Next Article in Journal
Regulatory Role of Ribonucleotide Reductase Subunit M2 in Hepatocyte Growth and Pathogenesis of Hepatitis C Virus
Next Article in Special Issue
The Ketogenic Diet in Colorectal Cancer: A Means to an End
Previous Article in Journal
Spinocerebellar Ataxia in a Hungarian Female Patient with a Novel Variant of Unknown Significance in the CCDC88C Gene
Previous Article in Special Issue
Understanding Interstitial Lung Diseases Associated with Connective Tissue Disease (CTD-ILD): Genetics, Cellular Pathophysiology, and Biologic Drivers
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 24
Issue 3
10.3390/ijms24032618
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

MicroRNA and Messenger RNA Expression Profiles in Canine Mammary Gland Tumor

by
Eun Pyo Kim
1,†,
Giup Jang
2,†,
Jin-Wook Kim
1,3,
Sang Wha Kim
4,5,
Heaji Chung
6,
Yun Jung Yang
6,
Wan Hee Kim
6 and
Geon A Kim
7,8,*
1
Department of Theriogenology and Biotechnology, Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul 08826, Republic of Korea
2
Research and Development Center, Einocle Inc., Seoul 02841, Republic of Korea
3
Research and Development Center, ROKIT Healthcare Inc., Seoul 08512, Republic of Korea
4
Department of Microbiology and Immunology, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
5
Institute of Endemic Disease, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
6
Department of General Surgery, College of Veterinary Medicine, Seoul National University, Seoul 08826, Republic of Korea
7
Department of Biomedical Laboratory Science, School of Healthcare Science, Eulji University, Uijeongbu 11759, Republic of Korea
8
Department of Senior Healthcare, Eulji University, Uijeongbu 11759, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2023, 24(3), 2618; https://doi.org/10.3390/ijms24032618
Submission received: 2 November 2022 / Revised: 16 January 2023 / Accepted: 27 January 2023 / Published: 30 January 2023
(This article belongs to the Special Issue Scientific Discoveries Supporting Theories in Science: From Thinking to Practice 2.0)
Browse Figures
Versions Notes

Abstract

:
Canine mammary gland tumor (CMT) is the most frequently diagnosed neoplasm in intact female dogs. As prognosis depends on the malignancy of tumors and metastasis levels, early and accurate diagnosis are crucial for prolongation of life expectancy. The genetic similarity of dogs with humans in addition to environmental and physiological similarities make them ideal models for the study of cancer. In this study, we analyzed differentially expressed microRNAs followed by RNA-Seq to investigate the alterations in mRNA levels based on the malignancy (benign, malignant) and the biopsy locations (tumors, surrounding normal tissues). We identified multiple breast cancer-related genes regardless of malignancy. We found cfa-miR-503 to be the only miRNA that showed altered expression in response to malignancy in CMTs. Although further validation is needed, cfa-miR-503 could be used as a potential diagnostic biomarker as well as a potential RNA-based anti-tumor drug in malignant CMTs.

1. Introduction

Cancer is the most common cause of death in dogs [1]. Among various cancers, canine mammary gland tumor (CMT) is the most frequently occurring tumor in intact female dogs [2,3,4]. Approximately 50% of the CMT cases lead to malignancy [5]; owing to its high incidence and mortality rates, CMT is of significance in canine medicine. To date, several studies have focused on CMT owing to its highly complex pathogenicity [2,6,7,8,9].
In addition to its clinical significance to dogs, studies on canine cancer are also applicable to medical research targeting human diseases, as dogs have been considered to be ideal animal models [1]. Dogs contract clinically similar diseases to humans; they also share similar organ sizes with humans [10,11]. The fact that dogs share similar living spaces with their human owners, minimizes gaps in environmental factors between the two [10,11]. Since dogs age faster than humans and usually experience minimal genetic variations owing to breeding, they are valuable animal models of human disease [12]. The variety of studies conducted on dogs to date have continuously proven the value of dogs as model species connecting mice and humans.
Dogs have greater genetic similarity with humans than mice [11]. About half of the hereditary diseases in dogs are remarkably similar to those in humans [11]. CMT in dogs share similarities with breast cancer (BC) in humans: In dogs, diverse genes including Tumor protein 53 (p53), Breast Cancer1 (BRCA1), Breast Cancer 2 (BRCA2), Phosphatase and Tensin Homolog (PTEN), as well as E-cadherin and Serine/Threonine kinase11 (STK11) are known contributors of CMT [13], which have also been attributed to human BC.
The microRNAs (miRNAs), also known as small non-coding RNA, are usually composed of about 22 nucleotides. In addition, miRNAs play key roles in post-transcriptional gene silencing by pairing with complementary messenger RNA in diverse animal clades [14]. Thanks to their functional characteristics, miRNA studies have been actively conducted to gain new insights in human diseases. In BC, studies involving miRNA include using it as a biomarker, profiling miRNA for cancer diagnosis, as well as developing miRNAs as tools to understand disease prognosis, therapy response, and resistance mechanisms [15,16,17].
Mature canine miRNAs show high homology to those of humans, which opens possibilities for comparative studies [18]. The importance of miRNA in canine cancer studies has been identified to be of significance leading to a number of studies [19,20,21,22]. A miRNA study on CMT, targeting 10 types of human oncomirs identified nine miRNAs (let-7f, miR-15a, miR-16, miR-17-5p, miR-21, miR-29b, miR-125b, miR-155, miR-181b) which showed expression patterns similar to miRNAs in human BC [23]. However, systemic miRNA profiling of CMT is yet to be conducted.
The objective of this study was to identify the expression patterns of miRNAs in CMT tissues. Benign and malignant CMTs determined based on histopathology and metastasis patterns would be compared with miRNA expression profiles of normal tissues. Similar comparative analysis would also be performed between benign and malignant CMTs. Differentially expressed miRNAs (DE-miRNAs) from the above comparative analyses would be listed, and their functions and expression patterns in both dogs and humans would be discussed based on the existing literature.

2. Results

DE-miRNAs based on microarray were identified using three comparisons: (1) BCMT-T vs. BCMT-N, (2) MCMT-T vs. MCMT-N, and (3) MCMT-T vs. BCMT-T; T means tumor and N means normal. The cut-off values are |fold change| ≥ 2, p-value < 0.05 (t-test), and DABG p-value < 0.05. DABG p-value was computed based on the probability that the signal intensity is part of the null distribution. It could be useful in the detection and removal of low intensity signals. Eighteen upregulated miRNAs in BCMT-T vs. BCMT-N, twenty upregulated miRNAs in MCMT-T vs. MCMT-N, and one upregulated miRNA in MCMT-T vs. BCMT-T were identified. Downregulated miRNAs were not detected in all comparisons. Figure 1 shows the volcano plot for identifying DE-miRNAs of each comparison based on fold change and p-value.
We performed the pathway analysis from KEGG based on DE-miRNAs. Among the various pathways in KEGG, we identified the role of miRNAs as “MicroRNAs in cancer”. This category contains sub-pathways for nine different types of cancer. Among these nine types, we focused on the breast cancer pathway. Since only one miRNA was detected in the MCMT-T vs. BCMT-T, this comparison was excluded from the pathway analysis. Figure 2 shows the result of breast cancer pathway in “MicroRNAs in cancer” for the miRNAs of BCMT-T vs. BCMT-N and MCMT-T vs. MCMT-N.
Among the 18 array-based DE-miRNAs of BCMT-T vs. BCMT-N, breast cancer related miRNAs were identified as cfa-miR-21, cfa-miR-129b, cfa-miR-155, and cfa-miR-222. Among the 20 miRNA of MCMT-T vs. MCMT-N, breast cancer related miRNAs were identified as cfa-miR-221 and cfa-miR-222. Moreover, we discovered the expression pattern of target genes of breast cancer related miRNAs identified in the pathway at the mRNA level. The target genes in the BCMT group were BAK1, CDKN1B, PDCD4, SERPINB5, SOCS1, and TPM1, and the target gene in the MCMT group was CDKN1B. Table 1 shows the fold change and p-value of target genes at the mRNA level. It was found that the target genes of miRNA were downregulated in the breast cancer pathway; the genes with significant p-value under 0.05 at the mRNA level were downregulated (PDCD4, SOCS1, and CDKN1B).
We performed gene-level analysis of DE-miRNAs based on the miRNA-gene associations collected from microRNA target prediction database (miRDB). The miRDB contains information regarding predicted target genes and predicted score for miRNAs. We filtered associations with a predictive score of >90 for miRNA-gene relationships. Since the same gene is associated with multiple miRNAs, the collected target genes were used after de-duplication. Three hundred and seventy target genes in DE-miRNA of BCMT-T vs. BCMT-N, six hundred and forty eight target genes in DE-miRNA of MCMT-T vs. MCMT-N, and three target genes in DE-miRNA of BCMT-T vs. MCMT-T were collected. Functional analysis was performed using clusterProfiler based on predicted genes. Among the various biological functions, we focused on specific biological functions: Autophagy, response to oxidative stress, and p53 signaling pathway. As few target genes were detected in the MCMT-T vs. BCMT-T, this comparison was excluded from the enrichment analysis. Figure 3 shows the results for the top 10 categories among Gene Ontology and KEGG enrichment analysis.
To evaluate the results of clusterProfiler, we collected disease related Gene Ontologies from the Comparative Toxicogenomics Database (CTD) [24]. We used phenotype (GO)-disease inference networks among various information in CTD, which contains a list of diseases associated with Gene Ontology. We collected a list of GO categories associated with breast cancer using the MESH ID (D001943). In addition, GOs that do not have information on genes from the collected list were removed. We filtered 5282 categories in GO_BP, 560 in GO_CC, and 1060 in GO_MF. We compared GOs with p-value < 0.05 among clusterProfiler and filtered CTD (Figure 4).
It was found that the common function of the array-based DE-miRNAs’ target genes was similar to those associated with breast cancer. In particular, categories related to angiogenesis were detected in the intersection. According to Nishida et al., the onset of cancer is related to angiogenesis [25]. Moreover, it was identified that angiogenesis is closely related to progression in breast cancer [25,26]. Significant pathways related breast cancer, such as melanoma (p-value < 0.001), breast cancer (p-value = 0.009), and microRNAs in cancer (p-value = 0.009) in the BCMT group, p53 signaling pathway (p-value < 0.001), autophagy, and EGFR tyrosine kinase inhibitor resistance in MCMT group, were observed from KEGG analysis results.
We extracted mRNA level DEG for a similar comparative analysis as performed using miRNAs. The cut-off values were |fold change| ≥ 2 and p-value < 0.05. About 962 genes (392 upregulated and 570 downregulated) in BCMT-T vs. BCMT-N, and 1541 genes (856 upregulated and 685 downregulated) in MCMT-T vs. MCMT-N were identified. Figure 5 shows the volcano plot for identifying DEGs of each comparison based on fold change and p-value.
The relationship between identified DEGs and target genes of miRNA was analyzed. Genes corresponding to protein coding were filtered from DEGs. Figure 6 shows the DE-miRNA target gene and DEG of mRNA-based Venn diagram.
The number of genes commonly appearing between miRNA and mRNA was 12 in BCMT group and 54 in MCMT group. It was found that a small number of genes appeared as intersections compared with the number of detected genes in the miRNA and mRNA DEG analysis. To evaluate whether the genes corresponding to the intersection are breast cancer related genes, we collected disease-gene associations from DisGeNet [27]. DisGeNet is a database containing disease-gene associations for humans. Due to the lack of information on disease related genes for dogs, DisGeNet was used to verify genes corresponding to intersections. We identified 10 breast cancer related genes among 12 corresponding to the intersection in BCMT-T vs. BCMT-N (83%), and 25 related genes among 54 corresponding to the intersection in MCMT-T vs. MCMT-N (46%) (Table 2). We detected that the target genes of miRNA showed a significant expression pattern even at the mRNA level. All of the analyzed data are addressed in Supplementary Data S2.

3. Discussion

When comparing BCMT-N and BCMT-T, 18 miRNAs were classified as DE-miRNAs with |fold change| ≥ 2 and p-value < 0.05. Moreover, cfa-miR-21 and cfa-miR-502 were already discovered for their overexpression in CMT and are diagnostic targets of CMT [23,28,29]. In particular, cfa-miR-21 is related to the inhibition of tumor cell apoptosis in dogs [23]. Human homolog genes of the five DE-miRNAs viz. hsa-miR-21, hsa-miR-185, hsa-miR-125b, hsa-miR-500, and hsa-miR-502 are all related to human cancers, whereas all of the miRNAs except for hsa-miR-500 are related to human BC [30,31,32].
Hsa-miR-21 is a well-known oncomir [19,33] that shows an increase in copy number in human tumor tissues [34]; it is indicated in tumorigenesis, apoptosis, cell proliferation, and cancer progression in human cancers [33]. Since hsa-miR-21 is the only miRNA overexpressed in six types of human cancers, it qualifies as an important candidate for cancer studies [30]. Furthermore, hsa-miR-21 contributes to the maintenance of malignant phenotypes in certain cancers; therefore, it could be used as a biomarker for malignancy [35]. Most importantly, the role of hsa-miR-21 in BC has also been studied; it is correlated with the presence and progression of BC, as it targets the tumor suppressor protein, Programmed Cell Death 4 [36,37]. The overexpression of cfa-miR-21 is observed in both canine (CMT) and human tissue (BC) [23]. In dogs, cfa-miR-21 is attributed to the inhibition of tumor cell apoptosis [23]; it is quite natural that one of the top five DE-miRNAs was found to be cfa-miR-21 in this study (Supplementary Data S1A). However, in contrast to hsa-miR-21 which is identified as a biomarker for tumor malignancy in humans, cfa-miR-21 was found only in the DE-miRNA list of BCMT and not from the MCMT tissue (Data S1A,B).
The function of hsa-miR-185 as a tumor suppressor has been established by various studies; it inhibits the proliferation of human colon cancer cell as well as the development of glioma by inhibiting global DNA methylation [38,39]. The role of hsa-miR-185 in human BC has also been identified as a tumor suppressor since it inhibits BC by regulating S100A8/A9, NF-κB/Snail signaling pathway, and programmed cell death. Although cfa-miR-185 was found to be related to IL-7R expression in dogs [40], its association with tumors has not yet been established. As observed in this study, BMGT individuals showed significantly higher expression of cfa-miR-185 in tumor tissue than normal tissues, which is contrary to the role of tumor suppression shown by hsa-miR-185 in humans.
Moreover, cfa-miR-125b is attributed to host cell resistance against canine influenza virus in [41] and to testicular retinoic acid induced spermatogenesis [42]. However, the role of cfa-miR-125b in canine tumors has not yet been identified; this study is the first to record the overexpression of cfa-miR-125b in MGMT dogs. On the contrary, hsa-miR-125b has been known to act as both an oncogene and a tumor-suppressor gene. Hsa-miR-125b contributes to the regulation of glycolysis, apoptosis, metastasis, and cancer stem cells [43]. Decreased hsa-miR-125b contributes to prostate tumorigenesis via tumor cell behavior alteration as it works as a tumor suppressor [44]. Furthermore, hsa-miR-125b plays a role in γ-irradiation sensitivity in BC; its increased expression results in enhanced apoptotic activity and senescence after irradiation of BC cells [45].
In this study, tumor tissue of MCMT patients showed a significant upregulation of cfa-miR-500 compared with the normal tissue (Data S1B). Since there are no existing reports on the expression patterns or the function of cfa-miR-500 in dogs, the results obtained in this study are of significance to CMT research. In humans, the expression pattern and function of hsa-miR-500 have been studied extensively. Jiang et al. revealed that hsa-miR-500 suppresses the proliferation and metastasis of non-small cell lung cancer. However, most of the studies show the function of the hsa-miR-500 as an oncomir, such as the cfa-miR-500 in this study [46]. Furthermore, hsa-miR-500 has been highly correlated with the malignant progression of gastric cancer [47] and is found to be upregulated in human hepatocellular carcinoma and prostate cancer tissues [48,49].
Hsa-miR-502 is known for its suppressive action on the proliferation of BC [50]. Moreover, it plays a role in inhibiting proliferation, tumor growth, invasion, and metastasis in hepatocellular carcinoma [51]. On the other hand, hsa-miR-502 is observed to function as an oncomir as it promotes cancer cell proliferation and inhibits apoptosis in esophageal cancer [52]. According to Xiaoli et al., cfa-miR-502 is significantly upregulated in CMT compared with normal tissue [29]. Since overexpression of cfa-miR-502 was detected in this study, the role of cfa-miR-502 could be further explored in future studies (Data S1A).
In addition, hsa-miR-146a is associated with diverse tumors in humans, and it works as tumor suppressor miRNA or oncomiR depending on the target gene [53]. Moreover, hsa-miR-146a showed significantly lower expression than normal tissue in gastric cancer tissue, which makes the miRNA an independent prognostic factor for cancer patients [54]. As a tumor suppressor gene, it plays an important role in the proliferation and oncogenic transformation of myeloid cells; it is also found to be downregulated in hepatocellular carcinoma tissue [55,56]. Furthermore, cfa-miR-146a in dogs is expressed in response to a tumor (Data S1A,B) and inflammation similar to hsa-miR-146a in humans; it is one of the overexpressed miRNAs in canine meningioma and is related to cell proliferation and migration [57]. Significantly increased expression of cfa-miR-146a in tumor tissues of MCMT was observed in this study (Data S1B); cfa-miR-146a also contributes to the inflammatory response in canine meningioma and peri-implantitis, as similarly found in humans [57,58].
Nevertheless, cfa-miR-23b has not been mentioned in officially published studies, which makes its increased expression in tumor tissues of MCMT in this study the first to be reported in dogs (Data S1B). However, hsa-miR-23b has been discussed in various studies in relation to human disease; hsa-miR-23b is an oncomir in human BC [59], while it exhibits decreased expression in pituitary adenoma as hsa-miR-23b inhibits proliferation by cell cycle arrest [60]. In addition, hsa-miR-23b is more often identified as a tumor suppressor in human biology and hsa-miR-23b is related to cell aggressiveness inhibition, which indicates its potential use as a biomarker for diagnosis and prognosis of cancer [61]. Hsa-miR-23b inhibits cell proliferation and invasion in prostate cancer, thereby affecting the epithelial-mesenchymal transition process [62]. The downregulation of hsa-miR-23b is related to the poor prognosis of colorectal cancer [63] and cervical cancer [64].
Hsa-miR-221 and hsa-miR-222 miRNAs affect proliferation, differentiation, and invasion of cancer cells, and are upregulated in human BC, multiple myeloma, malignant melanoma, glioma, colorectal cancer, etc. [65,66,67]. The abnormal expression of hsa-miR-221 and hsa-miR-222 is attributed to the development of malignant tumors [67]. Their functions have been studied in detail in human BC. Hsa-miR-221 and hsa-miR-222 affect cancer development and progression as they are related to the telomerase activity, apoptosis, angiogenesis, proliferation, autophagy, and epithelial-mesenchymal transition; they also affect anticancer drug resistance in BC [65,68]. However, in contrast to humans, only a limited number of studies were conducted with cfa-miR-221 and cfa-miR-222. These miRNAs are upregulated in canine prostate cancer, contribute to cell proliferation, and exhibit increased expression in the pituitary when exposed to chronic stress stimulation [62,69]. To date, no study has been conducted in relation to CMT, and the significant upregulation of cfa-miR-221 and cfa-miR-222 in MCMT observed in this study is the first one to be recorded (Data S1B).
A comparison of malignant and benign tumor tissues (MCMT-T vs. BCMT-T) identified cfa-miR-503 as the only DE-miRNA (Figure 1C and Data S1C). In the previous studies, cfa-miR-503 has been found to contribute to follicular growth and oocyte maturation in dogs [70], and to the doxorubicin sensitivity in tumor tissue [71]. On the other hand, in humans, hsa-miR-503 is indicated as a tumor suppressor and oncogenic miRNA. Hsa-miR-503 inhibits tumorigenesis, progression, proliferation, metastasis in hepatocellular carcinoma, lung cancer, BC, colorectal cancer, ovarian cancer, cervical cancer, etc., and functions as a tumor suppressor [72,73,74]. However, these characteristics are not yet studied in dogs.
In summary, among the miRNAs discussed here, we first reported that cfa-miR-503 was related to the malignancy. Although not evaluated in this study, cfa-miR-503 could not only be a novel short RNA-based drug for canine malignant CMTs that functions as a tumor suppressor with anti-metastasis activity, but could also be a novel biomarker for malignancy diagnostic methods in CMT, while the histopathological assessments are vague. Nevertheless, further studies on the mechanism of action should be conducted for the evaluation and validation of cfa-miR-503; this miRNA may be a promising candidate both for a novel drug and a biomarker.

4. Materials and Methods

4.1. Tissue Sample Collection

Tissue samples were collected from dogs at Seoul National University Veterinary Medical Teaching Hospital as well as from over 10 other local animal hospitals in the Republic of Korea during 2020–2021. CMT diagnosis and malignancy classification were carried out based on both histopathological examination (IDEXX, Seongnam-si, Republic of Korea) and metastasis status. Eight normal tissues and twelve benign CMT (BCMT) tissues were collected from twelve dogs with BCMT (Table 3). Similarly, four normal tissues and eight malignant CMT (MCMT) tissues were collected from eight dogs with MCMT (Table 3). Blood tests were performed for all animals to rule out other medical issues. Normal, unaffected tissues were also collected adjacent to the CMT mass. All samples for miRNA-seq and RNA-Seq were preserved in RNAlater (Thermofisher, Waltham, MA, USA) and stored frozen at −80 °C until further analysis. Samples were collected only after receiving consent from the dog owners. This study was approved by the Seoul National University Institutional Animal Care and Use Committee (approval number: SNU-200217-3-2).

4.2. Total RNA Extraction and Quality Check

Total RNA was extracted from each tissue sample using Easy-Spin Total RNA Extraction kit (Intron Biotechnology, Seoul, Republic of Korea) according to the manufacturer’s protocol. RNA purity and integrity were evaluated using ND-2000 Spectrophotometer (NanoDrop, Wilmington, DE, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA), respectively.

4.3. Microarray Hybridization and Scanning for miRNA

Microarray hybridization was performed using the Affymetrix GeneChip miRNA 4.0 Array (Thermofisher, MA, USA) according to the manufacturer’s protocol. Total RNA samples were labeled using FlashTag™ Biotin RNA Labeling Kit (Genisphere, PA, USA). The labeled RNA samples were then quantified, fractionated, and hybridized to the microarray according to the manufacturer’s protocol. RNA-array hybridization was performed on an Affymetrix® 450 Fluidics Station (Thermofisher, MA, USA). The arrays were stained using a GeneChip Fluidics Station 450 (Affymetrix, Santa Clara, CA, USA) and scanned using an Affymetrix GCS 3000 scanner (Affymetrix, Santa Clara, CA, USA). The miRNA-mRNA hybridization signals were analyzed using the Affymetrix® GeneChip™ Command Console.

4.4. Raw Data Preparation and Statistical Analysis

Raw data were extracted automatically through the Affymetrix data extraction protocol using the Affymetrix GeneChip® Command Console® Software (AGCC) version 6.1 (ThermoFisher, MA, USA). The CEL files import, miRNA level RMA + DABG-All analyses and results were exported using Affymetrix® Power Tools (APT) Software version 2.11.4 (ThermoFisher, MA, USA). Array data were filtered using probes of annotated species.

4.5. RNA-Seq Library Construction and Sequencing

Total RNA concentration was calculated using Quant-IT RiboGreen (Invitrogen, ThermoFisher, MA, USA, #R11490). To assess the integrity of the total RNA, samples were carried out on the TapeStation RNA ScreenTape (Agilent Technologies, CA, USA, #5067-5576). Only high-quality RNA preparations with RNA integrity number (RIN) > 7.0 were used for RNA library construction. A library was independently prepared with 1 µg total RNA of each sample using the Illumina TruSeq Stranded mRNA Sample Prep Kit (RS-122-2101, Illumina, Inc., San Diego, CA, USA). The first step in the workflow involved purifying the poly-A containing mRNA molecules using poly-T-attached magnetic beads. Following purification, the mRNA samples were fragmented into small pieces using divalent cations under an elevated temperature. The cleaved RNA fragments were then used as templates to generate first strand cDNA using SuperScript II reverse transcriptase (Invitrogen, MA, USA)) with random primers. This was followed by second strand cDNA synthesis using DNA Polymerase I, RNase H, and dUTP. Next, the generated double stranded cDNA fragments were subjected to an end repair process involving adenylation followed by adapter ligation. Thereafter, the products were purified and enriched with PCR to create the final cDNA library. The libraries were quantified using KAPA Library Quantification kits for Illumina Sequencing platforms according to the qPCR Quantification Protocol Guide (KAPA BIOSYSTEMS, Wilmington, DE, USA) and qualified using the TapeStation D1000 ScreenTape (Agilent Technologies, CA, USA). Finally, indexed libraries were submitted to Illumina NovaSeq (Illumina, Inc., San Diego, CA, USA) to perform paired-end (2 × 100 bp) sequencing.

4.6. Data Analysis

At the miRNA level, raw data were extracted automatically in Affymetrix data extraction protocol using the software provided by Affymetrix GeneChip® Command Console® Software (AGCC). The CEL files import, miRNA level RMA + DABG-All analyses and results were exported using Affymetrix® Power Tools (APT) Software. Array data were filtered using probes of annotated species.
At the mRNA level, we preprocessed the raw reads from the sequencer to remove low quality and adapter sequences before analysis and aligned the processed reads to the Canis lupus familiaris (CanFam3.1) database using HISAT v2.1.0 [75]. HISAT utilizes two types of indexes for alignment (a global, whole-genome index and tens of thousands of small local indexes). These two types of indexes are constructed using the same Burrows–Wheeler transform (BWT), which is a graph FM index (GFM), such as Bowtie2. Due to its use in efficient data structures and algorithms, HISAT generates spliced alignments several times faster than Bowtie and BWA. The reference genome sequence of Canis lupus familiaris (CanFam3.1) and annotation data were downloaded from the NCBI database. Then, the transcript assembly of known transcripts was processed by StringTie v2.1.3b [76]. Based on the results, expression abundance of transcripts was calculated as read count or Fragments Per Kilobase of exon per Million fragments mapped (FPKM value) per sample. The expression profiles were used for additional analysis, such as differentially expressed genes (DEGs). In groups with different conditions, DEGs or transcripts can be filtered through statistical hypothesis testing.
A comparative analysis between test and control samples was carried out using independent t-test and fold change with the null hypothesis, which indicates that no difference exists among groups. False discovery rate (FDR) was controlled by adjusting p-value using Benjamini-Hochberg algorithm. All statistical tests and visualization of DEGs were conducted using R statistical language (v3.3.2).
KEGG Mapper [77] and clusterProfiler v3.18.1 [78] in R were used to perform the functional analysis of miRNAs and genes. Based on KEGG Mapper, the roles of miRNAs and genes were discovered. Biological functions of DEGs were analyzed based on clusterProfiler. We collected miRNA-gene associations from miRDB [79] and performed gene level analysis based on miRNA targets. Moreover, DEGs were identified by comparisons at the mRNA level; additional analysis was performed between the miRNA target genes and DEGs at mRNA levels.

5. Conclusions

In this study, we identified multiple BC-related DEGs in CMT samples. Although several miRNAs were significantly altered by tumorigenesis, only cfa-miR-503 was differentially expressed by malignancy. Along with our results, we conclude that cfa-miR-503 could be used as a potential biomarker for diagnosis and prognostic evaluation of malignant CMTs. Moreover, it could be suggested as a novel RNA-based drug to alleviate metastasis and proliferation of malignant CMTs.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms24032618/s1.

Author Contributions

Conceptualization, E.P.K., S.W.K. and G.A.K.; methodology, E.P.K., G.J., J.-W.K., S.W.K., H.C., Y.J.Y., W.H.K. and G.A.K.; formal analysis, G.J. and J.-W.K.; investigation, E.P.K., S.W.K., G.J., J.-W.K. and G.A.K.; data curation, E.P.K., S.W.K., G.J., J.-W.K. and G.A.K.; writing—original draft preparation, E.P.K., S.W.K., G.J., J.-W.K. and G.A.K.; writing—review and editing, H.C., Y.J.Y., W.H.K. and G.A.K.; visualization, S.W.K. and G.J.; supervision, G.A.K.; project administration, G.A.K.; funding acquisition, W.H.K. and G.A.K. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Cooperative Research Program for Agriculture Science and Technology Development (#PJ014990022021), the Research Institute for Veterinary Science, and BK plus 21 Program.

Institutional Review Board Statement

The tissue samples collected from dogs for this study were obtained only after receiving consent from the dog owners. This study was approved by the Seoul National University Institutional Animal Care and Use Committee (approval number: SNU-200217-3-2).

Informed Consent Statement

Informed consent was obtained from all owners involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

CMT: Canine mammary gland tumor; BC: Breast cancer; p53: Tumor protein 53; BRCA1: Breast cancer1; BRCA2: Breast cancer2; PTEN: Phosphatase and tensin homolog; STK11: Serine/threonine kinase11; DE-miRNAs: Differentially expressed miRNAs; BCMT: Benign CMT; MCMT: Malignant CMT; RIN: RNA integrity number; DEGs: Differentially expressed genes; miRDB: MicroRNA target prediction database; CTD: Comparative toxicogenomics database.

References

  1. Gardner, H.L.; Fenger, J.M.; London, C.A. Dogs as a Model for Cancer. Annu. Rev. Anim. Biosci. 2016, 4, 199–222. [Google Scholar] [CrossRef] [PubMed]
  2. Thumser-Henner, P.; Nytko, K.J.; Bley, C.R. Mutations of BRCA2 in canine mammary tumors and their targeting potential in clinical therapy. BMC Vet. Res. 2020, 16, 30. [Google Scholar] [CrossRef] [Green Version]
  3. Sorenmo, K. Canine mammary gland tumors. Vet. Clin. N. Am. Small Anim. Pract. 2003, 33, 573–596. [Google Scholar] [CrossRef] [PubMed]
  4. Dorn, C.R.; Taylor, D.O.N.; Schneider, R.; Hibbard, H.H.; Klauber, M.R. Survey of animal neoplasms in Alameda and Contra Costa Counties, California. II. Cancer morbidity in dogs and cats from Alameda County. J. Natl. Cancer Inst. 1968, 40, 307–318. [Google Scholar]
  5. Gilbertson, S.R.; Kurzman, I.D.; Zachrau, R.E.; Hurvitz, A.I.; Black, M.M. Canine Mammary Epithelial Neoplasms: Biologic Implications of Morphologic Characteristics Assessed in 232 Dogs. Vet. Pathol. 1983, 20, 127–142. [Google Scholar] [CrossRef] [Green Version]
  6. Kowal, K.; Tkaczyk-Wlizło, A.; Pierzchała, M.; Gawor, J.; Ślaska, B. Molecular differences in mitochondrial DNA genomes of dogs with malignant mammary tumours. Vet. Comp. Oncol. 2022, 20, 256–264. [Google Scholar] [CrossRef]
  7. Biondi, L.R.; Tedardi, M.V.; Gentile, L.B.; Chamas, P.P.C.; Dagli, M.L.Z. Quantification of Global DNA Methylation in Canine Mammary Gland Tumors via Immunostaining of 5-Methylcytosine: Histopathological and Clinical Correlations. Front. Vet. Sci. 2021, 8, 8241. [Google Scholar] [CrossRef]
  8. Khand, F.M.; Yao, D.-W.; Hao, P.; Wu, X.-Q.; Kamboh, A.A.; Yang, D.-J. Microsatellite Instability and MMR Genes Abnormalities in Canine Mammary Gland Tumors. Diagnostics 2020, 10, 104. [Google Scholar] [CrossRef] [Green Version]
  9. Mohammadreza Nassiri, A.S.; Vakili-Azghandi, M.; Gopalan, V.; Doosti, M.; Ghovvati, S.; Movassaghi, A.R. VEGF-C and p53 gene expression in the normal and neoplastic mammary gland of canines: A pilot study. J. Cell Mol. Res. 2021, 13, 10–18. [Google Scholar]
  10. Hong, S.G.; Kim, M.K.; Jang, G.; Oh, H.J.; Park, J.E.; Kang, J.T.; Koo, O.J.; Kim, T.; Kwon, M.S.; Koo, B.C.; et al. Generation of red fluorescent protein transgenic dogs. Genesis 2009, 47, 314–322. [Google Scholar] [CrossRef]
  11. Tsai, K.L.; Clark, L.A.; Murphy, K.E. Understanding hereditary diseases using the dog and human as companion model systems. Mamm. Genome 2007, 18, 444–451. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Rowell, J.L.; McCarthy, D.O.; Alvarez, C.E. Dog models of naturally occurring cancer. Trends Mol. Med. 2011, 17, 380–388. [Google Scholar] [CrossRef] [PubMed]
  13. Huskey, A.L.W.; Goebel, K.; Lloveras-Fuentes, C.; McNeely, I.; Merner, N.D. Whole genome sequencing for the investigation of canine mammary tumor inheritance—An initial assessment of high-risk breast cancer genes reveal BRCA2 and STK11 variants potentially associated with risk in purebred dogs. Canine Med. Genet. 2020, 7, 8. [Google Scholar] [CrossRef]
  14. O’Brien, J.; Hayder, H.; Zayed, Y.; Peng, C. Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation. Front. Endocrinol. 2018, 9, 402. [Google Scholar] [CrossRef] [Green Version]
  15. Iorio, M.; Casalini, P.; Tagliabue, E.; Ménard, S.; Croce, C.M. MicroRNA profiling as a tool to understand prognosis, therapy response and resistance in breast cancer. Eur. J. Cancer 2008, 44, 2753–2759. [Google Scholar] [CrossRef] [PubMed]
  16. Mar-Aguilar, F.; Mendoza-Ramírez, J.A.; Malagón-Santiago, I.; Espino-Silva, P.K.; Santuario-Facio, S.K.; Ruiz-Flores, P.; Rodríguez-Padilla, C.; Reséndez-Pérez, D. Serum circulating microRNA profiling for identification of potential breast cancer biomarkers. Dis. Markers 2013, 34, 163–169. [Google Scholar] [CrossRef]
  17. Zou, X.; Xia, T.; Li, M.; Wang, T.; Liu, P.; Zhou, X.; Huang, Z.; Zhu, W. MicroRNA profiling in serum: Potential signatures for breast cancer diagnosis. Cancer Biomark. 2021, 30, 41–53. [Google Scholar] [CrossRef]
  18. Wagner, S.; Willenbrock, S.; Nolte, I.; Escobar, H.M. Comparison of non-coding RNAs in human and canine cancer. Front. Genet. 2013, 4, 46. [Google Scholar] [CrossRef] [Green Version]
  19. Sahabi, K.; Selvarajah, G.T.; Abdullah, R.; Cheah, Y.K.; Tan, G.C. Comparative aspects of microRNA expression in canine and human cancers. J. Vet. Sci. 2018, 19, 162–171. [Google Scholar] [CrossRef]
  20. Leonardi, L.; Scotlandi, K.; Pettinari, I.; Benassi, M.; Porcellato, I.; Pazzaglia, L. MiRNAs in Canine and Human Osteosarcoma: A Highlight Review on Comparative Biomolecular Aspects. Cells 2021, 10, 428. [Google Scholar] [CrossRef]
  21. Craig, K.K.L.; Wood, G.A.; Keller, S.; Mutsaers, A.J.; Wood, R.D. MicroRNA profiling in canine multicentric lymphoma. PLoS ONE 2019, 14, e0226357. [Google Scholar] [CrossRef] [Green Version]
  22. Kobayashi, M.; Saito, A.; Tanaka, Y.; Michishita, M.; Kobayashi, M.; Irimajiri, M.; Kaneda, T.; Ochiai, K.; Bonkobara, M.; Takahashi, K.; et al. MicroRNA expression profiling in canine prostate cancer. J. Vet. Med. Sci. 2017, 79, 719–725. [Google Scholar] [CrossRef] [PubMed]
  23. Boggs, R.M.; Wright, Z.M.; Stickney, M.J.; Porter, W.W.; Murphy, K.E. MicroRNA expression in canine mammary cancer. Mamm. Genome 2008, 19, 561–569. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Davis, A.P.; Grondin, C.J.; Johnson, R.J.; Sciaky, D.; Wiegers, J.; Wiegers, T.C.; Mattingly, C.J. Comparative Toxicogenomics Database (CTD): Update 2021. Nucleic Acids Res. 2020, 49, D1138–D1143. [Google Scholar] [CrossRef]
  25. Nishida, N.; Yano, H.; Nishida, T.; Kamura, T.; Kojiro, M. Angiogenesis in cancer. Vasc. Health Risk Manag. 2006, 2, 213–219. [Google Scholar] [CrossRef] [PubMed]
  26. Madu, C.O.; Wang, S.; Madu, C.O.; Lu, Y. Angiogenesis in Breast Cancer Progression, Diagnosis, and Treatment. J. Cancer 2020, 11, 4474–4494. [Google Scholar] [CrossRef]
  27. Piñero, J.; Queralt-Rosinach, N.; Bravo, À.; Deu-Pons, J.; Bauer-Mehren, A.; Baron, M.; Sanz, F.; Furlong, L.I. DisGeNET: A discovery platform for the dynamical exploration of human diseases and their genes. Database 2015, 2015, bav028. [Google Scholar] [CrossRef] [Green Version]
  28. Fish, E.J.; Irizarry, K.J.; DeInnocentes, P.; Ellis, C.J.; Prasad, N.; Moss, A.G.; Bird, R.C. Malignant canine mammary epithelial cells shed exosomes containing differentially expressed microRNA that regulate oncogenic networks. BMC Cancer 2018, 18, 832. [Google Scholar] [CrossRef]
  29. Ren, X.; Fan, Y.; Shi, D.; Tian, C.; Lu, S.; Liu, Y. Expressions of miR-502 in Canine Breast Cancer and Clinical Significance. Acta Vet. Zootech. Sin. 2020, 51, 193–197. [Google Scholar]
  30. Wang, Y.; Zeng, G.; Jiang, Y. The Emerging Roles of miR-125b in Cancers. Cancer Manag. Res. 2020, 12, 1079–1088. [Google Scholar] [CrossRef] [Green Version]
  31. Kalinina, T.S.; Kononchuk, V.V.; Yakovleva, A.K.; Alekseenok, E.Y.; Sidorov, S.V.; Gulyaeva, L.F. Association between Lymph Node Status and Expression Levels of Androgen Receptor, miR-185, miR-205, and miR-21 in Breast Cancer Subtypes. Int. J. Breast Cancer 2020, 2020, 3259393. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Sun, L.-L.; Wang, J.; Zhao, Z.-J.; Liu, N.; Wang, A.-L.; Ren, H.-Y.; Yang, F.; Diao, K.-X.; Fu, W.-N.; Wang, E.-H.; et al. Suppressive role of miR-502-5p in breast cancer via downregulation of TRAF2. Oncol. Rep. 2014, 31, 2085–2092. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Si, M.-L.; Zhu, S.; Wu, H.; Lu, Z.; Wu, F.; Mo, Y.-Y. miR-21-mediated tumor growth. Oncogene 2006, 26, 2799–2803. [Google Scholar] [CrossRef]
  34. Czubak, K.; Lewandowska, M.A.; Klonowska, K.; Roszkowski, K.; Kowalewski, J.; Figlerowicz, M.; Kozlowski, P. High copy number variation of cancer-related microRNA genes and frequent amplification of DICER1 and DROSHA in lung cancer. Oncotarget 2015, 6, 23399–23416. [Google Scholar] [CrossRef] [Green Version]
  35. Medina, P.P.; Nolde, M.; Slack, F.J. OncomiR addiction in an in vivo model of microRNA-21-induced pre-B-cell lymphoma. Nature 2010, 467, 86–90. [Google Scholar] [CrossRef]
  36. Frankel, L.B.; Christoffersen, N.R.; Jacobsen, A.; Lindow, M.; Krogh, A.; Lund, A.H. Programmed Cell Death 4 (PDCD4) Is an Important Functional Target of the MicroRNA miR-21 in Breast Cancer Cells. J. Biol. Chem. 2008, 283, 1026–1033. [Google Scholar] [CrossRef] [Green Version]
  37. Asaga, S.; Kuo, C.; Nguyen, T.; Terpenning, M.; Giuliano, A.E.; Hoon, D.S. Direct Serum Assay for MicroRNA-21 Concentrations in Early and Advanced Breast Cancer. Clin. Chem. 2011, 57, 84–91. [Google Scholar] [CrossRef] [Green Version]
  38. Zhang, W.; Sun, Z.; Su, L.; Wang, F.; Jiang, Y.; Yu, D.; Zhang, F.; Sun, Z.; Liang, W. miRNA-185 serves as a prognostic factor and suppresses migration and invasion through Wnt1 in colon cancer. Eur. J. Pharmacol. 2018, 825, 75–84. [Google Scholar] [CrossRef]
  39. Zhang, Z.; Tang, H.; Wang, Z.; Zhang, B.; Liu, W.; Lu, H.; Xiao, L.; Liu, X.; Wang, R.; Li, X.; et al. MiR-185 Targets the DNA Methyltransferases 1 and Regulates Global DNA Methylation in human glioma. Mol. Cancer 2011, 10, 124. [Google Scholar] [CrossRef] [Green Version]
  40. Holder, A.; Jones, G.; Soutter, F.; Palmer, D.B.; Aspinall, R.; Catchpole, B. Polymorphisms in the canine IL7R 3′UTR are associated with thymic output in Labrador retriever dogs and influence post-transcriptional regulation by microRNA 185. Dev. Comp. Immunol. 2018, 81, 244–251. [Google Scholar] [CrossRef] [Green Version]
  41. Xie, X.; Pang, M.; Liang, S.; Lin, Y.; Zhao, Y.; Qiu, D.; Liu, J.; Dong, Y.; Liu, Y. Cellular microRNAs influence replication of H3N2 canine influenza virus in infected cells. Vet. Microbiol. 2021, 257, 109083. [Google Scholar] [CrossRef] [PubMed]
  42. Kasimanickam, V.R.; Kasimanickam, R.K.; Dernell, W.S. Dysregulated microRNA Clusters in Response to Retinoic Acid and CYP26B1 Inhibitor Induced Testicular Function in Dogs. PLoS ONE 2014, 9, e99433. [Google Scholar] [CrossRef] [PubMed]
  43. Peng, B.; Theng, P.Y.; Le, M.T.N. Essential functions of miR-125b in cancer. Cell Prolif. 2021, 54, e12913. [Google Scholar] [CrossRef] [PubMed]
  44. Budd, W.T.; Seashols-Williams, S.; Clark, G.C.; Weaver, D.; Calvert, V.; Petricoin, E.; Dragoescu, E.A.; O’Hanlon, K.; Zehner, Z.E. Dual Action of miR-125b As a Tumor Suppressor and OncomiR-22 Promotes Prostate Cancer Tumor-igenesis. PLoS ONE 2015, 10, e0142373. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Metheetrairut, C.; Adams, B.D.; Nallur, S.; Weidhaas, J.B.; Slack, F.J. cel-mir-237 and its homologue, hsa-miR-125b, modulate the cellular response to ionizing radiation. Oncogene 2017, 36, 512–524. [Google Scholar] [CrossRef] [Green Version]
  46. Jiang, M.; Zhou, L.-Y.; Xu, N.; An, Q. Down-regulation of miR-500 and miR-628 suppress non-small cell lung cancer proliferation, migration and invasion by targeting ING1. Biomed. Pharmacother. 2018, 108, 1628–1639. [Google Scholar] [CrossRef]
  47. Zhang, L.; Ding, Y.; Yuan, Z.; Liu, J.; Sun, J.; Lei, F.; Wu, S.; Li, S.; Zhang, D. MicroRNA-500 sustains nuclear factor-kappaB activation and induces gastric cancer cell proliferation and resistance to apoptosis. Oncotarget 2015, 6, 2483–2495. [Google Scholar] [CrossRef] [Green Version]
  48. Yamamoto, Y.; Kosaka, N.; Tanaka, M.; Koizumi, F.; Kanai, Y.; Mizutani, T.; Murakami, Y.; Kuroda, M.; Miyajima, A.; Kato, T.; et al. MicroRNA-500 as a potential diagnostic marker for hepatocellular carcinoma. Biomarkers 2009, 14, 529–538. [Google Scholar] [CrossRef]
  49. Zhang, Z.; Cui, R.; Li, H.; Li, J. miR-500 promotes cell proliferation by directly targettng LRP1B in prostate cancer. Biosci. Rep. 2019, 39, BSR20181854. [Google Scholar] [CrossRef] [Green Version]
  50. Liu, B.; Zhang, X.; Song, F.; Zheng, H.; Zhao, Y.; Li, H.; Zhang, L.; Yang, M.; Zhang, W.; Chen, K. MiR-502/SET8 regulatory circuit in pathobiology of breast cancer. Cancer Lett. 2016, 376, 259–267. [Google Scholar] [CrossRef]
  51. Chen, S.; Li, F.; Chai, H.; Tao, X.; Wang, H.; Ji, A. miR-502 inhibits cell proliferation and tumor growth in hepatocellular carcinoma through suppressing phosphoinositide 3-kinase catalytic subunit gamma. Biochem. Biophys. Res. Commun. 2015, 464, 500–505. [Google Scholar] [CrossRef] [PubMed]
  52. Xu, J.; Pan, X.; Hu, Z. MiR-502 mediates esophageal cancer cell TE1 proliferation by promoting AKT phosphorylation. Biochem. Biophys. Res. Commun. 2018, 501, 119–123. [Google Scholar] [CrossRef] [PubMed]
  53. Iacona, J.R.; Monteleone, N.J.; Lutz, C.S. miR-146a suppresses 5-lipoxygenase activating protein (FLAP) expression and Leukotriene B4 production in lung cancer cells. Oncotarget 2018, 9, 26751–26769. [Google Scholar] [CrossRef] [PubMed]
  54. Kogo, R.; Mimori, K.; Tanaka, F.; Komune, S.; Mori, M. Clinical Significance of miR-146a in Gastric Cancer Cases. Clin. Cancer Res. 2011, 17, 4277–4284. [Google Scholar] [CrossRef] [Green Version]
  55. Karakatsanis, A.; Papaconstantinou, I.; Gazouli, M.; Lyberopoulou, A.; Polymeneas, G.; Voros, D. Expression of microRNAs, miR-21, miR-31, miR-122, miR-145, miR-146a, miR-200c, miR-221, miR-222, and miR-223 in patients with hepatocellular carcinoma or intrahepatic cholangiocarcinoma and its prognostic significance. Mol. Carcinog. 2013, 52, 297–303. [Google Scholar] [CrossRef]
  56. Boldin, M.P.; Taganov, K.D.; Rao, D.; Yang, L.; Zhao, J.L.; Kalwani, M.; Garcia-Flores, Y.; Luong, M.; Devrekanli, A.; Xu, J.; et al. miR-146a is a significant brake on autoimmunity, myeloproliferation, and cancer in mice. J. Exp. Med. 2011, 208, 1189–1201. [Google Scholar] [CrossRef]
  57. Foiani, G.; Guelfi, G.; Mandara, M.T. MicroRNA Dysregulation in Canine Meningioma: RT-qPCR Analysis of For-malin-Fixed Paraffin-Embedded Samples. J. Neuropathol. Exp. Neurol. 2021, 80, 769–775. [Google Scholar] [CrossRef]
  58. Wu, X.; Chen, X.; Mi, W.; Wu, T.; Gu, Q.; Huang, H. MicroRNA sequence analysis identifies microRNAs associated with peri-implantitis in dogs. Biosci. Rep. 2017, 37, BSR20170768. [Google Scholar] [CrossRef] [Green Version]
  59. Hannafon, B.N.; Cai, A.; Calloway, C.L.; Xu, Y.-F.; Zhang, R.; Fung, K.-M.; Ding, W.-Q. miR-23b and miR-27b are oncogenic microRNAs in breast cancer: Evidence from a CRISPR/Cas9 deletion study. BMC Cancer 2019, 19, 642. [Google Scholar] [CrossRef]
  60. Leone, V.; Langella, C.; D’Angelo, D.; Mussnich, P.; Wierinckx, A.; Terracciano, L.; Raverot, G.; Lachuer, J.; Rotondi, S.; Jaffrain-Rea, M.-L.; et al. miR-23b and miR-130b expression is downregulated in pituitary adenomas. Mol. Cell. Endocrinol. 2014, 390, 1–7. [Google Scholar] [CrossRef]
  61. Grossi, I.; Arici, B.; Portolani, N.; De Petro, G.; Salvi, A. Clinical and biological significance of miR-23b and miR-193a in human hepatocellular carcinoma. Oncotarget 2017, 8, 6955–6969. [Google Scholar] [CrossRef] [Green Version]
  62. Zhou, C.; Chen, Y.; He, X.; Zheng, Z.; Xue, D. Functional Implication of Exosomal miR-217 and miR-23b-3p in the Progression of Prostate Cancer. OncoTargets Ther. 2020, 13, 11595–11606. [Google Scholar] [CrossRef] [PubMed]
  63. Kou, C.-H.; Zhou, T.; Han, X.-L.; Zhuang, H.-J.; Qian, H.-X. Downregulation of mir-23b in plasma is associated with poor prognosis in patients with colorectal cancer. Oncol. Lett. 2016, 12, 4838–4844. [Google Scholar] [CrossRef] [PubMed]
  64. Campos-Viguri, G.E.; Jiménez-Wences, H.; Peralta-Zaragoza, O.; Torres-Altamirano, G.; Soto-Flores, D.G.; Hernández-Sotelo, D.; Alarcón-Romero, L.D.C.; Jiménez-López, M.A.; Illades-Aguiar, B.; Fernández-Tilapa, G. miR-23b as a potential tumor suppressor and its regulation by DNA methylation in cervical cancer. Infect. Agents Cancer 2015, 10, 42. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Chen, W.X.; Hu, Q.; Qiu, M.T.; Zhong, S.L.; Xu, J.J.; Tang, J.H.; Zhao, J.H. miR-221/222: Promising biomarkers for breast cancer. Tumour Biol. 2013, 34, 1361–1370. [Google Scholar] [CrossRef] [PubMed]
  66. Yang, C.-J.; Shen, W.G.; Liu, C.-J.; Chen, Y.-W.; Lu, H.-H.; Tsai, M.-M.; Lin, S.-C. miR-221 and miR-222 expression increased the growth and tumorigenesis of oral carcinoma cells. J. Oral Pathol. Med. 2011, 40, 560–566. [Google Scholar] [CrossRef]
  67. Song, J.; Ouyang, Y.; Che, J.; Li, X.; Zhao, Y.; Yang, K.; Zhao, X.; Chen, Y.; Fan, C.; Yuan, W. Potential Value of miR-221/222 as Diagnostic, Prognostic, and Therapeutic Biomarkers for Diseases. Front. Immunol. 2017, 8, 56. [Google Scholar] [CrossRef] [Green Version]
  68. Zhong, S.; Li, W.; Chen, Z.; Xu, J.; Zhao, J. miR-222 and miR-29a contribute to the drug-resistance of breast cancer cells. Gene 2013, 531, 8–14. [Google Scholar] [CrossRef]
  69. Luo, W.; Fang, M.; Xu, H.; Xing, H.; Fu, J.; Nie, Q. Comparison of miRNA expression profiles in pituitary–adrenal axis between Beagle and Chinese Field dogs after chronic stress exposure. PeerJ 2016, 4, e1682. [Google Scholar] [CrossRef] [Green Version]
  70. Lange-Consiglio, A.; Perrini, C.; Albini, G.; Modina, S.; Lodde, V.; Orsini, E.; Esposti, P.; Cremonesi, F. Oviductal microvesicles and their effect on in vitro maturation of canine oocytes. Reproduction 2017, 154, 167–180. [Google Scholar] [CrossRef]
  71. Fowles, J.S.; Brown, K.C.; Hess, A.M.; Duval, D.L.; Gustafson, D.L. Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma. BMC Bioinform. 2016, 17, 93. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  72. Gupta, G.; Chellappan, D.K.; de Jesus Andreoli Pinto, T.; Hansbro, P.M.; Bebawy, M.; Dua, K. Tumor suppressor role of miR-503. Panminerva Med. 2018, 60, 17–24. [Google Scholar] [CrossRef] [PubMed]
  73. Sun, Q.; Li, Q.; Xie, F. LncRNA-MALAT1 regulates proliferation and apoptosis of ovarian cancer cells by targeting miR-503-5p. Onco Targets Ther. 2019, 12, 6297–6307. [Google Scholar] [CrossRef] [PubMed]
  74. Fu, Y.; Meng, Y.; Gu, X.; Tian, S.; Hou, X.; Ji, M. miR-503 expression is downregulated in cervical cancer and suppresses tumor growth by targeting AKT2. J. Cell. Biochem. 2019, 120, 8177–8184. [Google Scholar] [CrossRef] [PubMed]
  75. Kim, D.; Langmead, B.; Salzberg, S.L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods 2015, 12, 357–360. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Pertea, M.; Pertea, G.M.; Antonescu, C.M.; Chang, T.-C.; Mendell, J.T.; Salzberg, S.L. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 2015, 33, 290–295. [Google Scholar] [CrossRef] [Green Version]
  77. Kanehisa, M.; Sato, Y.; Kawashima, M. KEGG mapping tools for uncovering hidden features in biological data. Protein Sci. 2022, 31, 47–53. [Google Scholar] [CrossRef]
  78. Yu, G.; Wang, L.-G.; Han, Y.; He, Q.-Y. clusterProfiler: An R Package for Comparing Biological Themes among Gene Clusters. OMICS 2012, 16, 284–287. [Google Scholar] [CrossRef]
  79. Wong, N.; Wang, X. miRDB: An online resource for microRNA target prediction and functional annotations. Nucleic Acids Res. 2015, 43, D146–D152. [Google Scholar] [CrossRef]
Ijms 24 02618 g001 550
Figure 1. Volcano plot in canine mammary gland tumor for array-based miRNA. Red dots indicate upregulated miRNAs and grey dots indicate unsignificant miRNAs. Red and blue dotted line are threshold of fold change and black dotted line is threshold of p-value. (A) BCMT-T vs. BCMT-N; (B) MCMT-T vs. MCMT-N; (C) MCMT-T vs. BCMT-T (Figure 2).
Figure 1. Volcano plot in canine mammary gland tumor for array-based miRNA. Red dots indicate upregulated miRNAs and grey dots indicate unsignificant miRNAs. Red and blue dotted line are threshold of fold change and black dotted line is threshold of p-value. (A) BCMT-T vs. BCMT-N; (B) MCMT-T vs. MCMT-N; (C) MCMT-T vs. BCMT-T (Figure 2).
Ijms 24 02618 g001
Ijms 24 02618 g002 550
Figure 2. Pathway analysis in breast cancer. Red boxes indicate miRNAs detected in the pathway among DE-miRNAs. (A) BCMT-T vs. BCMT-N; (B) MCMT-T vs. MCMT-N.
Figure 2. Pathway analysis in breast cancer. Red boxes indicate miRNAs detected in the pathway among DE-miRNAs. (A) BCMT-T vs. BCMT-N; (B) MCMT-T vs. MCMT-N.
Ijms 24 02618 g002
Ijms 24 02618 g003 550
Figure 3. Enrichment analysis for GO_BP, GO_CC, GO_MF, and KEGG. GO_BP: Biological processing; GO_CC: Cellular component; GO_MF: Molecular function of Gene Ontology; KEGG: Biological pathway. (A) BCMT-T vs. BCMT-N; (B) MCMT-T vs. MCMT-N.
Figure 3. Enrichment analysis for GO_BP, GO_CC, GO_MF, and KEGG. GO_BP: Biological processing; GO_CC: Cellular component; GO_MF: Molecular function of Gene Ontology; KEGG: Biological pathway. (A) BCMT-T vs. BCMT-N; (B) MCMT-T vs. MCMT-N.
Ijms 24 02618 g003
Ijms 24 02618 g004 550
Figure 4. Venn diagrams showing GO_BP, GO_CC, and GO_MF of miRNAs. GO_BP: Biological processing; GO_CC: Cellular component; GO_MF: Molecular function of Gene Ontology; (A) BCMT-T vs. BCMT-N; (B) MCMT-T vs. MCMT-N.
Figure 4. Venn diagrams showing GO_BP, GO_CC, and GO_MF of miRNAs. GO_BP: Biological processing; GO_CC: Cellular component; GO_MF: Molecular function of Gene Ontology; (A) BCMT-T vs. BCMT-N; (B) MCMT-T vs. MCMT-N.
Ijms 24 02618 g004
Ijms 24 02618 g005 550
Figure 5. Volcano plot in canine mammary gland tumor for mRNA. Red dots indicate upregulation, blue dots indicate downregulation and grey dots indicate unsignificant miRNAs. Red and blue dotted line are threshold of fold change and black dotted line is threshold of p-value. (A) BCMT-T vs. BCMT-N; (B) MCMT-T vs. MCMT-N.
Figure 5. Volcano plot in canine mammary gland tumor for mRNA. Red dots indicate upregulation, blue dots indicate downregulation and grey dots indicate unsignificant miRNAs. Red and blue dotted line are threshold of fold change and black dotted line is threshold of p-value. (A) BCMT-T vs. BCMT-N; (B) MCMT-T vs. MCMT-N.
Ijms 24 02618 g005
Ijms 24 02618 g006 550
Figure 6. Venn diagram of commonly appearing genes between miRNAs and their targeted mRNAs. (A) BCMT-T vs. BCMT-N; (B) MCMT-T vs. MCMT-N.
Figure 6. Venn diagram of commonly appearing genes between miRNAs and their targeted mRNAs. (A) BCMT-T vs. BCMT-N; (B) MCMT-T vs. MCMT-N.
Ijms 24 02618 g006
Table
Table 1. The mRNA expression profile of miRNA-target genes.
Table 1. The mRNA expression profile of miRNA-target genes.
BCMT-T vs. BCMT-N
GeneFold Changep-Value
BAK11.1340.390
CDKN1B−1.2560.167
PDCD4−2.486<0.001
SERPINB51.0810.838
SOCS1−2.1830.005
TPM1−1.2490.177
MCMT-T vs. MCMT-N
GeneFold Changep-Value
CDKN1B−1.6120.025
Table
Table 2. Intersection and breast cancer related genes of miRNA and mRNA. Bold type indicates breast cancer related gene.
Table 2. Intersection and breast cancer related genes of miRNA and mRNA. Bold type indicates breast cancer related gene.
ComparisonGenes
BCMT-T vs. BCMT-NACSM5, ALDH1A1, ANTXR2, F2RL2, FOS, KLF4, LEF1, NOVA1, PDGFRB, RORA, SDK1, TRIM46
MCMT-T vs. MCMT-NALDH1A2, ANK2, APLN, B3GNT7, BEND6, CHEK1, CHST1, CKAP4, DCBLD2, DCLK1, ECSCR, ELF5, ELL2, FAM81A, FBXO5, FHL1, GJA1, GJC1, GPAM, HSPA4L, HUNK, IGF1, JPH1, KCNQ5, KLHDC1, LONRF3, MAP3K1, MEGF10, MMD, MMP13, MYBL1, NCKAP5, NOVA1, NUAK2, PCOLCE2, PGAM1, PRUNE2, PTPRD, RIMS3, RORA, RTKN2, SEC23A, SEMA6D, SEMA7A, SESN1, SIMC1, SLC6A6, SLC7A11, ST6GALNAC3, TEF, TMEM51, TMOD2, VGLL3, WASF3
Table
Table 3. Animals used for sample collection.
Table 3. Animals used for sample collection.
Tumor ClassificationIDBreedSexAge (Years)Histopathological FeaturesNormal Tissue
Benign CMTBCMT-1Shih-tzuF *7Benign mixed mammary tumorY
BCMT-2Alaskan malamuteF6Benign mammary complex adenomaY
BCMT-3Shih-tzuFS **11Benign mixed mammary tumorY
BCMT-4Toy poodleFS12Mammary adenomaY
BCMT-5MalteseF10Simple adenomaY
BCMT-6Bichon friesF6Benign mammary lobular hyperplasiaY
BCMT-7DachshundF11Complex mammary adenomaY
BCMT-8MixedFS16Benign mammary adenomaY
BCMT-9MalteseFS11Complex mammary adenomaN
BCMT-10Cocker spanielF10Complex mammary tubular adenomaN
BCMT-11MalteseFS14Benign mammary complex adenomaN
BCMT-12PoodleFS11Benign mammary complex adenomaN
Malignant CMTMCMT-1PomeranianF12Mammary ductular adenocarcinoma, low grade (grade 1)Y
MCMT-2MalteseF12Mammary adenocarcinoma, high gradeY
MCMT-3Shih-tzuFS11Tubulopapillary mammary gland carcinoma, low grade (grade 1)Y
MCMT-4BeagleFS11Mammary ductular adenocarcinoma, low grade (grade 1)N
MCMT-5MixedF8Multinodular mammary gland adenocarcinomaY
MCMT-6PoodleFS11Mammary ductular adenocarcinoma, low grade (grade 1)N
MCMT-7PoodleFS13Mammary adenocarcinoma, high gradeN
MCMT-8Shih-tzuF11Mammary carcinoma, complex type (grade 1)N
* F: Female; ** FS: Spayed Female; Y: Collected; N: Not Collected.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kim, E.P.; Jang, G.; Kim, J.-W.; Kim, S.W.; Chung, H.; Yang, Y.J.; Kim, W.H.; Kim, G.A. MicroRNA and Messenger RNA Expression Profiles in Canine Mammary Gland Tumor. Int. J. Mol. Sci. 2023, 24, 2618. https://doi.org/10.3390/ijms24032618

AMA Style

Kim EP, Jang G, Kim J-W, Kim SW, Chung H, Yang YJ, Kim WH, Kim GA. MicroRNA and Messenger RNA Expression Profiles in Canine Mammary Gland Tumor. International Journal of Molecular Sciences. 2023; 24(3):2618. https://doi.org/10.3390/ijms24032618

Chicago/Turabian Style

Kim, Eun Pyo, Giup Jang, Jin-Wook Kim, Sang Wha Kim, Heaji Chung, Yun Jung Yang, Wan Hee Kim, and Geon A Kim. 2023. "MicroRNA and Messenger RNA Expression Profiles in Canine Mammary Gland Tumor" International Journal of Molecular Sciences 24, no. 3: 2618. https://doi.org/10.3390/ijms24032618

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop