Ornithine decarboxylase antizyme 2 (OAZ2) in human colon adenocarcinoma: a potent prognostic factor associated with immunity

doi:10.21203/rs.3.rs-1500331/v1

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Ornithine decarboxylase antizyme 2 (OAZ2) in human colon adenocarcinoma: a potent prognostic factor associated with immunity

https://doi.org/10.21203/rs.3.rs-1500331/v1

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Backgrounds

Till now, few researches have been focused on the role OAZ2 gene played in colorectal cancer about prognostic and immunologic. In this article, we are aiming to explore more specific role and mechanism of OAZ2 in colon adenocarcinoma (COAD).

Methods

The expression level of OAZ2 and related clinical data were all downloaded from online database The Cancer Genome Atlas (TCGA). Uni- and multivariate Cox hazard regression analyses were performed to identify potent prognostic indicators. Gene set enrichment analysis (GSEA) and nomogram model was utilized to find interrelated signaling pathways and evaluate prognostic value separately. Associations between OAZ2 and immunity were also figured out in this research.

Results

OAZ2 gene has been detected low expression levels between normal and tumor tissues across multitudinous tumors. Box-plot analysis revealed that OAZ2 had low expression level in COAD patient-tissues (P < 0.05). Uni- and multivariate Cox regression analysis were utilized to conform that OAZ2 could work as a prognostic indicator independently (P < 0.05). GSEA was applied to find two OAZ2-related signaling pathways including cardiac muscle contraction and oxidative phosphorylation pathways. Besides, a model of nomogram was set up successfully by us for prediction of survival rates of COAD patients. Finally, through analyzation about PPI and immunity, we found that 10 OAZ2-related genes and OAZ2 expression level was closely connected with immunity such as tumor mutational burden (TMB), microsatellite instability (MSI) as well as tumor immune infiltration, tumor microenvironment, immune check-point molecules and immune cells pathway.

Conclusions

OAZ2 was proved to be a useful indicator in COAD patients for prognosis closely-related to OS. We found OAZ2 had strong connections with immunity. Further study about the role of OAZ2 in prognosis in vivo and in vitro need to be carried out in our follow-up research.

OAZ2

COAD

Prognosis

Immunity

Overall survival

Colorectal cancer (CRC) known as one of the most prevalent malignant digestive tumors has the third highest incidence rate and the second highest mortality rate worldwide[1]. There were approximately 149500 new-diagnosed cases and 52980 death tolls States during 2021 in the US[2]. Incidence rate of CRC among people aged younger than 50 years increased by about 2% annually while the death rate of CRC also increased by approximately 1.3% per year in the individual aged less than 50-years old[3]. Colon adenocarcinoma (COAD) was summarized in previous studies as the most universal sub-type of colorectal malignant tumor of which the 5-year survival rate is dropping annually[4]. Though more and more clinical treatments such as surgery, chemotherapy and radiotherapy are getting mature and are available for people who diagnosed with COAD, poor prognosis remains to be a cruel and realistic problem[5]. Therefore, to find related molecular mechanisms of COAD becomes an emergency and research related novel biomarkers also needs to be first aid[6].

Polyamines as products of a rate-limiting reaction catalyzed by an enzyme called ornithine decarboxylase (ODC) are important for cellular growth[7]. Depletion of polyamines will lead to cytolysis[8] and ascendant levels of polyamines are tightly linked with multiple cancers[9]. Ornithine decarboxylase antizyme 2 (OAZ2), also known as antizyme 2, belongs to antizyme family in which includes 3 definite members antizyme 1–3 and one putative member antizyme 4[10]. OAZ2 is one of the predominant antizyme proteins, inhibiting both ODC activity and polyamine uptake while promoting ODC degradation in vivo at the same time and it seems could serve as a tumor suppressor[7]. With the rapid development of high throughput sequencing technology and the growing well-built networks of public database, increasing number of genes which could serve as potential prognostic biomarkers can be excavated from public resource center such as The Cancer Genome Atlas (TCGA)[11]. Geerts D et al. suggested that OAZ2 can serve as a predictor importantly correlated with the prognosis of neuroblastoma tumors[12]. Li Y et al. indicated that overexpression of OAZ2 can rescue the decreased chemosensitivity induced by the deficiency of miR-34a[13]. Cho LY et al. selected OAZ2 rs1800566 as an important single nucleotide polymorphisms (SNPs) and was found dramatically decreased in gastric cancer[14]. Until today, few published researches have reported the role OAZ2 gene played in COAD. Then in our study, we are aiming to explore more specific role and mechanism of OAZ2 in colon adenocarcinoma, trying to discover a brand-new potential biomarker for the improvement of the survival and prognosis rates of COAD. All statistical tests mentioned in this study were bilateral tests with P < 0.05 considered statistically significant.

Data acquisition

The RNA-seq FPKM data of OAZ2 and related clinical information in 39 normal samples as well as 398 COAD samples were acquired from the public database The Cancer Genome Atlas (TCGA) Data portal (https://portal.gdc.cancer.gov/). Log2 transformation and R language (https://www.r-project.org/) version 4.0.3 were applied to standardize and analyze all the data[15]. Further we completed an overlap with OAZ2 mRNA by taking advantage of R programming language and we got useful expression matrix of target gene with related clinical information. Currently, overall survival (OS) is usually considered as primary outcome in studies. In this study, we also use OS to assess prognosis and tools such as ‘limma’ package for calculation of differentially expressed genes (DEGs) with cut-off standard such as |log2 FC (fold change) | ≥1 and adjusted P-value (FDR) < 0.05[16].

Gene set enrichment analysis

The online tool Gene Set Enrichment Analysis (GSEA) as an emerging means was frequently performed to explore whether the marker gene expression data could serve as predictors to figure out significant statistically differences between two distinct groups divided according to the expression of OAZ2 in COAD samples[17]. With the help of the Molecular Signatures Database (MSigDB) tool, we applied GSEA for the analysis of important differential survival between two groups which divided according to the high and low expression level of OAZ2[18]. Analysis each time contains 1000-time permutation tests at least and by this mean we could find critical significant biological pathway, with the normalized P value < 0.05 and the normalized enrichment score (NES)’s threshold > 1.5.

The Kaplan Meier survival analysis and the receiver operating characteristic analysis

398 COAD patients enrolled in this study were differentiated into high and low risk groups on the basis of OAZ2 median-expression as cut-off value. We analyzed the survival difference of these two groups by the method of Kaplan–Meier (K–M) survival analysis and did a comparison through log-rank testing. The model of receiver operating characteristic (ROC) was developed by us through the medium of R language “survival ROC” package and quantity of area under the curve (AUC) was calculated at the same time for the assessment of the sensitivity and specificity of OAZ2.

Uni- and Multivariate Cox hazard regression analysis

In order to identify whether OAZ2 and other seven clinicopathological parameters such as age, gender, race, stage, T, M and N all could serve as prognostic indicators independently in patients with colorectal malignant tumors, uni- and multivariate Cox hazard regression analyses were performed in this article under the usage of R language package.

The establishment of a nomogram for prognosis prediction

We established a nomogram model to predict survival probabilities of COAD patients in 1-, 3-, and 5-year respectively and we got visualized relationship between eight individual predictors as age, gender, race, stage, T, M, N, OAZ2 and related overall survival rates via utilizing R package “rms”. The score scale was applied into the nomogram and every parameter was distributed with one point. We finally calculated total score with the points of all predictors were added up.

Usage of Sangerbox online website tool

With the employment of online tool Sangerbox, we were capable to exploring the associations between the expression level of OAZ2 and immunity via pearson’s methods such as tumor mutational burden (TMB), microsatellite instability (MSI), neoantigen as well as tumor immune infiltration, tumor microenvironment, immune check-point molecules, immune cells pathway[11].

PPI network and HPA database

Protein-protein interaction (PPI) analyzation was conducted with the assistance of online website STRING for searching potential genes related with OAZ2. And online database The Human Protein Atlas (HPA) was utilized to verify the protein-expression of OAZ2 in COAD with technology of immunohistochemical staining by antibody HPA047694.

Statistical analysis

We conducted all statistical analyses by means of R 4.0.3 software program (https://www.r-project.org/) and compared the differences of OAZ2 expression between the normal tissues and tumor tissues by means of R ’limma’ package (http://www.bioconductor.org/packages/release/bioc/html/limma.html) via using Student’s t test[19]. In order to appraise the connection between the expression level of OAZ2 and clinicopathological variables, we performed Wilcoxon signed-rank test and logistic regression as well[20]. We divided COAD patients into high- and low- risk groups on the basis of ‘OAZ2’s expression grade. Through the method of Kaplan-Meier curve[21], we implemented works of survival analysis and finished the calculation of ROC-curves as well as the values of area under the curves (AUC) with foundation of running R ’survivialROC’ package. For the sake of finding independent prognostic targets, the univariate and multivariate cox hazard regression analyses were carried out by us at the same time. Finally, we built a nomogram model with the point-scale calculation for visualizing the survival rates in 1-, 3-, and 5-year respectively with the usage of R ’rms’ package.

Expression levels of OAZ2 in COAD from TCGA and HPA database

The mRNA expression levels of OAZ2 across multitudinous tumors were investigated for the identification of the differential expression patterns between normal tissues and COAD tumor tissues from TCGA database (Fig. 1A). OAZ2 expression level is lower in tumor tissues compared with normal tissues as shown in Boxplot (N = 39; T = 398) with P < 0.05 (Fig. 1B). Further, the patients with COAD were classified into risk-high and -low groups in accordance with median expression level of OAZ2. However, Kaplan-Meier survival analysis (K-M) supported that OAZ2-low level group had higher overall survival rates than OAZ2-high level group (P = 0.02; Fig. 1C) and AUCs of ROC curves of OAZ2 related 1-, 3- and 5-year exhibited separately 0.640, 0.631and 0.690 (Fig. 1D). As exhibited in (Fig. 1E, F), the staining result of immunohistochemical downloaded from HPA database demonstrated that OAZ2 moderately expressed in normal colon tissues and could not be detected in COAD patients’ tissues.

Association between OAZ2 expression and clinicopathologic parameters

Aiming to discover the relationship between OAZ2 mRNA expression level and other seven clinicopathological parameters like age, race, gender, stage and T, N, M staging, we took advantage of logistic regression and the Wilcoxon signed-rank test in this study. However, we didn’t find any obvious connection among these factors with all P > 0.05 (Fig. 2A-G).

OAZ2 could serve as an independent prognostic factor

Uni- and multivariate Cox regression analysis were by us utilized to filter factors independently for prognosis closely related to OS from these eight parameters (Table.1). As shown in Fig. 3A, parameters for instance stage, gender, T, M, N, OAZ2 expression were all tightly related to the overall survival rate in the first univariate cox regression analysis (all P < 0.05; Fig. 3A and Table.1) while in the multivariate Cox regression analysis, only stage, gender, M, OAZ2 expression were found associated with OS (all P < 0.05; Fig. 3B and Table.1). In summary, stage, gender, M and especially OAZ2 expression might supposed to be regarded as in our article independent prognostic factors for COAD patients.

Establishment of COAD prognostic prediction nomogram

We established a nomogram model on the basis of clinicopathological parameters such as age, gender, race, stage, T, M, N and OAZ2 for predicting survival rates of OS in 1-, 3-, 5-year and finally the C-index of this nomogram was 0.784 (Fig. 4A; Table.2). The calibration curves of survival probabilities in 1-, 3-, 5-year verified the consistency between our forecast and observation (Fig. 4B-D). Moreover, the AUCs of survival curves in 1-, 3-, 5-year were 0.790, 0.801, 0.755 respectively, representing a medium level prediction accuracy and which manifest the effectivity of this prognostic nomogram (Fig. 4E-G).

GSEA identified OAZ2related signaling pathways

In order to figure out how OAZ2 was involved in the COAD pathogenesis, the GSEA by us was applied between OAZ2-high and OAZ2-low expression groups for the acquisition of useful signaling pathways. We found that there existed two signaling pathways including cardiac muscle contraction and oxidative phosphorylation were significantly associated with high OAZ2 expression with the threshold of normalized P value < 0.05, NES > 1.5 (Fig. 5A-B; Table 3).

Table 1

Uni- and multivariate cox regression analyses of COAD from TCGA database;
	Univariate analysis				Multivariate analysis
Characteristics	HR	HR.95 L	HR.95 H	pvalue	HR	HR.95 L	HR.95 H	pvalue
Age	1.019482	0.994269	1.045334	0.131014	1.022369	0.995434	1.050033	0.104368
Gender	2.233110	1.180471	4.224399	0.013509	2.215196	1.126256	4.356997	0.021196
Race	0.914905	0.502185	1.666818	0.771367	0.995853	0.536323	1.849118	0.989500
Stage	1.993568	1.397495	2.843882	0.000141	1.897585	1.073788	3.353388	0.027452
T	2.713683	1.444052	5.099591	0.001925	1.697640	0.780158	3.694102	0.182159
M	1.628803	1.140460	2.326253	0.007303	1.544719	1.013599	2.354143	0.043094
N	1.663132	1.167274	2.369631	0.004859	0.905736	0.521924	1.571797	0.724813
OAZ2	1.285123	1.101919	1.498786	0.001390	1.336142	1.133840	1.574539	0.000541

Table 2

C-index and 1-, 3-, 5-year AUCs of nomogram based on OAZ2;
	1-year	3-year	5-year	C-index
AUC	0.790	0.801	0.755	0.784

Table 3

Gene set enrichment analysis based on OAZ2 in TCGA;
Gene set name	NES	NOM p-val	FDR q-val
KEGG_CARDIAC_MUSCLE_CONTRACTION	-1.534	0.046	0.256
KEGG_OXIDATIVE_PHOSPHORYLATION	-1.739	0.030	0.426

Relationships between OAZ2 and PPI network, MSI, TNB, TMB in COAD

Protein-protein international network (PPI) analysis was implemented by us with the support of the website STRING running online and finally we found about 10 genes (PSMD12, PSMA1. PSMC3, PSMA3, PSMAS, OAZ3, OAZ1, ODC1, PSME2, AZIN1) were closely related to OAZ2 expression (Fig. 6A). Taking advantage of the online tool Sangerbox we calculated the relationship among OAZ2 expression and TMB, MSI, TNB through the way of pearson’s method with premise of threshold value P < 0.05. The result of our study emphasized that MSI (P = 0.00049; Fig. 6B) and TMB (P = 4.9e-05; Fig. 6C) were tightly connected with OAZ2 expression while TNB (P = 0.27; Fig. 6D) was identified not relevant with OAZ2.

Tumor immune infiltration and tumor microenvironment

With the help of online tool Sangerbox, we found there existed strong associations between OAZ2 mRNA expression level and seven tumor immune infiltration levels such as Purity, B cell, CD8 + T cell, CD4 + T cell, Macrophage, Neutrophil, Dendritic cell (all P < 0.05; Fig. 7A). At the same time, expression level of OAZ2 correlated intimately with ESTIMATEScore and ImmuneScore, Stromal Score (all P < 0.05; Fig. 7B).

Correlations between OAZ2 and immune checkpoint molecules, immune cells pathway in COAD

We calculated the connections between immune-checkpoint molecules, immune cells pathway through and OAZ2 through utilizing the pearson’s method. In regard to immune checkpoint molecules, we detected that the OAZ2 expression level had a significantly association with BTLA, CD27, CD274, CD86, HAVCR2, ICOS, LAIR1, TNFRSF8, VSIR and so on in patients with COAD (all P < 0.05; Fig. 7C). With respect to immune cells pathway, we found that the OAZ2 mRNA expression closely related to Activated B cell, Activated CD4 T cell, Central memory CD4 T cell, Effector memeory CD4 T cell, Gamma delta T cell, Immature dendritic cell, Natural killer cell and Type1 T helper cell and so on in patients with COAD (all P < 0.05; Fig. 7D).

Ornithine decarboxylase (ODC) plays an important role in polyamine biosynthesis which interrelated with many cellular processes such as proliferation, differentiation and apoptosis[22]. Over-accumulation of ODC may stimulate tumorigenesis and could be detected in many epithelial cancers like colon, skin, prostate. As universally demonstrated in numerous studies, antizyme especially Ornithine decarboxylase antizyme1 (OAZ1) could suppress tumor-growth through promoting degradation of ODC[23, 24].

Similar to OAZ1, OAZ2 inhibits the transport of polyamine via binding directly to ODC[25] and can serve as a tumor suppressor in gastric cancer[14] and neuroblastoma[12]. Cho LY et al. demonstrated that OAZ2 rs7403751 showed an obvious decrease in gastric cancer risk under high concentration of daidzein[14]. He H et al. confirmed that OAZ2 could augment OAZ1’s function of maintaining steady state of the follicle through inhibiting polyamine biosynthesis[26]. Li Y et al. proved that OAZ2 was identified as direct downstream target of tumor-suppressor miR-34a and overexpression of OAZ2 could save the chemosensitivity in colon cancer which impaired by deficiency of miR-34a[13]. However, little research has been carried out about the prognostic roles of OAZ2 in CRC.

In this research, we firstly reported the relationship between the low expression of OAZ2 and the poor prognosis in COAD patients. OAZ2 was found expressed lower in tumor tissue than in normal tissue and it was significantly correlated with OS. Our study systematically analyzed the RNA-seq data downloaded from the TCGA database in COAD and divided these patients into two sets in accordance with the medium-level expression of OAZ2: low- or high-level groups. Uni- and multivariate Cox regression analysis were executed to indicate that gender, stage, M and the expression level of OAZ2 all could independently worked as prognostic factors for COAD patients.

Owing to the advantages of user convenience and superiority over traditional diagnostic criteria, nomogram model has gradually become a new method for prognosis of tumor[27, 28]. Wu J et al. established a nomogram which based on seven variables including factors like clinicopathological, demographic and it had been proved to be correlated with the overall survival of low-grade endometrial stromal sarcoma patients[29]. Zeng Q et al. build a nomogram prognosis model and validated that it possessed more brilliant capabilities of predicting the OS than existing models we had for breast malignant tumor patients[30]. In our research, we established a predictive model of nomogram vividly on the basis of OAZ2 expression level and connected clinicopathological parameters such as age, gender, race, stage, T, M, N. At the same time, the AUCs of 1-, 3- and 5-years survival rates and the calibration curves value were calculated for assessment and we could find the model of prognosis nomogram we constructed working definitely well.

GSEA as one useful means of revealing the difference in survival was performed by us in this article to find the OAZ2 related signaling pathways between the OAZ2 high-expression level and low-expression level groups[31]. Two signaling pathways including cardiac muscle contraction and oxidative phosphorylation showed significant association with low-OAZ2 phenotype. As previous researches have shown that oxidative phosphorylation (OXPHOS) and glycolysis could provide critical ATP which needed by tumor cells in proliferation process[32]. Proved by Sun X and Vellinga TT, OXPHOS had strong capacity in increasing the malignancy of CRC and also could raise drug resistance of CRC[33]. Wu Z et al. certificated that OMA1 played a major role in coordinating glycolysis and oxidative phosphorylation and promoting the progression of CRC under Hypoxic environment[34]. Consistent with our research that OXPHOS could serve as significant signaling pathway in colorectal cancer proliferation. Qi L et al. found that three signaling pathways including cellular respiration, electron transfer chain and oxidative phosphorylation were closely related to low-ITGBL group in colorectal cancer via usage of GSEA[35]. Mahdevar M et al. proved that LncRNA SRA could act as a tumor suppressor in CRC through the oxidative phosphorylation pathway[36]. Liu YD et al. demonstrated that Let-7a could inhibit OXPHOS pathway through SNAP23 thus suppressing the progression of colorectal cancer[37].

Tumor mutational burden (TMB) had been proved by previous studies that it could act as independent response indicator of immune checkpoint inhibitors (ICPIs)’s treatment effect[38] and microsatellite instability (MSI) was also certificated that it could play a role in immunotherapy as a predictive target[39]. In our study, we analyzed the connection between OAZ2 expression and MSI, TMB, tumor neoantigen burden (TNB) through utilizing the pearson’s method and there existed strong contact among OAZ2 and MSI, TMB in COAD patients without TNB. We also had verified the association of OAZ2 expression and tumor immune infiltration levels, tumor environment such as Purity, B cell, CD8 + T cell, CD4 + T cell, Macrophage, Neutrophil, Dendritic cell, ESTIMATEScore, ImmuneScore, Stromal Score, the results showed there existed close-knit between them. And we also confirmed closely relationships in COAD patients between OAZ2 and immune checkpoint molecules, immune cell pathways such as BTLA, CD27, CD274, CD86, HAVCR2, ICOS, LAIR1, TNFRSF8, VSIR, Activated B cell, Activated CD4 T cell, Central memory CD4 T cell, Effector memeory CD4 T cell, Gamma delta T cell, Immature dendritic cell, Natural killer cell, Type1 T helper cell and so on. Finally, we came to the conclusion that OAZ2 had an intimate relationship with immunity.

Overall, it might be our first time to probe the relationship between expression level of OAZ2 and its effect on COAD patients. OAZ2 proved to be a reliable landmark for prognosis indicated by uni- and multivariate cox hazard regression analyses. Then, OAZ2 was also found closely related with oxidative phosphorylation pathway via GSEA and intimately connected with immunity via MSI, TMB. We successfully build a model for survival prognosis called nomogram which based on OAZ2. However, there also existed limitations in this research. To first, it seemed to be insufficient and limited to obtain clinical information from TCGA dataset barely, with absent information of treatment too. To second, we just paid attention on bioinformatic analysis of OAZ2 in this article while lacking validation of experiment. Last but not the least, it would be the further target for us to verify the conclusion through implementing in vivo and in vitro experiments.

In summarization, our research discussed that OAZ2 could serve as a potent indicator for COAD in prognosis and related closely to immunity. We also discovered two significant signaling pathways via using GSEA and built a nomogram model successfully. Furthermore, immunity was certificated intimate relationships with OAZ2 in COAD. However, the role of OAZ2 in prognosis also need verification in vivo and in vitro at the same time in our follow-up research.

CRC

Colorectal cancer

COAD

Colon adenocarcinoma

ODC

Ornithine decarboxylase

OAZ2

Ornithine decarboxylase antizyme 2

TCGA

The Cancer Genome Atlas

SNPs

Single nucleotide polymorphisms

Overall survival

DEGs

Differentially expressed genes

GSEA

Gene set enrichment analysis

MSigDB

Molecular signatures database

ROC

Receiver operating characteristic

AUC

Area under the curve

TMB

Tumor mutational burden

MSI

Microsatellite instability

TNB

Tumor neoantigen burden

PPI

Protein-protein interaction

OXPHOS

Oxidative phosphorylation.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and material

The RNA-seq FPKM data of OAZ2 and related clinical information could be acquired from The Cancer Genome Atlas (TCGA) Data portal (https://portal.gdc.cancer.gov/).

Competing interests

The authors declare that they have no competing interests.

Funding

This article has no fundings.

Authors' contributions

WC: Protocol/project development; BYZ: Data collection or management; GXZ: Data analysis; LSQ: Data analysis and manuscript revision; SJZ, XYM: Manuscript writing/editing.

Acknowledgements

We would like to thank the timely help contributed by researchers and co-researchers for this study. All authors have read and approved the final manuscript.

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Ornithine decarboxylase antizyme 2 (OAZ2) in human colon adenocarcinoma: a potent prognostic factor associated with immunity

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Abstract

Figures

Introduction

Methods And Materials

Data acquisition

Gene set enrichment analysis

The Kaplan Meier survival analysis and the receiver operating characteristic analysis

Uni- and Multivariate Cox hazard regression analysis

The establishment of a nomogram for prognosis prediction

Usage of Sangerbox online website tool

PPI network and HPA database

Statistical analysis

Results

Expression levels of OAZ2 in COAD from TCGA and HPA database

Association between OAZ2 expression and clinicopathologic parameters

OAZ2 could serve as an independent prognostic factor

Establishment of COAD prognostic prediction nomogram

GSEA identified OAZ2related signaling pathways

Relationships between OAZ2 and PPI network, MSI, TNB, TMB in COAD

Tumor immune infiltration and tumor microenvironment

Correlations between OAZ2 and immune checkpoint molecules, immune cells pathway in COAD

Discussion

Conclusions

Abbreviations

Declarations

References

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