT1) promotes the growth of breast cancer cells through improving mtor-mediated mitochondrial biogenesis and function. Biochem. Biophys. Res. Commun. 486, 224–231 (2017).
CAS PubMed Google Scholar
Zheng, Y.-H. et al. BCAT1, a key prognostic predictor of hepatocellular carcinoma, promotes cell proliferation and induces chemoresistance to cisplatin. Liver Int. 36, 1836–1847 (2016).
CAS PubMed Google Scholar
Ding, Y. et al. BCAT1, as a prognostic factor for HCC, can promote the development of liver cancer through activation of the AKT signaling pathway and EMT. J. Mol. Histol. 1–15 (2022).
Hattori, A. et al. Cancer progression by reprogrammed BCAA metabolism in myeloid leukaemia. Nature 545, 500–504 (2017).
CAS PubMed PubMed Central ADS Google Scholar
Shu, X. et al. BCAT1 Activates PI3K/AKT/mTOR pathway and contributes to the angiogenesis and tumorigenicity of gastric cancer. Frontiers in Cell and Developmental Biology 1345 (2021).
Xu, Y. et al. Overexpression of BCAT1 is a prognostic marker in gastric cancer. Hum. Pathol. 75, 41–46 (2018).
CAS PubMed Google Scholar
Wang, P. et al. BCAT1 promotes proliferation of endometrial cancer cells through reprogrammed BCAA metabolism. Int. J. Clin. Exp. Pathol. 11, 5536 (2018).
CAS PubMed PubMed Central Google Scholar
Lin, X., Tan, S., Fu, L. & Dong, Q. BCAT1 overexpression promotes proliferation, invasion, and Wnt signaling in non-small cell lung cancers. Onco. Targets. Ther. 13, 3583 (2020).
CAS PubMed PubMed Central Google Scholar
Li, G.-S. et al. BCAT1: A risk factor in multiple cancers based on a pan-cancer analysis. Cancer Med. 11, 1396–1412 (2022).
CAS PubMed PubMed Central Google Scholar
Yu, M. et al. BCAT1 promotes lung adenocarcinoma progression through enhanced mitochondrial function and NF-\(\kappa\)B pathway activation. J. Zhejiang Univ.-Sci. B 23, 760–769 (2022).
CAS PubMed PubMed Central Google Scholar
van der Mijn, J. C. et al. Transcriptional and metabolic remodeling in clear cell renal cell carcinoma caused by ATF4 activation and the integrated stress response (ISR). Mol. Carcinog. 61, 851–864 (2022).
PubMed PubMed Central Google Scholar
Green, N. H. et al. MTHFD2 links RNA methylation to metabolic reprogramming in renal cell carcinoma. Oncogene 38, 6211–6225 (2019).
CAS PubMed PubMed Central Google Scholar
Noguchi, K. et al. The mitochondrial one-carbon metabolic pathway is associated with patient survival in pancreatic cancer. Oncol. Lett. 16, 1827–1834 (2018).
PubMed PubMed Central Google Scholar
Huang, J., Qin, Y., Lin, C., Huang, X. & Zhang, F. MTHFD2 facilitates breast cancer cell proliferation via the AKT signaling pathway. Exp. Ther. Med. 22, 1–8 (2021).
Google Scholar
Pikman, Y. et al. Targeting MTHFD2 in acute myeloid leukemia. J. Exp. Med. 213, 1285–1306 (2016).
CAS PubMed PubMed Central Google Scholar
Yu, C. et al. Down-regulation of MTHFD2 inhibits NSCLC progression by suppressing cycle-related genes. J. Cell Mol. Med. 24, 1568–1577 (2020).
CAS PubMed Google Scholar
Gao, Y., Feng, L., Zhang, L., Geng, J. & Zhang, E. ATF4/MYC regulates MTHFD2 to promote NSCLC progression by mediating redox homeostasis. Dis. Markers 2022, 7527996 (2022).
Liu, X. et al. Methylenetetrahydrofolate dehydrogenase 2 overexpression is associated with tumor aggressiveness and poor prognosis in hepatocellular carcinoma. Dig. Liver Dis. 48, 953–960 (2016).
CAS PubMed ADS Google Scholar
Ju, H.-Q. et al. Modulation of redox homeostasis by inhibition of MTHFD2 in colorectal cancer: Mechanisms and therapeutic implications. JNCI: J. Natl. Cancer Inst.111, 584–596 (2019).
Koufaris, C. et al. Suppression of MTHFD2 in MCF-7 breast cancer cells increases glycolysis, dependency on exogenous glycine, and sensitivity to folate depletion. J. Proteome Res. 15, 2618–2625 (2016).
CAS PubMed Google Scholar
Lee, J. et al. A novel oral inhibitor for one-carbon metabolism and checkpoint kinase 1 inhibitor as a rational combination treatment for breast cancer. Biochem. Biophys. Res. Commun. 584, 7–14 (2021).
CAS PubMed Google Scholar
Li, Q. et al. MTHFD2 promotes ovarian cancer growth and metastasis via activation of the STAT3 signaling pathway. FEBS Open Bio. 11, 2845–2857 (2021).
CAS PubMed PubMed Central Google Scholar
Liu, X. et al. Non-metabolic function of MTHFD2 activates CDK2 in bladder cancer. Cancer Sci. 112, 4909 (2021).
CAS PubMed PubMed Central Google Scholar
Zhu, Z. et al. Folate enzyme MTHFD2 links one-carbon metabolism to unfolded protein response in glioblastoma. Cancer Lett. 549, 215903 (2022).
CAS PubMed Google Scholar
Wu, S. et al. Knockdown of MTHFD2 inhibits proliferation and migration of nasopharyngeal carcinoma cells through the ERK signaling pathway. Biochem. Biophys. Res. Commun. 614, 47–55 (2022).
CAS PubMed Google Scholar
Wang, W. et al. The emerging role of MTHFD family genes in regulating the tumor immunity of oral squamous cell carcinoma. J. Oncol.2022 (2022).
Nilsson, R. et al. Metabolic enzyme expression highlights a key role for MTHFD2 and the mitochondrial folate pathway in cancer. Nat. Commun. 5, 1–10 (2014).
CAS Google Scholar
Shang, M. et al. The folate cycle enzyme MTHFD2 induces cancer immune evasion through PD-L1 up-regulation. Nat. Commun. 12, 1–16 (2021).
ADS Google Scholar
Choudhury, Y. et al. Attenuated adenosine-to-inosine editing of microRNA-376a* promotes invasiveness of glioblastoma cells. J. Clin. Investig. 122, 4059–4076 (2012).
CAS PubMed PubMed Central Google Scholar
Gong, J. et al. Comprehensive analysis of human small RNA sequencing data provides insights into expression profiles and miRNA editing. RNA Biol. 11, 1375–1385 (2014).
PubMed Google Scholar
Bo, X. et al. The regulation and function of microRNA-377/RASSF8 signaling axis in gastric cancer. Oncol. Lett. 15, 3630–3638 (2018).
PubMed PubMed Central Google Scholar
He, C., Wang, L., Zhang, J. & Xu, H. Hypoxia-inducible microRNA-224 promotes the cell growth, migration and invasion by directly targeting RASSF8 in gastric cancer. Mol. Cancer 16, 1–14 (2017).
Google Scholar
Chen, Y., Bian, L. & Zhang, Y. MiR-505 mediates methotrexate resistance in colorectal cancer by targeting RASSF8. J. Pharm. Pharmacol. 70, 937–951 (2018).
CAS PubMed Google Scholar
Zhang, X. et al. RASSF8-AS1 displays low expression in colorectal cancer and up-regulates RASSF8 to suppress cell invasion and migration. Pathol.-Res. Pract. 237, 153996 (2022).
CAS PubMed Google Scholar
Huang, Y. et al. Over-expressed miR-224 promotes the progression of cervical cancer via targeting RASSF8. PLoS ONE 11, e0162378 (2016).
PubMed PubMed Central Google Scholar
Li, X. et al. CircCERS6 suppresses the development of epithelial ovarian cancer through mediating miR-630/RASSF8. Biochem. Genet. 1–19 (2022).
Wang, J. et al. RASSF8 regulates progression of cutaneous melanoma through nuclear factor-\(\kappa\)b. Oncotarget 6, 30165 (2015).
PubMed PubMed Central Google Scholar
Zhang, L. et al. RASSF8 downregulation promotes lymphangiogenesis and metastasis in esophageal squamous cell carcinoma. Oncotarget 6, 34510 (2015).
PubMed PubMed Central Google Scholar
Falvella, F. et al. Identification of RASSF8 as a candidate lung tumor suppressor gene. Oncogene 25, 3934–3938 (2006).
CAS PubMed Google Scholar
Wang, L., Liu, W., Zhang, Y. & Huang, X. The miR-224 promotes non-small cell lung cancer cell proliferation by directly targeting RASSF8. Eur. Rev. Med. Pharmacol. Sci. 21, 3223–3231 (2017).
CAS PubMed Google Scholar
Wang, T. et al. MiR-505-5p inhibits proliferation and promotes apoptosis of osteosarcoma cells via regulating RASSF8 expression. J. BU ON. Off. J. Balkan Union Oncol.26, 599–605 (2021).
Kajdasz, A. et al. Identification of RCC subtype-specific microRNAs-meta-analysis of high-throughput RCC tumor microRNA expression data. Cancers 13, 548 (2021).
CAS PubMed PubMed Central Google Scholar
Beuselinck, B. et al. Molecular subtypes of clear cell renal cell carcinoma are associated with sunitinib response in the metastatic setting. Clin. Cancer Res. 21, 1329–1339 (2015).
CAS PubMed Google Scholar
Andrews, S. et al. FastQC: A quality control tool for high throughput sequence data (2010).
Rosenbloom, K. R. et al. The UCSC genome browser database: 2015 update. Nucleic Acids Res. 43, D670–D681 (2015).
CAS PubMed Google Scholar
Kozomara, A. & Griffiths-Jones, S. miRBase: Annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 42, D68–D73 (2014).
CAS PubMed Google Scholar
Ye, J., McGinnis, S. & Madden, T. L. BLAST: Improvements for better sequence analysis. Nucleic Acids Res. 34, W6–W9 (2006).
CAS PubMed PubMed Central Google Scholar
Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, 1–10 (2009).
Google Scholar
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Stat. Soc.: Ser. B (Methodol.) 57, 289–300 (1995).
MathSciNet MATH Google Scholar
Kiran, A. & Baranov, P. V. DARNED: A DAtabase of RNa EDiting in humans. Bioinformatics 26, 1772–1776 (2010).
CAS PubMed Google Scholar
Ramaswami, G. & Li, J. B. RADAR: A rigorously annotated database of A-to-I RNA editing. Nucleic Acids Res. 42, D109–D113 (2014).
CAS PubMed Google Scholar
Peng, Z. et al. Comprehensive analysis of RNA-Seq data reveals extensive RNA editing in a human transcriptome. Nat. Biotechnol. 30, 253–260 (2012).
CAS PubMed Google Scholar
Nigita, G. et al. Tissue and exosomal miRNA editing in non-small cell lung cancer. Sci. Rep. 8, 1–8 (2018).
CAS Google Scholar
Smigielski, E. M., Sirotkin, K., Ward, M. & Sherry, S. T. dbSNP: A database of single nucleotide polymorphisms. Nucleic Acids Res. 28, 352–355 (2000).
CAS PubMed PubMed Central Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).
Google Scholar
Memczak, S. et al. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 495, 333–338 (2013).
CAS PubMed ADS Google Scholar
Zhao, Q. et al. Transcriptomic characterization and innovative molecular classification of clear cell renal cell carcinoma in the chinese population. Cancer Cell Int. 20, 1–14 (2020).
CAS Google Scholar
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
CAS PubMed Google Scholar
Xie, C. et al. KOBAS 2.0: A web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 39, W316–W322 (2011).
CAS PubMed PubMed Central Google Scholar

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Funding

The research was supported in part by a grand (No. 31460295) of the National Natural Science Foundation of China and an Open Research Fund (No. SKLGE-2107) of State Key Laboratory of Genetic Engineering, Fudan University, China to YZ.

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Authors and Affiliations

State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China
Yulong Liu, Wanran Li & Chunyi Mao
College of Landscape and Horticulture, Yunnan Agricultural University, Kunming, 650201, Yunnan, China
Shiyong Guo, Wenping Xie, Huaide Yang, Wanran Li, Nan Zhou, Guangchen Zhou, Chunyi Mao & Yun Zheng
Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China
Wenping Xie
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China
Huaide Yang
School of Criminal Investigation, Yunnan Police College, Kunming, 650223, Yunnan, China
Jun Yang

Authors

Yulong Liu
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Shiyong Guo
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Wenping Xie
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Huaide Yang
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Wanran Li
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Nan Zhou
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Jun Yang
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Guangchen Zhou
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Chunyi Mao
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Yun Zheng
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Contributions

Y.L.: data curation, formal analysis, investigation, validation, visualisation, writing - original draft; S.G.: data curation, formal analysis, investigation, validation, visualisation; W.X.: data curation, formal analysis, investigation, validation; H.Y.: data curation, formal analysis; W.L.: investigation, validation; N.Z.: formal analysis, visualisation; J.Y.: formal analysis; G.Z.: validation; C.M.: validation; Y.Z.: conceptualisation, formal analysis, funding acquisition, methodology, project administration, resources, software, supervision, writing - review & editing.

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Correspondence to Yun Zheng.

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Liu, Y., Guo, S., Xie, W. et al. Identification of microRNA editing sites in clear cell renal cell carcinoma. Sci Rep 13, 15117 (2023). https://doi.org/10.1038/s41598-023-42302-y

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Received: 15 May 2023
Accepted: 07 September 2023
Published: 13 September 2023
DOI: https://doi.org/10.1038/s41598-023-42302-y

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