RNA interaction and expression analysis of UHRF1 in breast cancer, gastric cancer, and colorectal cancer patients: systems biology investigation and experimental validation

doi:10.21203/rs.3.rs-4271471/v1

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RNA interaction and expression analysis of UHRF1 in breast cancer, gastric cancer, and colorectal cancer patients: systems biology investigation and experimental validation

https://doi.org/10.21203/rs.3.rs-4271471/v1

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Background

Breast cancer is the most commonly diagnosed cancer in women and ranks as the second most prevalent cancer globally. The prevalence of gastric cancer (GC) varies substantially between men and women, as well as in different countries. Male rates are two to three times higher than female rates. Colorectal cancer ranks as the third most common cancer globally and is the second leading cause of death related to cancer. In this study, our objective was to identify new non-coding biomarkers for breast cancer, gastric cancer, and colorectal cancer.

Method

Microarray analysis was performed to find the central protein-coding gene with the dysregulation in BC, GC, and CRC. Using ENCORI, validation of microarray analysis and survival analysis was utilized. RNA and protein interaction was performed by miRWalk, lncRRIsearch, and STRING. Signaling pathways were identified using Enrichr and Reactome databases. To validate the expression analysis and confirm the biomarker potential of RNAs, a qRT-PCR experiment was conducted.

Results

Based on microarray analysis, UHRF1 has a significant high expression in BC, CC, and GC. UHRF1 modulates DNA methylation and gene expression signaling pathways. lncRNAs EMX2OS and ZNF213-AS1 have interaction with UHRF1 mRNA. miR-4479 suppresses the expression of UHRF1 with interaction to 3’UTR region. qRT-PCR validates bioinformatics expression analysis. Furthermore, ROC analysis suggested that UHRF1, EMX2OS, and ZNF213-AS1 could potentially be used as diagnostic biomarkers for breast cancer, gastric cancer, and colorectal cancer.

Conclusion

miR-4479, lncRNAs EMX2OS, and ZNF213-AS1 regulate the DNA methylation signaling pathway via interaction with UHRF1. UHRF1, EMX2OS, and ZNF213-AS1 may be regarded as potential diagnostic biomarkers for breast cancer, gastric cancer, and colorectal cancer.

Bioinformatics

Cancer Biology

microRNA

lncRNA

Systems Biology

RNA Interaction

Microarray

UHRF1

Breast cancer is the most common cancer among women worldwide and ranks second among all newly diagnosed cancers. Extensive research suggests that lifestyle and environmental factors, such as a high-fat diet, alcohol consumption, and insufficient physical activity, contribute significantly to the development of breast cancer. By addressing these factors through primary prevention, it is possible to reduce both morbidity and mortality. It is estimated that environmental influences are responsible for approximately 70% of all cancer cases, with breast cancer constituting 90–95% of these instances (1).

The incidence of gastric cancer (GC) varies greatly between men and women and between nations. Men have rates that are two to three times greater than women (2). When comparing countries, East Asia, East Europe, and South America have the greatest incidence rates, whereas North America and most of Africa have the lowest rates (3). Colorectal cancer ranks as the third most common disease worldwide and the second leading cause of cancer-related deaths. In 2018, it was estimated that there were 1.8 million new cases and 881,000 deaths from colorectal cancer. The epidemiology of CRC varies greatly throughout parts of the world, as well as with age, gender, and racial groups (4).

Cancer biomarkers are widely utilized in cancer treatment and monitoring of disease progression, therapeutic response, relapses, and drug resistance. The majority of biomarkers investigated are in the field of cancer. Extensive research is being conducted utilizing various methods/tissues to uncover biomarkers for early detection, which has been generally fruitless (5). Different studies revealed coding or non-coding cancer biomarkers using different approaches. MicroRNAs (miRNAs) are non-coding RNA molecules with a single strand that influence gene expression post-transcriptionally by binding to messenger RNAs.

MiRNAs are key gene expression regulators, and their dysregulation has been linked to a variety of human and canine disorders. MiRNA-based medicines have also been explored in several malignancies, with substantial therapeutic advantages for patients. Furthermore, studying miRNA production and regulatory mechanisms in cancer might give vital information regarding chemotherapeutic resistance, leading to more customized cancer treatment (6). Long noncoding RNAs (lncRNAs) have been shown in recent research to have essential roles in cancer spread. Nonprotein coding RNAs with a length of more than 200 nucleotides are referred to as lncRNAs. More and more research has found that lncRNAs are involved in a wide range of biological processes and are linked to a variety of disorders, including cancer (7).

In this investigation, we performed an integrated systems biology investigation to find novel coding and non-coding biomarkers of GC, BC, and CRC. Using a high-throughput data analysis, a significant potential coding gene has been selected. Furthermore, through RNA and protein interaction analyses, potential novel non-coding RNAs that might regulate selected genes had been selected, and the expression level of them had been evaluated through bioinformatics and experimental evaluations.

2.1. Microarray analysis

High-throughput microarray data analysis was performed to find novel coding potential biomarkers of GC, BC, and CRC. For selecting the differentially expressed genes (DEGs) in BC, GC, and CRC, the following microarray datasets were selected: GSE10810, GSE54129, and GSE81558. Table 1 shows the characteristics of the mentioned datasets. Using the affy package, raw microarray datasets were analyzed. Raw data normalization was conducted using the Robust Multi-array Average (RMA) method from the affy package. The quality of the raw dataset was assessed using principal component analysis (PCA), sample correlation analysis, and boxplots of expression data for each normal and tumor sample. The limma package was utilized for statistical analysis of microarray expression data. Both the affy and limma packages were obtained from Bioconductor.org. Visualization of microarray analysis plots was achieved using the ggplot2 and pheatmap packages, which were downloaded from https://cran.r-project.org/. Gene expression analysis was validated through the online databases GEPIA2 (http://gepia2.cancer-pku.cn/) and ENCORI (https://rnasysu.com/encori/).

Table 1

Characteristics of microarray datasets in this study. Three datasets were used in this study.
dataset	tumor samples	control samples	platform	disease	reference
GSE10810	31	27	GPL570	breast cancer	(8)
GSE54129	111	21	GPL570	gastric cancer	-
GSE81558	42	9	GPL15207	colorectal cancer	(9)

2.2. RNA interaction and enrichment analyses

In this study, different online software was used for selecting novel non-coding regulatory biomarkers in BC, GC, and CRC samples. Using lncRRIsearch online software (http://rtools.cbrc.jp/LncRRIsearch) (10), novel regulatory lncRNAs had been selected. For understanding the protein interaction network of selected mRNA, the STRING online database (https://string-db.org/) (11) was used. For finding novel regulatory miRNAs, miRWalk online software was used (http://mirwalk.umm.uni-heidelberg.de/) (12–14). Using Cytoscape software, the RNA interaction network was visualized (15, 16). Pathway enrichment analysis was performed by enrichr (https://maayanlab.cloud/Enrichr/) (17, 18) and Reactome (https://reactome.org/) (19–21) online databases. To understand the correlation between the expression level of targets with the survival rate of cancer patients, survival analysis was performed by ENCORI online database.

2.3. Clinical features of human samples for qRT-PCR experiment

The Al-Zahra Hospital Ethics Committee of the Isfahan University of Medical Science authorized all research methodologies involving human samples in this investigation, and all patients completed written consent forms. In a case-control study, the expression levels of specific mRNA and lncRNAs in 20 BC, CRC, and GC tissue samples were compared to 20 nearby healthy tissue samples in each group. None of the patients had ever had chemotherapy or radiation. Tissue samples were thoroughly cleansed in distilled water before being frozen in liquid nitrogen for pathologist evaluation in the RNA Later solution (Invitrogen, USA). Clinicopathological characteristic of human samples is provided in Table 2–4.

Table 2

Clinical characteristics of BC samples.
Variable	Status	Number	%
Stage	I	0	0
	II	6	30
	III	12	60
	IV	0	0
	Unknown	2	10
Age	< 45	10	50
	> 45	8	40
	Unknown	2	10
Tumor size (TS)	< 5cm	12	60
	> 5cm	6	30
	Unknown	2	10
Menopausal status	Yes	18	90
	No	2	10
	Unknown	0	0
Lymph node	Yes	16	80
	No	2	10
	Unknown	2	10
ER receptor	Positive	8	40
	Negative	7	35
	Unknown	5	25
PR receptor	Positive	6	30
	Negative	9	45
	Unknown	5	25
HER2/neu receptor	Positive	10	50
	Negative	5	25
	Unknown	5	25

Table 2

Clinicopathological characteristic of GC samples.
Variable	Status	Number	%
Age	< 50	8	40
Age	> 50	12	60
Sex	Male	18	90
Sex	Female	2	10
Tumor Size	< 5 cm	10	50
Tumor Size	> 5 cm	10	50
Histology	Adenocarcinoma	18	90
	Mucinous Adenocarcinoma	1	5
	Singet Ring Carcinoma	1	5
Perineural Invasion	No	6	30
Perineural Invasion	Yes	14	70
Nodal Extension	No	16	80
Nodal Extension	Yes	4	20
TNM Staging	I	1	5
	II	6	30
	IIIA	2	10
	IIIB	4	20
	IV	7	35
Family History	No	14	70
Family History	Yes	6	30
Smoking	DX-Smoker at Diagnosis but Discontinued	2	10
	Ex-Smoker	2	10
	Non-Smoker	15	75
	smoker	1	5

Table 3

Clinicopathological table of colorectal cancer patients.
Variable	Status	Number	%
Stage	I	2	10
	II	3	15
	III	7	35
	IV	8	40
	Unknown	0	0
Age	< 50	5	25
	> 50	15	75
	Unknown	0	0
Tumor size (TS)	< 5cm	9	45
	> 5cm	11	55
	Unknown	0	0
Lymphatic Invasion	Yes	8	40
	No	11	55
	Unknown	1	5
Perineural Invasion	Yes	12	60
	No	8	40
	Unknown	0	0
Smoking	Non-smoker	17	85
	Smoker	3	15
	Unknown	0	0
Sex	Female	11	55
	Male	9	45
	Unknown	0	0

2.4. Statistical analyses

Statistical analysis of the qRT-PCR experiment was performed by Graph Pad Prism 8. Paired t-test and unpaired t-test were performed to find the significance level. Receiver operating characteristic (ROC) analysis was performed to find the diagnostic capability of tumor samples. In the ROC results, the area under the curve (AUC) is evaluated. An AUC between 0.7 and 0.8 shows an acceptable biomarker, an AUC between 0.8 and 0.9 demonstrates a good biomarker, and an AUC between 0.9 and 1 shows an excellent diagnostic biomarker.

3.1. Microarray analysis

Microarray analysis was performed on three high-throughput datasets to find novel diagnostic biomarkers of BC, GC, and CRC. Principal component analysis (PCA) was performed to evaluate the quality of microarray samples. Based on PCA analysis for BC (Fig. 1A), GC (Fig. 1B), and CRC (Fig. 1C), all microarray samples have suitable quality and are ready for further analysis. Differential expression analysis (DEA) was performed to find novel shared potential diagnostic biomarkers of BC, GC, and CRC. Based on the mentioned analysis, UHRF1 has a significant up-regulation in BC, GC, and CRC (Fig. 2). Figure 3 shows the top differentially expressed genes (DEGs) in tumor samples.

3.2. Protein interaction and pathway analysis

Protein interaction analysis revealed the protein interactome of UHRF1 (Fig. 4). Pathway enrichment analysis was performed on the mentioned interactome to find a more accurate regulatory mechanism of UHRF1. Based on pathway enrichment analysis, UHRF1 regulates DNA methylation and epigenetics regulation of gene expression signaling pathways (Fig. 5). A list of related biological processes, molecular functions, and cellular components of UHRF1 is provided in Table 4.

Table 4

Gene ontology analysis of UHRF1 and its protein interactome.
Pathway enrichment (Reactome)
Term	Adjusted P-value	Odds Ratio	Genes
Epigenetic Regulation Of Gene Expression R-HSA-212165	1.44E-09	271.0909091	DNMT1;HDAC1;UHRF1;EHMT2;DNMT3A;EP300
Gene Expression (Transcription) R-HSA-74160	3.07E-06	51.48924358	RB1;DNMT1;KAT5;HDAC1;UHRF1;EHMT2;DNMT3A;EP300
Chromatin Modifying Enzymes R-HSA-3247509	3.83E-06	84.79399142	KAT5;HDAC1;EHMT2;DNMT3A;EP300
DNA Methylation R-HSA-5334118	2.79E-05	275.9308756	DNMT1;UHRF1;DNMT3A
Regulation Of TP53 Activity R-HSA-5633007	3.09E-05	86.43572985	KAT5;HDAC1;EHMT2;EP300

GO (Biological Process)
Term	Adjusted P-value	Odds Ratio	Genes
Negative Regulation Of Transcription By RNA Polymerase II (GO:0000122)	3.75E-06	59.36419753	RB1;KAT5;HDAC1;UHRF1;EHMT2;DNMT3A;EP300
Negative Regulation Of DNA-templated Transcription (GO:0045892)	1.44E-05	43.48526523	RB1;KAT5;HDAC1;UHRF1;EHMT2;DNMT3A;EP300
Negative Regulation Of Gene Expression, Epigenetic (GO:0045814)	1.86E-04	178.0535714	RB1;DNMT1;UHRF1
Internal Protein Amino Acid Acetylation (GO:0006475)	4.07E-04	832.6666667	KAT5;EP300
Histone Modification (GO:0016570)	4.07E-04	109.4065934	KAT5;HDAC1;EP300

GO (Molecular function)
Term	Adjusted P-value	Odds Ratio	Genes
Nucleus (GO:0005634)	0.001683855	13.85219915	RB1;DNMT1;KAT5;HDAC1;UHRF1;EHMT2;DNMT3A;EP300
Euchromatin (GO:0000791)	0.001683855	118.7380952	UHRF1;DNMT3A
Intracellular Membrane-Bounded Organelle (GO:0043231)	0.002852689	11.47513064	RB1;DNMT1;KAT5;HDAC1;UHRF1;EHMT2;DNMT3A;EP300
Swr1 Complex (GO:0000812)	0.020958323	201.8080808	KAT5
Sin3 Complex (GO:0016580)	0.020958323	110.9444444	HDAC1

GO (Cellular component)
Term	Adjusted P-value	Odds Ratio	Genes
DNA-binding Transcription Factor Binding (GO:0140297)	7.68E-06	71.16606498	RB1;KAT5;HDAC1;DNMT3A;EP300
Transcription Coregulator Binding (GO:0001221)	4.13E-04	88.8125	HDAC1;EHMT2;EP300
Histone H4 Acetyltransferase Activity (GO:0010485)	7.85E-04	277.3888889	KAT5;EP300
NF-kappaB Binding (GO:0051059)	7.85E-04	207.9791667	HDAC1;EP300
Histone Acetyltransferase Activity (GO:0004402)	7.85E-04	199.65	KAT5;EP300

3.3. Non-coding RNA interaction

MiRNA-mRNA interaction analysis was performed to find novel regulatory RNA for UHRF1. miRNAs with binding probability (score) of 1, interaction in the seed region, and lower interaction energy were selected. Based on miRNA interaction, miR-4479 (score: 1, energy: -30.7) has a significant miRNA interaction with the 3’UTR of UHRF1 mRNA (Fig. 6). A list of the top 20 regulatory miRNAs for UHRF1 is provided in Table 5. lncRNA interaction with lncRRIsearch revealed that UHRF1 has a significant interaction with EMX2OS and ZNF213-AS1 lncRNAs.

Table 5

miRNA interaction analysis of UHRF1 using miRWalk.
miRNA	symbol	score	energy	seed	position
miR-4479	UHRF1	1	-30.7	1	3UTR
miR-3648	UHRF1	1	-30.6	1	3UTR
miR-4740-5p	UHRF1	1	-30.6	1	3UTR
miR-1292-3p	UHRF1	1	-30.5	1	3UTR
miR-6769a-3p	UHRF1	1	-30.4	1	3UTR
miR-181d-3p	UHRF1	1	-29.5	1	3UTR
miR-5010-5p	UHRF1	1	-28.9	1	3UTR
miR-6735-3p	UHRF1	1	-28.8	1	3UTR
miR-7843-5p	UHRF1	1	-28.8	1	3UTR
miR-4739	UHRF1	1	-28.7	1	3UTR
miR-10396a-3p	UHRF1	1	-28.5	1	3UTR
miR-7846-3p	UHRF1	1	-28.3	1	3UTR
miR-3132	UHRF1	1	-28.1	1	3UTR
miR-6798-5p	UHRF1	1	-28	1	3UTR
miR-7160-5p	UHRF1	1	-27.9	1	3UTR
miR-1199-5p	UHRF1	1	-27.8	1	3UTR
miR-4663	UHRF1	1	-27.7	1	3UTR
miR-6735-5p	UHRF1	1	-27.7	1	3UTR
miR-6822-3p	UHRF1	1	-27.3	1	3UTR
miR-7109-5p	UHRF1	1	-27.1	1	3UTR

3.4. Non-coding expression analysis

Based on expression analysis by ENCORI, UHRF1 and ZNF213-AS1 exhibit significantly elevated expression in BC, GC, and CRC. EMX2OS also shows significantly low expression in GC, BC, and CRC (Fig. 7). Survival analysis indicated a significant correlation between low expression of UHRF1 and improved survival rates in GC patients (p-value: 0.016, HR: 0.67). Conversely, there was a non-significant correlation between high expression of ZNF213-AS1 and lower survival rates in BC, GC, and CRC. Additionally, a significant positive correlation was found between the expression level of EMX2OS and survival rates in GC patients.

3.5. Validation of expression analysis by qRT-PCR

qRT-PCR experiment revealed that UHRF1 and ZNF213-AS1 have significantly high expression in BC, CRC, and GC. Based on the experiment, EMX2OS was found to be significantly underexpressed in GC, CRC, and BC (Fig. 9). Obtained experimental results validate bioinformatics investigations. ROC analysis also indicated that UHRF1, ZNF213-AS1, and EMX2OS could potentially serve as effective diagnostic biomarkers of BC, GC, and CRC (Fig. 10). Table 6 provides the statistical information on expression and ROC analysis.

Table 6

Statistical information of gene expression and ROC analyses, based on qRT-PCR data.
		expression		ROC
gene	disease	logFC	p-value	AUC	p-value
UHRF1	BC	3.0700	< 0.0001	0.8900	< 0.0001
	GC	2.9920	0.0027	0.7975	0.0013
	CC	2.7470	0.0009	0.7450	0.0080
EMX2OS	BC	-2.6090	0.0070	0.7775	0.0027
	GC	-3.1230	0.0009	0.7875	0.0019
	CC	-3.3610	< 0.0001	0.8725	< 0.0001
ZNF213-AS1	BC	2.7240	0.0034	0.8175	0.0006
	GC	3.0050	0.0099	0.7350	0.1100
	CC	2.0640	0.0164	0.7700	0.0035

UHRF1, an epigenetic regulator present in proliferating cancer cells, interacts with AMPK, inhibiting its activity in both normal and stress conditions. As a nuclear protein, UHRF1 promotes the retention of AMPK in the nucleus and significantly reduces its activity against substrates such as H2B and EZH2. Additionally, UHRF1 effectively decreases AMPK activity in the cytoplasm, likely as a result of the nucleocytoplasmic shuttling of AMPK (22). Previous studies revealed possible effects of UHRF1 protein in the different cancer types. For example, Q et al. in 2019 revealed that UHRF1 knockdown drastically reduced aerobic glycolysis in pancreatic cancer cells. Furthermore, they found that UHRF1 knockdown also reduced hypoxia-inducible factor (HIF)1 levels and HIF1 targeting glycolytic genes (23). Based on the study of Yin et al. in 2018, UHRF1/BRCA1 complex is one of the main targets of poly ADP ribose polymerase (PARP) inhibitor and histone deacetylase (HDAC) inhibitor (24).

Previous studies also mentioned the possible roles of UHRF1 in the regulation of BC, GC, and CRC. In mammalian cells, UHRF1 aids in the creation and maintenance of DNA methylation patterns. The establishment domains, including E3 ligase activity, are well described, whereas the maintenance domains are less understood. In human CRC cells, UHRF1's ability to bind histone- and hemimethylated DNA, but not its E3 ligase activity, maintains cancer-specific DNA methylation patterns. Disruption of these chromatin reader activities leads to the restoration of DNA hypermethylation, reactivation of epigenetically silenced tumor suppressor genes (TSGs), and a reduction in the oncogenic properties of CRC (25). Y et al. 2012 reported that UHRF1 has a significant role in the cellular proliferation biological process and might affect CRC risk through this pathway (26). Based on the study of Y et al. in 2019, miR-506 could have a significant regulatory effect on the KISS1/PI3K/NF-kB signaling pathway through silencing UHRF1 in CRC patients (27). The report of J et al. in 2021 revealed the possible effect of UHRF1 silencing on the STAT1 and DNMT1 regulation and inhibition of CRC growth (28). Our results are consistent with the mentioned previous studies. Based on our results, up-regulation of UHRF1 has a significant correlation with the higher risk of CRC.

Based on the study of Luo G. et al. in 2022, UHRF1 regulates the estrogen signaling pathway and could regulate cell growth in BC patients (29). UHRF1 could enhance BC development through KLF17 suppression. This suppression happens using promoter hypermethylation (30). In GC patients, miR-146a/b could regulate GC invasion and metastasis by targeting UHRF1 (31).

No prior research has explored the potential roles of ZNF213-AS1 in the development of breast cancer, gastric cancer, and colorectal cancer. Based on our study, EMX2OS and ZNF213-AS1 are the two potential regulatory diagnostic biomarkers of BC, GC, and CRC. The mentioned lncRNAs might affect the DNA methylation and gene expression signaling pathways through the regulation of UHRF1 expression level. Furthermore, we found that miR-4479 could modulate DNA methylation and gene expression signaling pathways via negative regulation of UHRF1. No previous research has investigated the potential role of miR-4479 in BC, CRC, and GC. However, previous studies revealed the potential roles of this microRNA in ovarian cancer and lung cancer. Based on the study of Wang et al. in 2022, miR-4479 has a significantly low expression in epithelial ovarian cancer (EOC) patients. Furthermore, miR-4479 could be considered as a potential diagnostic biomarker of EOC (32). According to the 2022 study by Chakraborty et al., miR-4479 targets genes that are up-regulated and overexpressed in lung cancer (33). Our previous studies also revealed potential coding and non-coding biomarkers of BC (34–37) and GC (38).

Based on previous studies, lncRNA EMX2OS regulates the invasion and regulation of ovarian cancer cells through the regulation of PD-L1/AKT3/miR-654-3p (39). In The GC samples, EMX2OS could be an enhancer RNA and regulate the prognosis of EMX2OS (40) In 2021, Molaei Ramshe et al. conducted a study on the expression level of EMX2OS in breast cancer patients and found no significant change in the expression levels of EMX2OS in breast cancer samples (41). However, more studies are needed for the validation of the obtained results in this experiment.

EMX2OS and ZNF213-AS1 lncRNAs are the two novel diagnostic biomarkers of BC, GC, and CRC as the two dysregulated non-coding RNA. These two lncRNAs modulate DNA methylation and gene expression signaling pathways through the regulation of UHRF1. UHRF1 is a potential diagnostic biomarker and oncogene of BC, GC, and CRC. miR-4479 also regulates UHRF1 and could affect the DNA methylation signaling pathway.

6.1 Ethics approval: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the Ethics Committee of Isfahan University of Medical Sciences.

6.2 Consent for publication: Informed consent was obtained from all individual participants included in the study.

6.3 Availability of data and materials: The datasets generated or analyzed during the current study are available in the GEO repository, GSE10810, GSE54129, and GSE81558.

6.4 Conflicts of interest: The authors declare that they have no competing interests.

6.5 Financial support and sponsorship: Not applicable.

6.6 Authors’ contribution: Dorna Dayani, Simin Sharifi, Seyedeh Solmaz Mohammadi, Masoumeh Ghafourzadeh, Sheida Bahrami, Melika Azaripour, Nasim Karimi, and Ali Ghaneh: Software, Formal Analysis, Investigation, Data Curation, Writing – Original Draft, Visualization; Mohammad Rezaei and Seyedeh Zahra Shirdeli: Writing – Review & Editing, Conceptualization, Methodology, Validation, Supervision; Mansoureh Azadeh: Writing – Review & Editing, Conceptualization, Methodology, Validation, Resources, Project Administration, Resources. Dorna Dayani, Simin Sharifi, Seyedeh Solmaz Mohammadi, and Masoumeh Ghafourzadeh equally contributed to this study as the first authors. Sheida Bahrami, Melika Azaripour, Nasim Karimi, and Ali Ghaneh equally contributed to this study as second authors. Mohammad Rezaei and Seyedeh Zahra Shirdeli equally contributed to this study as supervisors and third authors.

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The authors declare no competing interests.

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RNA interaction and expression analysis of UHRF1 in breast cancer, gastric cancer, and colorectal cancer patients: systems biology investigation and experimental validation

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Abstract

Background

Method

Results

Conclusion

Figures

1. Introduction

2. Materials and methods

2.1. Microarray analysis

2.2. RNA interaction and enrichment analyses

2.3. Clinical features of human samples for qRT-PCR experiment

2.4. Statistical analyses

3. Results

3.1. Microarray analysis

3.2. Protein interaction and pathway analysis

3.3. Non-coding RNA interaction

3.4. Non-coding expression analysis

3.5. Validation of expression analysis by qRT-PCR

4. Discussion

5. Conclusion

6. Declarations

References

Additional Declarations

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