# CKS2 and S100A12: Two Novel Diagnostic Biomarkers for Rheumatoid Arthritis

Authors: Zhi-wen Wang; Li-jun Zhang; Yu Zhuang; Zhi-fen Lv; Zhi-ming Tan
Journal: Disease Markers
 (2022)

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## Abstract

Rheumatoid arthritis (RA) is a chronic systematicness autoimmunity disease with joint inflammation. RA etiology is still unknown. Early and exact diagnosing is still hard to reach. In the paper, we purposed to discover novel diagnosis biological marker for RA. Two open, usable gene expression profiles of human RA as well as controlled specimens (dataset GSE17755 as well as GSE93272) were downloaded from the GEO database. Differentially expressed genes (DEGs) were screened between 331 RA and 88 control samples. Functional enrichment analysis was applied to explore the possible function of DEGs. Expression levels as well as diagnosis values of biological marker in RA were further verified in our cohort by the use of RT-PCR and ROC assays. We identified 13 DEGs between RA samples and control samples. 13 DEGs were remarkably abundant in NF-kappa B signal pathway. Among the 13 DEGs, CKS2, S100A12, LY96, and ANXA3 exhibited a strong diagnostic ability in screening RA specimens from normal specimens using allAUC>0.8. Moreover, we confirmed that the expression of CKS2 and S100A12 was distinctly upregulated in RA specimens contrasted to normal specimens. Overall, serum CKS2 and S100A12 could be used as novel diagnosis biological markers for RA patients.


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## Body

## 1. Introduction

Rheumatoid arthritis (RA) is a chronic autoimmunity disorder characterized by continuous intra-articular synovitis as well as systematicness inflammation [1]. RA affects about 1% of global population, showing it one of the most epidemic autoimmunity disorders today [2]. Early diagnosis and novel medicine as well as treatment options can improve the prognosis of RA patients [3, 4]. Current treatments for RA contain ordinary as well as biological antirheumatic drugs to treat the disease [5, 6]. These medicines currently applied in single or merging have led to entire or partial clinic remissions in majority of RA patients, but challenges of resistance to certain RA drugs as well as their toxicity remain. Thus, elucidating the pathogenesis of RA and identifying novel prognostic and diagnostic biomarkers remain a priority.Many studies have reported that the dysregulation of some functional genes was involved in the progression of RA [7, 8]. For instance, the level of FURIN in peripheral blood from patients with RA was remarkably increased and was related to disorder activity [9]. FURIN inhibits THP-1-induced macrophages with increased IL-1β levels, suggesting that FURIN could have anti-inflammatory functions [9]. Information biology is a novel crossdiscipline subject integrating molecular biology as well as information technology [10, 11]. It is important to show the molecule mechanism of illness. As a new technique, gene chip has been applied for highly efficient as well as large-scale biological data acquisition, which can extensively gather illness expression profile information [12]. In the paper, we purposed to verify vital biological marker of abnormal expression gene among RA patients and offer diagnosis as well as target for drugs of OA.


## 2. Materials and Methods

### 2.1. Microarray Data

mRNA expression profile information of GSE17755 included blood specimens of 99 RA patients as well as 45 cases of healthy controls were from GEO (https://www.ncbi.nlm.nih.gov/geo/). GSE93272 included blood specimens of 232 RA patients as well as 43 cases of healthy controls. The mRNA levels of the GSE17755 datasets were quantified based on the Hitachisoft AceGene Human Oligo Chip 30 K 1 Chip Version, and the mRNA levels of the GSE93272 datasets were quantified based on the Affymetrix Human Genome U133 Plus 2.0 Array. The two datasets were combined in a metadata queue for further combination study. Moreover, using operation function of “SVA” package of R software eliminated batch influence.


### 2.2. Data Processing and DEG Screening

GSE17755 and GSE93272 datasets were combined in a metadata queue for further integration analysis. Moreover, using operation function of “SVA” package ofR software eliminated batch influence. Limma package of R was applied for background revising, normalizing among arrays, and differential expression analysis between 331 RA and 88 control samples. The false discovery rate of specimens adjusted to p<0.05 as well as logfoldchangeFC>0.6 used as a threshold point of DEGs.


### 2.3. Functional Enrichment Analysis

DEG-based Gene Ontology (GO) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses was carried out through “clusterProfiler”R package [13].


### 2.4. Clinical Specimens

Eight human RA knee gristle samples were gathered from the Huizhou Municipal Central Hospital between 2021.6 and 2022.1. All patients satisfied clinic as well as radiological diagnosis standard of RA. Eight regular knee gristle samples were acquired from sick persons with trauma-derived amputating. Moreover, eight blood samples from eight RA patients and eight healthy blood samples from healthy controls were obtained. Patients in the control group had no history of RA. Present research has received Ethics Committee’s approval of the Huizhou Municipal Central Hospital. All participators signed informed consents.


### 2.5. Quantitative RT-PCR

All primers and detection reagents for the detection of genes were from Qiagen (Pudong, Shanghai, China). RNAse-free water was used for the experiments. RT2 first strand kit (Qiagen, China) was used for the synthesis of cDNA. Quantity of RNA was 1μg, and 2 μl of genomic DNA elimination mix was added and mixed, followed by incubation for 5 min at 42°C and then quick transfer to ice-cold water for 1 min. Reverse transcription mix, consisting of 5× buffer and reverse transcriptase enzyme, was then added and incubated for 15 min at 42°C. 1 μg RNA and 2 μ L of genomic DNA eliminating mixture were mixed together, then incubating for five minutes under 42°C and rapidly transferred to cold water for one minute. Reverse transcript mixture, composed of 5× buffer and reverse transcript enzyme, which was then put as well as cultured for 15 minutes under 42°C. After incubating, reaction mixture tube was placed at 95°C to stop the reaction. Results were standardized with GAPDH. The primers were shown in Table 1.Table 1
The primer sequences included in this study.
Name
Primer sequences (5′–3′)
CKS2: forward
TTCGACGAACACTACGAGTACC
CKS2: reverse
GGACACCAAGTCTCCTCCAC
S100A12: forward
AGCATCTGGAGGGAATTGTCA
S100A12: reverse
GCAATGGCTACCAGGGATATGAA
LY96: forward
GAATCTGAGAAGCAACAGTGGT
LY96: reverse
CTCAACATGCACAAATCCATTGG
ANXA3: forward
TTAGCCCATCAGTGGATGCTG
ANXA3: reverse
CTGTGCATTTGACCTCTCAGT
GAPDH: forward
ACAACTTTGGTATCGTGGAAGG
GAPDH: reverse
GCCATCACGCCACAGTTTC


### 2.6. Statistical Analysis

R (Version 3.6.3, R Core Team, Massachusetts, USA) and GraphPad Prism 5.0 software (Graphpad, La Jolla, CA, USA) were applied for all statistical analysis. For continuous variables, Student’s t-test was used to group and compare normal distribution variables, and Mann–Whitney U test was used for abnormal distribution variables. ROC curve study could be applied to determine diagnostic efficacy of the diagnostic biomarkers included. Only the p values ≤0.05 were considered to represent statistically significant analyses.


## 2.1. Microarray Data

mRNA expression profile information of GSE17755 included blood specimens of 99 RA patients as well as 45 cases of healthy controls were from GEO (https://www.ncbi.nlm.nih.gov/geo/). GSE93272 included blood specimens of 232 RA patients as well as 43 cases of healthy controls. The mRNA levels of the GSE17755 datasets were quantified based on the Hitachisoft AceGene Human Oligo Chip 30 K 1 Chip Version, and the mRNA levels of the GSE93272 datasets were quantified based on the Affymetrix Human Genome U133 Plus 2.0 Array. The two datasets were combined in a metadata queue for further combination study. Moreover, using operation function of “SVA” package of R software eliminated batch influence.


## 2.2. Data Processing and DEG Screening

GSE17755 and GSE93272 datasets were combined in a metadata queue for further integration analysis. Moreover, using operation function of “SVA” package ofR software eliminated batch influence. Limma package of R was applied for background revising, normalizing among arrays, and differential expression analysis between 331 RA and 88 control samples. The false discovery rate of specimens adjusted to p<0.05 as well as logfoldchangeFC>0.6 used as a threshold point of DEGs.


## 2.3. Functional Enrichment Analysis

DEG-based Gene Ontology (GO) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses was carried out through “clusterProfiler”R package [13].


## 2.4. Clinical Specimens

Eight human RA knee gristle samples were gathered from the Huizhou Municipal Central Hospital between 2021.6 and 2022.1. All patients satisfied clinic as well as radiological diagnosis standard of RA. Eight regular knee gristle samples were acquired from sick persons with trauma-derived amputating. Moreover, eight blood samples from eight RA patients and eight healthy blood samples from healthy controls were obtained. Patients in the control group had no history of RA. Present research has received Ethics Committee’s approval of the Huizhou Municipal Central Hospital. All participators signed informed consents.


## 2.5. Quantitative RT-PCR

All primers and detection reagents for the detection of genes were from Qiagen (Pudong, Shanghai, China). RNAse-free water was used for the experiments. RT2 first strand kit (Qiagen, China) was used for the synthesis of cDNA. Quantity of RNA was 1μg, and 2 μl of genomic DNA elimination mix was added and mixed, followed by incubation for 5 min at 42°C and then quick transfer to ice-cold water for 1 min. Reverse transcription mix, consisting of 5× buffer and reverse transcriptase enzyme, was then added and incubated for 15 min at 42°C. 1 μg RNA and 2 μ L of genomic DNA eliminating mixture were mixed together, then incubating for five minutes under 42°C and rapidly transferred to cold water for one minute. Reverse transcript mixture, composed of 5× buffer and reverse transcript enzyme, which was then put as well as cultured for 15 minutes under 42°C. After incubating, reaction mixture tube was placed at 95°C to stop the reaction. Results were standardized with GAPDH. The primers were shown in Table 1.Table 1
The primer sequences included in this study.
Name
Primer sequences (5′–3′)
CKS2: forward
TTCGACGAACACTACGAGTACC
CKS2: reverse
GGACACCAAGTCTCCTCCAC
S100A12: forward
AGCATCTGGAGGGAATTGTCA
S100A12: reverse
GCAATGGCTACCAGGGATATGAA
LY96: forward
GAATCTGAGAAGCAACAGTGGT
LY96: reverse
CTCAACATGCACAAATCCATTGG
ANXA3: forward
TTAGCCCATCAGTGGATGCTG
ANXA3: reverse
CTGTGCATTTGACCTCTCAGT
GAPDH: forward
ACAACTTTGGTATCGTGGAAGG
GAPDH: reverse
GCCATCACGCCACAGTTTC


## 2.6. Statistical Analysis

R (Version 3.6.3, R Core Team, Massachusetts, USA) and GraphPad Prism 5.0 software (Graphpad, La Jolla, CA, USA) were applied for all statistical analysis. For continuous variables, Student’s t-test was used to group and compare normal distribution variables, and Mann–Whitney U test was used for abnormal distribution variables. ROC curve study could be applied to determine diagnostic efficacy of the diagnostic biomarkers included. Only the p values ≤0.05 were considered to represent statistically significant analyses.


## 3. Results

### 3.1. Identification of DEGs in RA Patients

Information from all 331 RA and 88 controlled specimens from two GEO datasets (GSE17755 as well as GSE93272) were reviewed to study in the paper. DEGs of metadata were studied with limma package after eliminating batch influences. All 13 DEGs were acquired: 12 gene were remarkably upregulated, and 1 gene was remarkably downregulated (Figures1(a) and 1(b)).Figure 1
Recognition of DEGs in RA. (a) Volcanic map and (b) Heat map showed differential expression genes between RA samples and control samples.
(a)
(b)


### 3.2. Functional and Pathway Enrichment Analyses of DEGs

The function features of DEGs were explored by enrichment analysis of GO and KEGG pathways. GO analysis showed that, in the aspects of BP, 13 DEGs were remarkable abundant in neutrophil threshing, neutrophil activation referred to immunity reaction, neutrophil modulatory immune, and neutrophil activating. Regarding CC, the 13 DEGs were enriched in specific granule, secretory granule lumen, cytoplasmic vesicle lumen, and vesicle lumen. Under MF, 13 DEGs were enriched in calcium-dependent protein binding, histone binding, H4 histone acetyltransferase activity, and RAGE receptor binding (Figure2(a)). Moreover, the KEGG study results showed the 13 DEGs could be significantly abundant in NF-kappa B signal pathway and transcriptional misregulation in cancer (Figure 2(b)). DO pathway abundance analysis revealed illness enriched by 13 DEGs that were primarily related to atopic dermatitis (Figure 2(c)).Figure 2
GO note and KEGG pathway enrichment study of DEGs. (a) Top 10 abundant GO terminologies. (b) KEGG pathways. (c) Disease ontology enrichment analysis.
(a)
(b)
(c)


### 3.3. The Diagnostic Value of 13 DEGs in RA

The expressing pattern of all 13 DEGs in RA and controlled specimens were shown in Figure3. We can observe that the expressions of CKS2, S100A12, LY96, ANXA3, BCL2A1, CSTA, HAT1, EVI2A, TNFAIP6, FI44, DEFA4, and IFI44L were distinctly increased in RA specimens contrast to normal specimens, while the expression of LRRN3 could be distinctly decreased in RA samples. Moreover, we performed ROC assays to determine the diagnostic value of the 13 DEGs. As shown in Figure 4(a), we observed that four genes including CKS2, S100A12, LY96, and ANXA3 exhibited a strong diagnostic ability in screening RA samples from normal samples with all AUC>0.8. The other 9 genes also showed a relatively high accuracy with AUC>0.65 (Figure 4(b)).Figure 3
Histogram showed the expressing pattern of 13 DEGs among RA specimens as well as ordinary specimens.∗∗∗p<0.001.Figure 4
Diagnostic value of 13 DEGs in screening RA samples from normal samples was determined by ROC analysis.
(a)
(b)


### 3.4. The Confirmation of the Expression of Four Genes in our Cohort by RT-PCR

Then, we applied RT-PCR to examine critical four gene expressions including CKS2, S100A12, LY96, and ANXA3 in our cohort. As shown in Figure5(a), we observed that the expression of CKS2 and S100A12 was distinctly increased in RA specimens contrast to normal specimens. However, the expression of LY96 and ANXA3 remained unchanged between RA specimens contrast to normal specimens (Figure 5(b)). Moreover, we also observed that the expression of CKS2 and S100A12 was distinctly upregulated in blood samples from RA patients compared with blood samples from healthy controls (Figure 5(c)). Also, there was no distinct difference in the expression of LY96 and ANXA3 between RA blood samples and healthy blood samples (Figure 5(d)). Our findings suggested CKS2 and S100A12 as important factors in RA.Figure 5
The identification of the expression of four critical genes in RA samples and normal samples from RA patients from our hospital. (a, b) RT-PCR was applied to examine the expression of CKS2, S100A12, and LY96 as well as ANXA3 in RA samples and normal samples. (c, d) RT-PCR was applied to examine the expression of CKS2, S100A12, and LY96 s well as ANXA3 in blood specimens from RA patients as well as blood specimens from healthy controls.∗∗p<0.01, ∗∗∗p<0.001. The experiments were repeated three times, and each experiment was triplicated.
(a)
(b)
(c)
(d)


## 3.1. Identification of DEGs in RA Patients

Information from all 331 RA and 88 controlled specimens from two GEO datasets (GSE17755 as well as GSE93272) were reviewed to study in the paper. DEGs of metadata were studied with limma package after eliminating batch influences. All 13 DEGs were acquired: 12 gene were remarkably upregulated, and 1 gene was remarkably downregulated (Figures1(a) and 1(b)).Figure 1
Recognition of DEGs in RA. (a) Volcanic map and (b) Heat map showed differential expression genes between RA samples and control samples.
(a)
(b)


## 3.2. Functional and Pathway Enrichment Analyses of DEGs

The function features of DEGs were explored by enrichment analysis of GO and KEGG pathways. GO analysis showed that, in the aspects of BP, 13 DEGs were remarkable abundant in neutrophil threshing, neutrophil activation referred to immunity reaction, neutrophil modulatory immune, and neutrophil activating. Regarding CC, the 13 DEGs were enriched in specific granule, secretory granule lumen, cytoplasmic vesicle lumen, and vesicle lumen. Under MF, 13 DEGs were enriched in calcium-dependent protein binding, histone binding, H4 histone acetyltransferase activity, and RAGE receptor binding (Figure2(a)). Moreover, the KEGG study results showed the 13 DEGs could be significantly abundant in NF-kappa B signal pathway and transcriptional misregulation in cancer (Figure 2(b)). DO pathway abundance analysis revealed illness enriched by 13 DEGs that were primarily related to atopic dermatitis (Figure 2(c)).Figure 2
GO note and KEGG pathway enrichment study of DEGs. (a) Top 10 abundant GO terminologies. (b) KEGG pathways. (c) Disease ontology enrichment analysis.
(a)
(b)
(c)


## 3.3. The Diagnostic Value of 13 DEGs in RA

The expressing pattern of all 13 DEGs in RA and controlled specimens were shown in Figure3. We can observe that the expressions of CKS2, S100A12, LY96, ANXA3, BCL2A1, CSTA, HAT1, EVI2A, TNFAIP6, FI44, DEFA4, and IFI44L were distinctly increased in RA specimens contrast to normal specimens, while the expression of LRRN3 could be distinctly decreased in RA samples. Moreover, we performed ROC assays to determine the diagnostic value of the 13 DEGs. As shown in Figure 4(a), we observed that four genes including CKS2, S100A12, LY96, and ANXA3 exhibited a strong diagnostic ability in screening RA samples from normal samples with all AUC>0.8. The other 9 genes also showed a relatively high accuracy with AUC>0.65 (Figure 4(b)).Figure 3
Histogram showed the expressing pattern of 13 DEGs among RA specimens as well as ordinary specimens.∗∗∗p<0.001.Figure 4
Diagnostic value of 13 DEGs in screening RA samples from normal samples was determined by ROC analysis.
(a)
(b)


## 3.4. The Confirmation of the Expression of Four Genes in our Cohort by RT-PCR

Then, we applied RT-PCR to examine critical four gene expressions including CKS2, S100A12, LY96, and ANXA3 in our cohort. As shown in Figure5(a), we observed that the expression of CKS2 and S100A12 was distinctly increased in RA specimens contrast to normal specimens. However, the expression of LY96 and ANXA3 remained unchanged between RA specimens contrast to normal specimens (Figure 5(b)). Moreover, we also observed that the expression of CKS2 and S100A12 was distinctly upregulated in blood samples from RA patients compared with blood samples from healthy controls (Figure 5(c)). Also, there was no distinct difference in the expression of LY96 and ANXA3 between RA blood samples and healthy blood samples (Figure 5(d)). Our findings suggested CKS2 and S100A12 as important factors in RA.Figure 5
The identification of the expression of four critical genes in RA samples and normal samples from RA patients from our hospital. (a, b) RT-PCR was applied to examine the expression of CKS2, S100A12, and LY96 as well as ANXA3 in RA samples and normal samples. (c, d) RT-PCR was applied to examine the expression of CKS2, S100A12, and LY96 s well as ANXA3 in blood specimens from RA patients as well as blood specimens from healthy controls.∗∗p<0.01, ∗∗∗p<0.001. The experiments were repeated three times, and each experiment was triplicated.
(a)
(b)
(c)
(d)


## 4. Discussion

Over the past thirty years, many scientists have abroad researched changes in prevailing and occurrence rate of RA [14]. The researches show that RA is a universal illness, in spite of race, gender, ethnicity, national origin, age and so on [15, 16], but measurements of prevailing as well as occurrence rate transform through demographic characteristics and change over time. Diagnosis of early stage and therapy of RA can avoid or significantly slow the development of joint injury increasing to 90% among patients, preventing nonreversible disability [17, 18]. The developments of new tools to examine disease activity and identify the presence or absence of remission have promoted novel treatment strategies to arrest RA before joints are damaged irreversibly. In this study, we aimed to screened novel biomarkers based on GEO datasets and further confirmed the findings using our cohort.According to our findings, we identified 13 DEGs in RA. Importantly, the DEGs were confirmed in blood samples from RA patients. A biological marker is a patient feature evaluated as a sign for ordinary or pathologic procedure or bioresponse to therapy [19, 20]. It has been known to us that non- or minimally invasive biological markers can be very important for the diagnosis of various diseases. They have a lower cost and less trauma [21, 22]. In addition, some sensitive serum biomarkers can be used for the development of real-time monitoring system. In this study, we discovered 13 DEGs were remarkably abundant in neutrophil threshing, neutrophil activation referred to immunity reaction, neutrophil modulatory immune, and neutrophil activating. In addition, the KEGG study results indicated that the 13 DEGs were remarkably abundant in NF-kappa B signal pathway which has been confirmed to be involved in the transcriptional control of inflammation, highlighting that 13 DEGs may play an important role in RA progression via controlling inflammation [23, 24]. Importantly, ROC assays confirmed 13 DEGs as robust diagnostic biomarkers for RA. The 13 DEGs may acted as not only regulatory factors but also novel diagnostic biomarkers for RA.Among 13 DEGs, our attention focused on CKS2, S100A12, LY96, and ANXA3, which exhibited a strong diagnostic ability in screening RA samples from normal samples with allAUC>0.8. Then, we performed RT-PCRRA specimens and normal specimens, finding that the expressions of CKS2 and S100A12 were increased in RA samples. In blood samples, similar findings were also observed. Cyclin-dependent kinase regulatory subunits 1 (CKS1) and CKS2 are part of the family of small (9 kDa) CDK binding proteins that are highly conservative and exert a vital part in regulating cell circulation. Former researches have suggested that CKS2 could exert a vital part in somatic cell dividing as well as early embryonic progression [25, 26]. However, the expression and function of CKS2 in RA have not been investigated. S100A12 (calgranulin C) is part of the S100 family of calcium binding proteins [27]. 20 members of the part share EF-hand domain referred to calcium integration. To date, S100A12 inflammatory data have only been reported in the mice system [28]. In addition, several studies have reported that S100A12 may be involved in the progression of RA [29, 30]. However, the specific function and potential mechanisms remained largely unclear. Our findings, together with previous findings, suggested that S100A12 and CKS2 may be novel functional regulators in RA progression.


## 5. Conclusion

We identified 13 diagnostic biomarkers for RA. Importantly, we further confirmed that the expression of S100A12 and CKS2 was distinctly increased in RA patients. They may be novel regulatory factors in the development and progression of RA, and could be used as novel diagnostic biomarkers for RA.



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*Source: 2431976-2022-06-25.xml*