PGGHG and ODF3B as New Tumor Suppressor Genes in Renal Cell Carcinoma

Background : Renal cell carcinoma (RCC) is the most frequent heterogeneous type of kidney cancer. Although the role of many genes in the development of RCC has been shown, its specific molecular mechanism is still unknown. Objective : In this research, we studied the expression profile of two uncharacterized genes,


Introduction
Renal cell carcinoma (RCC) is the most common type of kidney malignancies and highly aggressive heterogeneous disease [1,2].The most common histological subtypes of RCC are clear cell, papillary, and chromophobe responsible for 70%, 10-15%, and 5% of the RCC types, respectively [3,4].So far, many factors have been implicated in the development of ccRCC, including genetic and environmental factors.Moreover, dysregulation of several genes has been contributed to the RCC progression and metastasis in different pathways.Clear cell RCC patients respond poorly to radiotherapy and chemotherapy.Therefore, the molecular predictions of disease progression and metastasis are important for therapies.We are looking to explore new molecular biomarkers of disease progression and ccRCCspecific molecular mechanisms that may provide new targeted treatment options.
TCGA data from GDC Data Portal (https://portal.gdc.cancer.gov)contains a huge genomic, epigenomic, transcriptomic, and proteomic data of numerous cancer types which can help scientists to use its data for investigation of uncharacterized genes in cancers.Using analysis of TCGA renal cancers data, we found altered expression of two uncharacterized genes, including proteinglucosylgalactosylhydroxylysine glucosidase (PGGHG) (also known as ATHL1) and outer the dense fiber of sperm tails 3B (ODF3B) (Also known as FAP123; ODF3L3) in KIRC.Their altered expression was correlated with worse prognosis with reduced survival in the KIRC patients.Moreover, using STRING database we found their involvement in the same network.Therefore, our study set out to investigate the potential contribution of these two genes with unknown roles, in RCC pathogenesis.
The chromosomal positions of ODF3B is 22q13.33.The pronephros is consisted of two types of epithelial cells, including transportive and multiciliated cells (MCCs).The epithelial MCCs expresses ODF3B protein [5].Previous study showed that ODF3B transcripts localize in pronephros cells and labels maturing MCCs in renal progenitors [6].
In relation to the PGGHG (located on 11p15.5), it was found to act in release of glucose from human placental type IV collagen [7].Data extracted from Enrichr (https:// maayanlab.cloud/Enrichr/)shows its involvement in hydrolase activity, hydrolyzing O-glycosyl compounds (GO: 0004553).Moreover, previous study conducted by Wei Shi et al. showed higher expression of hsa-mir-484 correlated with worse prognosis in breast cancer.In their study, they found that one of target gene of hsa-mir-484 was PGGHG [8].Based on these evidence, identification of the role of these two genes in renal cancer may help find new pathways in this cancer.

Gene selection
Using UALCAN webserver (http://ualcan.path.uab.edu.), firstly, we looked for significant upregulated genes in TCGA-KIRC.Next, we looked for genes with poor overall survival (OS) time with worse prognosis among them, and then we only considered two uncharacterized genes in KIRC including ODF3B and PGGHG.Moreover, we investigated the expression of these genes in other two types of renal cancer including Kidney renal papillary cell carcinoma (KIRP) and Kidney chromophobe (KICH).In addition to these data, to strengthen their role in renal cancers, we looked for their protein network using STRING (https://string-db.org) to find whether they are connected in same protein network and also to help identify their possible pathways.

Patient characteristics and tumor samples
To investigate mRNA expression of PGGHG and ODF3B in renal cancers, we collected 40 tumor tissues and their adjacent normal tissues of RCC patients from Ali-Asghar, Namazi, and Ghadir Mother and Child Hospitals (Shiraz, Iran).In total, RCC patients had not received radiotherapy and chemotherapy before surgery.The samples were immediately immersed in liquid nitrogen and stored at -80°C until use.The clinicopathologic and demographic features of RCC patients are shown in Table 1.After surgery, we followed up patients to find they had all-cause mortality.

RNA extraction and cDNA synthesis
Using the Trizol isolation reagent (Invitrogen, Thermo Fisher) total RNA was extracted from each sample according to the manufacturer's instructions.Gel electrophoresis and a nanodrop spectrophotometer (BioTek, HTX multimode reader) was used to assess RNA quality and quantity, respectively.Total RNA was reverse-transcribed into firststrand cDNA using the PrimeScript™ RT Reagent Kit (Takara, Cat.No: RR037A).

qRT-PCR
QRT-PCR was carried out using Power SYBR® Green PCR Master Mix (ABI, USA) on the 7500 real-time PCR system (ABI, life technology).7.5 µl BioFACT™ master mix including SYBR Green (Ampliqon, Cat.No: A325402-25), along with 1 µl of cDNA, 0.75 µl of each primer and 5 µl DNase-free dH 2 O was used for total volume of 15 µl reaction mix.B2M was used as an internal control.The sequences for primers are shown in Supplementary Table 1.Relative quantification was calculated by the 2 -∆∆CT .

Enrichment analysis
In our study we also used Enrichr database (https:// maayanlab.cloud/Enrichr/)to look for their pathways, ontologies, and possible upstream transcription factors and among others.To evaluate survival analysis our patients, we used the Kaplan-Meier method based on observed survival times.This analysis was performed using IBM SPSS version 26 software (IBM Corp, Armonk, NY).

TCGA data analysis
Using TCGA data we extracted 75 top upregulated genes in KIRC shown as heatmap in Figure 1A (extracted from UALCAN webserver (http://ualcan.path.uab.edu.), a heatmap containing some of these genes with similar patterns have been reported by Ping Wu et al. [9].Then among theme, we selected only two uncharacterized genes with worse prognosis with reduced survival in KIRC (Figure 1A-C).Moreover, we investigated their expression in other two renal cancer types KIRP and KICH, which both genes showed lower-expression in KICH but over-expression in KIRP similar to the KIRC (Figure 1D and 1E).We also found that these two proteins can have indirect connections in the same protein network (Figure 2, using STRING), suggesting their possible roles in renal pathways and cancers.In addition to these data, its protein network revealed its interaction with several important proteins involved in biological pathways and cancers (including renal cancers), for instance, COL10A1 [10] (Figure 2).Based on these primary data we conducted our experimental study to identify the expression of these two genes in our patients with renal cancers.

Downregulation of PGGHG and ODF3B gene expression in RCC
Using qRT-PCR, the expression levels of mRNAs were assessed in 40 tumor samples and their tumor's adjacent normal tissues.As shown in the figure 3A, the expression level of ODF3B was significantly lower in tumors tissues (median=0.625) in comparison to the tumor's adjacent normal tissues (median=0.977)(P value=0.042).Also, the expression of PGGHG had significantly lower in tumor tissues (median=0.378)compared to their adjacent normal tissues (median=1.120)(Pvalue=0.034,Figure 3B).

Association between PGGHG and ODF3B gene expression and clinicopathological and demographic features of RCC
Next, we investigated the relationship between PGGHG and ODF3B expression levels and the clinicopathologic status of patients with renal cell carcinoma.Our results demonstrated that ODF3B had lower expression in RCC patients who had tumor size>4cm (P value=0.060, Figure 3C).When we divided fold changes into two high and low expressions groups, chi-square test proved the significant relation between tumor size and ODF3B expression (P value= 0.011, Table 2).Comparison of PGGHG expression level and tumor size did not show a significant difference (P value=0.744).
Since ccRCC is the most common of RCC and oncocytoma is the least common type in our study community (Table 1), our analysis showed that PGGHG expression level was higher in oncocytoma type in comparison to other type of RCC (P value=0.017, Figure 3D).Similarly, the relationship between the ODF3B expression level and the tumor type showed significant.The expression level of ODF3B was higher in oncocytoma type (P value=0.004, Figure 3E).

Functional enrichment analysis
Using Enrichr, we found down-regulation of ODF3B upon RUNX1 Knock-out mouse, PPARG deficiency mouse, SETDB1 Knock-down in THP1 human cells, IRF9 Knock-down in human cells, NANOG over-expressed mouse, and CREM Knock-out mouse (Supplementary Table 2).These data can suggest the possible upstream transcription factors of ODF3B which regulate its expression in biological pathways.Among GO ontologies using Enrichr, ODF3B was found to be involved in cytoskeleton (GO: 0005856, p-value: 0.03000) in

terms of cellular component (CC).
In relation to the PGGHG, Enrichr revealed its downregulation upon ZNF503 shRNA in H1 human cells, NFYC Knock-out in mouse, BCL6 Knock-down in human cells, POU2AF1 over-expression in mouse, EPAS1 Knock-down in HUVEC human cells, NFYA Knock-out in mouse, FOXP3 activation in human cells, PPARG over-expression in mouse, and GATA4 over-expression in A549 human cells (Supplementary Table 3).The interesting result was that while PPARG over-expression in mouse resulted in PGGHG down-regulation, PPARG deficiency mouse showed ODF3B down-regulation.These data may indicate PPARG as the same upstream transcription factor affecting ODF3B and PGGHG.Among GO using Enrichr, PGGHG showed involvement in hydrolase activity, hydrolyzing O-glycosyl compounds (GO: 0004553, p-value: 0.001850, in terms of molecular function (MF).
As shown in Figure 2, protein networks of PGGHG and ODF3B are connected and correlated.Therefore, we investigated the GO terms for all proteins involved in these networks to shed light on the identification of possible pathways of PGGHG and ODF3B.

Overall survival
To evaluate survival analysis, we used the Kaplan-Meier method based on observed survival times.However, we were not able to predict the prognosis of renal cell carcinoma patients based on PGGHG and ODF3B gene expression using this data (Supplementary figure 1).The small proportion of participants evaluated, low duration of follow up and undetected exact cause of death could be some of the reasons.

Discussion
Renal cell carcinoma (RCC) is the most common form of kidney cancer and accounts for approximately 3.7% of the total cancer deaths [11].Identification of biomarkers helps in improving the prognosis and early detection of RCC.Despite astonishing molecular advances and their significant impact on the detection of functional mRNAs in malignancies, numerous questions remain regarding molecular interactions and their function and need further attention.
In this study, we compared the PGGHG and ODF3B expression levels in tumor and their adjacent normal tissues in RCC patients and their association with demographical and clinicopathologic factors.
Our study with the use of QPCR as gold standard for relative expression analysis revealed the downregulated level of PGGHG and ODF3B in the tumor tissues compared to their adjacent normal tissues which were in contract to TCGA-KIRC.The reverse data for both genes in our experiments can strengthen the involvement of both proteinss in the same network.There are some reasons which might show these reverse results as follow: 1-The TCGA data is the raw data of RNA sequencing which usually are validated by gold standard approaches such as QPCR.Since in some genes the coverage is not optimal and may affect the correct overall gene expression.2-Another factor which might affect the results is the population diversity since the TCGA data only has 8 KIRC samples from Asian region and we don't know how many samples are from Iranian population.3-Other environmental factors and life styles in different area might change the expression of these genes.However, since our study showed lower expression ISSN: 2581-7388 of these genes in all of our patients which was in contrast to TCGA data, more researches from different population with higher number of patients are needed to find whether different population, statuses, and other factors are involved in this reverse result, helping determination of the precise mechanism of their involvement in RCC pathogenesis.
Our results indicate that the expression level of ODF3B is significantly lower in tumor tissues than their normal adjacent ones.There are few investigations about the role of the studied genes in the development and progression of cancers.In the case of the ODF3B gene, an experimental study related to cancer has not been reported.Jihye Ryu et al. examine promotor activity of ODF3B in multiple sclerosis (MS) on large scale by genome-wide association studies (GWAS) signals.In this study, an association of promotor variants with the expression of this gene was reported [12].In another study, Honglin Zhu et al. showed the ODF3B as potential methylation-regulated differentially expressed gene [13].This gene delineates the abnormal activation of immune regulation in the pathogenesis of Systemic sclerosis (SSc).
Our study also showed significant lower expression of PGGHG in RCC tissues in comparison to their adjacent normal specimens.In the past decades, the role of aerobic glycolysis in cancer cells has been discussed, however, data supporting for glycogen biology in RCC are lacking [14].In line with our results, a study demonstrated that reduced AGL, a glycogen debranching enzyme, increased tumor growth in patients with bladder cancer by RNA interference screen [15].In another study, AGL loss led to the progression of bladder cancer.Details of this metabolomics pathway showed increased glucose metabolism with the help of serine hydroxymethyltransferase in cells [16].
Our data from Enrichr revealed significant KEGG, MF, BP, and CC for proteins involved in ODF3B and PGGHG networks, suggesting the possible roles of these two proteins, directly or indirectly, in these GO terms and KEGG pathways.Moreover, using Enrichr, we found the downregulation of ODF3B and PGGHG upon perturbations of some transcription factors, mainly PPARG which affected both genes.PPARG, Peroxisome Proliferator Activated Receptor Gamma, is involved in adipocyte differentiation considered a "master regulator" of adipogenesis.Moreover, it represses inflammatory response genes in mouse macrophages [17,18].We also identified the positive relationship of tumor size with the ODF3B expression level (Table 2).According to a study by Zhi et al., an increase in tumor size has been reported in the progression and high risk of lymph node metastases (LNM) in ccRCC [19].

Conclusion
In summary, we found lower expression of ODF3B and PGGHG in renal cancers and proposed their possible biological pathways and upstream transcription factors.Our results propose investigation of functional studies for ODF3B and PGGHG genes to identify their exact roles in biological pathways and cancers.While our study with the use of QPCR showed the lower-expression of PGGHG and ODF3B in our patients, they were in contract to TCGA-KIRC and some above-mentioned reasons might answer this reverse result.However, our study showed lower expression of these genes in all of our patients and further studies from different population with higher number of patients are needed to investigate the expression of these genes in renal cancers to uncover the exact role of them in these cancers.

Figure 1 :
Figure 1: A. Seventy-five top upregulated genes in TCGA-KIRC from UALCAN webserver.B. Expression of ODF3B in KIRC and effect of its altered expression on OS in TCGA-KIRC.C. Expression of PGGHG in KIRC and effect of its altered expression on OS in TCGA-KIRC.D. Expression of ODF3B in KICH and KIRP.E. Expression of PGGHG in KICH and KIRP.

Figure 2 .
Figure 2. Protein network of PGGHG and ODF3B.It shows connection between protein networks of PGGHG and ODF3B, representing their possible roles in the same pathways and cancer pathogenesis.

Figure 3 :
Figure 3: A and B. The boxplot of comparison of PGGHG and ODF3B expression between tumor samples and their paired adjacent normal tissues, respectively.C. The association between the expression of ODF3B and tumor size D. PGGHG expression levels in the different subgroups of tumor types in RCC.E. ODF3B expression levels in the different subgroups of tumor types in RCC.

Figure 4 :
Figure 4: Significant KEGG (Kyoto Encyclopedia of Genes and Genomes) human pathways, BP (biological process), MF (molecular function), and CC (cellular component) GO terms for proteins involved in PGGHG and ODF3B networks.

Table 1 .
Statistical analyses were done in IBM SPSS version 26 software (IBM Corp, Armonk, NY).The data were presented as mean and standard deviation for ΔΔCt, or median for fold change.Wilcoxon test was used to compare fold changes between tumors and adjacent normal renal The association of ODF3B and PGGHG expression levels with demographic and clinicopathological factors in RCC patients.

Table 2 .
The association of ODF3B and PGGHG expression levels with demographic and clinicopathological factors in RCC patients, according to categorizing patients in to two groups of high and low expressions.tissues.The relation of PGGHG and ODF3B expression with demographic and clinicopathological features was assessed by Mann-Whitney and Kruskal-Wallis tests.In the next step, fold changes were divided into 2 groups of high and low expressions conforming to median for each gene, and the comparison among these groups were analyzed by chi-square test and independent t test.After surgery, we followed up patients until all-cause death used and censured alive one.The log rank test is a statistical hypothesis test that may be used to compare two high and low gene expression survival curves.