Construction and Validation of A Novel RNA Binding Protein-Associated Prognostic Model for Gastric Cancer

doi:10.21203/rs.3.rs-958860/v1

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Construction and Validation of A Novel RNA Binding Protein-Associated Prognostic Model for Gastric Cancer

https://doi.org/10.21203/rs.3.rs-958860/v1

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Background and Aims: RNA binding proteins (RBPs) dysregulation played crucial roles in human malignancies prognosis. However, the roles of RBPs in gastric cancer (GC) prognosis remains unclear. Therefore, we aimed to construct a risk score model based on prognostic-associated RBPs to predict the survival of GC patients.

Methods: The RNA sequencing data of GC from the Cancer Genome Atlas (TCGA) database were download. The univariable and multivariable Cox regression analysis was performed to determine the prognostic-associated RBPs, and subsequently constructed a risk score model. Indeed, the risk score model was validated by Kaplan-Meier and receiver operating characteristic (ROC) curves based on training cohort and test cohort from TCGA. Finally, a nomogram was constructed to predict the survival of GC patients according to these prognostic-associated RBPs.

Results: Four prognostic-associated RBPs (RNASE3, RNASE1, BOLL, and PABPC5) were identified, and then constructed a risk score model. Further analysis showed that GC patients in the high-risk group had a poorer overall survival (OS) compared with that in the low-risk group based on the risk score model. The area under the curve of the time-dependent ROC of the prognostic model were 0.706 and 0.631 in the training cohort and test cohort, respectively, revealing a good prognostic model. A nomogram was established, and showed a satisfactory sensitivity and specificity to predict the survival rate of GC patients.

Conclusion: Four prognostic-associated RBPs were identified, and constructed a risk score model, which may provide promising treatment targets and prognostic markers for GC patients.

Surgery

Oncology

Gastric cancer

The Cancer Genome Atlas

RNA binding protein

Prognostic model

Gastric cancer (GC) is one of the most common malignancies and ranks the third cancer-related mortality worldwide^{1, 2}. Patients with GC are commonly diagnosed at an advanced stage because of lacking specific symptoms. Even though the progression of treatment modalities for GC, such as surgical resection, adjuvant chemotherapy and targeted therapy, are developed, the average 5-year relative survival rate of GC remain unsatisfactory^{3, 4}. Therefore, further understanding of molecular mechanism of GC to develop new diagnostic and prognostic biomarkers are critical to improve clinical outcomes.

RNA binding proteins (RBPs) are a type of proteins and contain ribonucleic acid (RNA)-binding domains^{5, 6}. At present, more than 1,500 RBPs have been found and identify as vital role in post-transcriptional gene modulation, such as RNA splicing, transport, translation, localization, and stability^{7, 8}. In recent years, an increasing number of studies have found that aberrant expression of RBPs play an important role in the initiation and development of various human malignancies. RNA binding protein RNPC1 directly bound to the 3'-untranslated region (3'-UTR) of AURKB, enhanced AURKB mRNA stability, and subsequently promoted the proliferation, migration, and invasion of gastric cancer cells⁹. SAMD4A, as a conserved RBP, has been identified as a novel breast tumor suppressor, due to which can inhibit tumor angiogenesis by specifically downregulating the expression of proangiogenic genes, such as C-X-C motif chemokine ligand 5 (CXCL5), endoglin (ENG), interleukin 1β (IL1β), and angiopoietin 1 (ANGPT1) through directly binding with 3'UTR of these mRNAs and leading to the imbalance of angiogenic genes expression¹⁰. It has found that RNA binding protein IMP3 directly binds to MEKK1 mRNA 3'-UTR, enhances MEKK1 mRNA stability, and promotes MEKK1 expression, which sequentially activates MEK1/ERK signaling and leads to metastasis in colorectal cancer patients¹¹. Notably, dysregulation of RBPs is closely associated with the occurrence and development of human malignancies. However, the knowledge of RBPs-related mechanisms in the occurrence and development of human malignancies remain undetermined. Therefore, a systematic function analysis of RBPs will help us better investigate their role and develop diagnostic and prognostic biomarkers.

Although many studies have demonstrated the relationship between RBPs and GC prognostic, an RBPs-associated prognostic model is rarely constructed^12–14. Therefore, we downloaded GC RNA-sequencing and corresponding clinical characteristics data from the Cancer Genome Atlas (TCGA) database. Subsequently, aberrant expressed RBPs were obtained compared cancerous and normal samples by bioinformatic analysis. Then, a four RBPs-associated prognostic model was constructed using a series of bioinformatic analysis. Moreover, the model was validated through intrinsic data (TCGA). Our finding might provide new potential biomarkers for GC treatment and prognosis.

Data acquisition and processing

RNA sequencing data of 375 GC samples and 32 normal samples, and corresponding clinical characteristics were downloaded from TCGA (https://portal.gdc.cancer.gov). The differently expressed RBPs compared GC samples with normal samples were identified using the limma package in R 4.0 software according to the criteria of |log2 ^{fold change (FC)}| ≥1 and false discovery rate (FDR)<0.05.

Gene ontology (GO) enrichment and Kyoto Encyclopaedia of genes and genomes (KEGG) pathway analysis

The GO enrichment and KEGG pathway analysis was to determine the biological functions of the differently expressed RBPs using clusterProfiler, org.Hs.eg.db, enrichplot and ggplot2 packages in R 4.0 software.

Protein-protein Interaction Network Construction And Module Screening

Functional associations between protein-protein interaction were explored through the STRING online database (https://string-db.org) and Cytoscape 3.6.1. Subsequently, the key modules and hub genes were screened by using Molecular Complex Detection (MCODE) according to the criteria of scores >7 and node counts >5¹⁵. All P<0.05 were considered as significant difference.

Screening of key RBPs and construction of a prognostic model

Univariate and multivariate Cox regression analyses were performed to identify prognostic-associated RBPs by the survival package in R software. Subsequently, a risk score model was constructed based on the expression levels and coefficients of prognosis-associated RBPs. The risk score formula for each GC patient was as follows: Risk scores = β1*Exp1 + β2*Exp2 + βi*Expi, where β represented the coefficient score, Exp represented the gene expression, and i represented i^th prognosis-associated RBP.

Validation The Performance Of The Prognostic Model

The GC patients were divided into low-risk and high-risk group based on the median risk score. A log-rank test was performed to evaluate the survival difference compared the low-risk group with the high-risk group using survival package in R software. Moreover, receiver operating characteristics (ROC) curves were drawn to determine the predictive capability of the prognostic model using survivalROC package in R software.

Nomogram Model Construction

A nomogram model was constructed using the rms package in R software based on the prognosis-associated RBPs to predict the survival rate of GC patients at 1-, 2-, and 3- years.

The expression levels and prognostic significance of the prognosis-associated RBPs

The Human Protein Atlas (HPA) is an online database, include a large of immunohistochemical data, and contain a lot of survival data from TCGA. Therefore, the HPA was used to analyze the expression levels and prognostic significance of the prognosis-associated RBPs in GC patients.

Statistical analysis

All analyses were conducted by available packages in R software 4.1.0. Values of p < 0.05 were considered statistical significance.

Identification of differently expressed RBPs in GC patients

In present study, we aimed to obtained the prognostic-associated RBPs in GC patients using bioinformatic analysis. The analysis steps were illustrated in Figure 1. The databases of GC were downloaded from TCGA, which included 375 GC samples and 32 normal samples. As Figure 2 showed, there were 79 differently expressed RBPs according to the criteria of |log2 ^{fold change (FC)}| ≥1 and false discovery rate (FDR)<0.05. Of these 79 RBPs, 39 RBPs were upregulated in GC samples, 40 RBPs were downregulated in GC samples (Table 1).

Table 1

The differently expressed RBPs
	Gene
Up-regulated	TRIM71 L1TD1 CD3EAP RDM1 XPO5 HENMT1 BOP1 BOLL TDRD5 OAS3 RNASE1 APOBEC4 PUS7 IGF2BP1 IGF2BP2 IGF2BP3 MOV10L1 WARS RNF113B DNMT3B TDRD6 PIWIL1 PIWIL3 RNASE2 RNASE3 DKC1 EZH2 URB2 DDX4 BRCA1 PABPC1L MEX3A TERT DCAF13 POP1 ZNF239 RANBP17EXO1 PABPC5
Down-regulated	RBFOX3 ELAVL3 ELAVL4 DZIP1 APOBEC2 APOBEC1 MYEF2 ZCCHC24 SMAD9 ADAD2 ADARB1TDRD10 AFF3 RBM20 SIDT2 PAIP2B RNASE10 ANG LARP6 ZFP36 EIF1AY RBM24 RBPMSRBPMS2 RNASE11 ENDOU DDX3Y SNRPN TLR3 CELF2 CPEB1 CPEB2 CPEB3 PABPC1L2APABPC1L2B NR0B1 EIF4E3 NANOS1 NOVA1

GO and KEGG pathway enrichment analysis of the differently expressed RBPs

GO enrichment analysis was performed to investigate the function enrichment of the differently expressed RBPs in GC. As Figure 3A showed, the differently expressed RBPs were significantly enriched in regulation of translation, regulation of cellular amide metabolic process, and RNA catabolic process in biological process (BP) analysis. In the cellular component (CC) analysis, the differently expressed RBPs were mainly enriched in cytoplasmic ribonucleoprotein granule, ribonucleoprotein granule, and P granule (Figure 3A). In regard to the molecular function (MF), the differently expressed RBPs were notably enriched in mRNA 3'−UTR binding, catalytic activity, acting on RNA, and translation regulator activity (Figure 3A). KEGG pathway analysis was conducted, and suggested that the differently expressed RBPs were significantly enriched in MicroRNAs in cancer, RNA degradation, and mRNA surveillance pathway (Figure 3B).

Ppi Network Construction And Key Modules Selecting

We constructed the PPI network through STRING database and Cytoscape software to investigate the potential roles of the differently expressed RBPs in GC. As Figure 4A showed, there were 59 nodes and 276 edges in the network based on the data from STRING database. Furthermore, we analyzed the co-expression network to detect possible key modules by using plug-in MODE in Cytosacpe software, and obtained 44 nodes and 136 edges (Figure 4B).

Identification Of Prognostic-associated Rbps

A total of 59 key differently expressed RBPs were obtained from the PPI network. Subsequently, univariable and multivariable Cox regression was performed to determine the prognostic-associated RBPs form these hub candidate RBPs in GC. As Figure 5A showed, 9 prognostic-associated RBPs (ADARB1, RNASE3, RNASE2, RNASE1, BOLL, EZH2, ZFP36, LARP6, and PABPC5) were identified by univariable Cox regression. Furthermore, 4 RBPs (RNASE3, RNASE1, BOLL, and PABPC5) were identified as independent prognostic-associated predictors in GC (Figure 5B).

Validation of expression and prognostic value of these prognostic-associated RBPs

We examined the expressions of RNASE3, RNASE1, BOLL, and PABPC5 compared 375 GC samples with 32 healthy controls from TCGA database. Figure 6A showed that RNASE3, RNASE1, BOLL, and PABPC5 was significantly up-regulated in GC samples compared to the healthy controls (P=0.0012, P=0.0011, P<0.001, and P=0.028, respectively). Moreover, we found that the higher expressions of RNASE3, RNASE1, BOLL, and PABPC5 were associated with a poorer overall survival (OS) by Kaplan-Meier survival analysis (P=0.019, P=0.046, P=0.017, and P=0.022, respectively) (Figure 6B).

Construction And Analysis Of A Prognostic-associated Risk Score Model

A prognostic-associated risk score model was constructed based on the four independent prognostic-associated RBP. The risk score of each GC patient was computed according to the following formula: Risk score = 0.44*Exp RNASE3 + 0.25*Exp RNASE1 + 2.46*Exp BOLL + 1.63*Exp PABPC5.

Then, we performed a survival analysis to assess the predictive performance of the risk score model. A total of GC patients from TCGA were divided into training and testing datasets including 187 cases and 184 case, respectively. Subsequently, 184 GC patients in training dataset were divided into low- and high-risk groups based on the median risk score. We found that GC patients in the high-risk group were associated with poorer OS compared to GC patients in the low-risk group (Figure 7A). Moreover, the area under the ROC curves (AUCs) for 1 year OS was 0.706 according to the risk score model in the training datasets (Figure 7B). Furthermore, the Figure 7C showed that the GC patients with overexpression of these four prognostic-associated RBPs contributed to high-risk scores and poorer OS. Consistently, in test datasets, we also discovered that GC patients in the high-risk group were associated with poorer OS based on this prognostic-associated risk score model. The results were showed in Figure 7D, 7E and 7F. Therefore, these results revealed that the four prognostic-associated RBPs risk score model had a satisfactory sensitivity and specificity of prediction values for GC patients.

Construction of a nomogram based on the four prognostic-associated RBPs

We built a nomogram plot to construct a quantitative model and predict the survival probability of 1-, 2-, and 3- years OS for GC patients based on four prognostic-associated RBPs (Figure 8). A vertical line was draw to determine the points for each variable and summed the points of all variables to calculate the total points for each patient, and then normalized them to distribution of 0 to 100. Therefore, the survival rates of GC patients can be calculated through drawing a vertical line between the total points axis and each prognosis axis at 1-, 2-, and 3- years.

Assessment Of The Prognostic Value Of Clinical Parameters

We performed univariable and multivariable Cox regression to assess the prognostic value of different clinical characteristics for GC patients from TCGA databases. In training dataset, we found that patients age, stage and risk scores were associated with OS by univariable Cox regression (Figure 9A). Moreover, multivariable Cox regression showed that patients age, stage and risk scores were associated with OS (Figure 9B). However, in test dataset, we discovered that stage and risk scores were associated with OS by univariable and multivariable Cox regression (Figure 9C and 9D).

The Protein Levels Of The Four Prognostic-associated Rbps

We detected the protein levels of the four prognostic-associated RBPs using the immunohistochemistry results from HPA. The results revealed that the protein levels of RNASE3, RNASE1, and PABPC5 was significantly up-regulated in GC tissues compared with normal tissues, whereas the protein level of BOLL was not detected between GC tissues and normal tissues (Figure 10).

It has been reported that RBPs dysregulation are associated with the occurrence and development of human malignances^16–18. However, few of the prognostic-associated RBPs has currently been identified in GC^{12, 13}. Therefore, it is necessary to explore crucial RBPs and understand the potential carcinogenic molecular mechanisms of GC. In present study, 79 differently expressed RBPs were identified compared GC samples with normal samples based on GC data from TCGA. Then, GO analysis, KEGG pathway analysis, and PPI network was systematically performed for these differently expressed RBPs. Subsequently, four prognostic-associated RBPs (RNASE3, RNASE1, BOLL, and PABPC5) were obtained for GC patients by using univariable and multivariable Cox regression. Moreover, we found that RNASE3, RNASE1, BOLL, and PABPC5 was significantly up-regulated in GC samples, and the overexpression of RNASE3, RNASE1, BOLL, and PABPC5 was associated with poor OS for GC patients. Therefore, a prognostic-associated risk score model was constructed based on the four prognostic-associated RBPs, and confirmed that which had satisfactory sensitivity and specificity to predict the survival rate of GC patients.

The function enrichment and KEGG pathway analysis revealed that the 79 differently RBPs were associated with regulation of translation, regulation of cellular amide metabolic process and RNA catabolic process, and MicroRNAs in cancer, RNA degradation and mRNA surveillance pathway. Several studies have found that regulation of translation, regulation of cellular amide metabolic process and RNA catabolic process is related to the occurrence and development of the human malignancies^19–22. Moreover, previous studies have proved that MicroRNAs, RNA degradation and mRNA surveillance pathway is closely associated the carcinogenic molecular mechanisms of human malignancies^23–28. Therefore, we considered that these differently RBPs may play an important role in the occurrence and development of GC.

Then, we built a PPI network of these differently expressed RBPs, and detected 59 hub RBPs. Indeed, we obtained four prognostic-associated RBPs (RNASE3, RNASE1, BOLL, and PABPC5) by using univariable and multivariable Cox regression based on these hub RBPs. RNASE3 is a member of the ribonuclease A superfamily, which is a small and highly cationic protein that participates in the immune defense response^29–31. It has reported that RNASE3 is one of the most factors associated with osteosarcoma metastasis³². RNASE1 belongs to the Ribonuclease A superfamily, which have a variety of biological functions, such as host defense, immune cell regulation, tissue repair and remodeling, and RNA cleavage^{33, 34}. It has shown that RNASE1 especially associates with the overall survival (OS) of multiple myeloma (MM) patients, as well as GC patients^{35, 36}. BOLL is an ancestral member of the DAZ (deleted in azoospermia) gene family, and require for testicular function, maintenance, and spermatogenesis in males³⁷. Kang KJ et al. have discovered that the over-expression of BOLL promotes colorectal cancer cell proliferation, colony formation, and migration³⁸. PABPC5 is a member of the cytosolic poly(A) binding protein family, and involves in metabolism of DNA and RNA in mitochondria³⁹. It has reported that PABPC5 is closely related to the poor prognosis of ovarian cancer patients⁴⁰. Therefore, RNASE3, RNASE1, BOLL, and PABPC5 played a vital role in the prognosis of human malignancies. However, the prognostic prediction value of RNASE3, RNASE1, BOLL, and PABPC5 for GC patients remains unclear.

Subsequently, the prognostic-associated risk score model was constructed based on the RNASE3, RNASE1, BOLL, and PABPC5. According to the risk score model, GC patients with high-risk score, who had a poor OS by validating training cohort and test cohort. Therefore, the prognostic-associated risk score model based the RNASE3, RNASE1, BOLL, and PABPC5, which had satisfactory sensitivity and specificity to predict the survival rate of GC patients.

Obviously, there were undeniable some limitations in present study. First, all the data were acquired based on online databases. Therefore, further studies are necessarily required to confirm our findings. Second, the expression levels of BOLL mRNA were up-regulated in GC tissues according to TCGA databases, whereas the protein levels of BOLL were not detected in GC tissues based on HPA databases. Therefore, it is necessary to determine the expression levels of mRNA and protein of BOLL using our own cohort samples in future. Third, it is vital to explore the underlying mechanism between these prognostic-associated RBPs (RNASE3, RNASE1, BOLL, and PABPC5) and GC in vivo and in vitro.

In conclusion, we comprehensively assessed the expression and prognostic value of differently RBPs through a systematic bioinformatics analysis in GC. The risk score model of four prognostic-associated RBPs was constructed, and which had satisfactory sensitivity and specificity for survival prediction in GC patients. Therefore, the present study may provide promising treatment targets and prognostic molecular markers.

RBPs: RNA binding proteins; GC, gastric cancer; ROC, receiver operating characteristic; TCGA, the Cancer Genome Atlas; OS, overall survival; FDR, false discovery rate; GO, Gene ontology; KEGG, Kyoto Encyclopaedia of genes and genomes; BP, biological process; CC, cellular component.

Acknowledgements

None.

Authors’ contributions

G-H Y designed the study, searched databases, performed data analysis, andwrote the manuscript.

Funding

Basic Research Project of Baoan District(No. 2020JD286).

Availability of data and materials

RNA sequencing data of 375 GC samples and 32 normal samples, and corresponding clinical characteristics were downloaded from TCGA (https://portal.gdc.cancer.gov). Immunohistochemical data are available from HPA (The Human Protein Atlas).

Declarations Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

There were no competing interests.

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Construction and Validation of A Novel RNA Binding Protein-Associated Prognostic Model for Gastric Cancer

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Version 1

Abstract

Figures

Introduction

Methods

Data acquisition and processing

Protein-protein Interaction Network Construction And Module Screening

Validation The Performance Of The Prognostic Model

Nomogram Model Construction

Statistical analysis

Results

Identification of differently expressed RBPs in GC patients

Ppi Network Construction And Key Modules Selecting

Identification Of Prognostic-associated Rbps

Construction And Analysis Of A Prognostic-associated Risk Score Model

Assessment Of The Prognostic Value Of Clinical Parameters

The Protein Levels Of The Four Prognostic-associated Rbps

Discussion

Abbreviations

Declarations

References

Status:

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