Journal of Proteomics & Bioinformatics
Open Access

Ribosome Biogenesis: A Major Player in the Exosome of Gastric Cancer

Chun-yan Lv^1*, Xian-kui Cheng², Yuan Zhong¹, You-li Jian³, Ming-ming He¹ and Tao Lei^{1 *}Correspondence: Chun-yan Lv, Department of Pathology, School of Basic Medicine, Chengdu University, Chengdu, China, Email:

Author info »

Introduction

Gastric Cancer (GC) is a kind of cancer that occurs in the stomach, mostly from gastric mucosal epithelial cells. The most common pathological type is adenocarcinoma. The long-term survival rate (>5 years) of early GC after surgery can reach 90.9%-100% [1-3]. However, there is still no effective treatment for advanced GC. Even with various treatments, the survival rate of 5-year is still less than 30%. The incidence rate of GC ranks the fifth in malignant tumors and the third in mortality worldwide. Early detection of early GC or postoperative recurrence of GC is still the focus of cancer prevention and treatment [4].

The exosome is extracellular vesicle with the diameter of 30- 150 nm, which is released into the extracellular matrix after the outer membrane fuses with the cell membrane [5]. They have lipid bilayer and contain mRNA, lncRNA, circRNA and transfer RNA (tRNA), etc., which are vital for tumor cell biology, such as proliferation and metastasis, also can serve as prognostic markers and/or grading basis for patients with tumors, belong to the research field of liquid biopsy [6-12].

In this study, we obtained the RNA (including mRNA, lncRNA and circRNA) information of plasma exosome samples from patients with GC and healthy people from exoRBase database, screened the differentially exosomal RNAs in GC using bioinformatics methods, constructed a network related to GC with the competitive endogenous RNA (ceRNA) [13-15]. The functional enrichment analysis of mRNA is also carried out in order to discover more new GC markers and provide guidance for early detection, early identification and early therapy of GC.

Materials and Methods

Data source

The expression profiles of RNA in GC and healthy blood samples were download from exoRBase 2.0. The data deadline was December 27, 2022. The data format was pretreated. 9 blood samples from GC patients and 118 healthy blood samples were obtained, involving mRNA and lncRNA 35517 and circRNAs 79084.

Filtering of Diversely Emanated Genes (DEGs)

The R language "limma" package was applied to take the mean value and the "sva" package was employed for batch correction to identify the DEGs. The filtering condition for DEGs was p-value<0.05. The corresponding volcano map was drawn with the "heat map" package of top 20 genes with obvious differences.

Prediction of miRNAs and construction of ceRNA networks

Target scan human and miRanda in ENCORI (https://starbase.sysu.edu.cn/) were employed to combinedly forecast the differentially express mRNAs-bound miRNAs. The miRNA target function in ENCORI was applied to forecast the miRNA binding to DEcircRNAs and the lncRNAs binding miRNAs were predicted in miRcode (http://www.mircode.org/). Finally, the prediction data of miRNAs and the corresponding mRNAs, circRNAs and lncRNAs were input into the cytoscape software (version 3.7.2) to display graphically the ceRNA network.

Hub gene sifting

The cytoHubba plugin of cytoscape software was utilized to pick the top 10 nodes out as hub gene by the betweenness algorithm and the topology index of the betweenness of each hub node was used to plot in excel.

Functional enrichment unpacking

The DEmRNAs were input to DAVID database (https://david.ncifcrf.gov/), with the identifier as "OFHCIAL-GENE-SYMBOL" and the species as "Homo sapiens", GO annotations and KEGG pathway analysis were carried according to the conditions of P<0.05 and False Discovery Rate (FDR)<0.05 (FDR is the p-value after correction and calculation).

Statistical methods

All statistical analyses were completed in R4.2.2 statistical software. The mean ± standard deviation was employed to display the measurement data and t-test or analysis of variance was utilized to perform the statistical test. P<0.05 means the difference was statistically significant.

Results

Results of DEGs screening

62 DemRNAs and 3 differentially expressed lncRNAs 2 (all up-regulated genes) were screened out, with the Heterogeneous Nuclear Ribo-Nucleo Protein K (HNRNPK) as the most significant mRNA, AC011450.1 exhibiting the most relevant lncRNA. The DEGs heat map is as documented (Figure 1). One down regulated circRNAs (hsa_circ_0007476, genomic position: chr4:53414615-53428183, genomic length: 13568, spliced length: 356) with differential expression (DEcircRNAs) as shown (Figure 2).

Figure 1: Heat map of messenger Ribo-Nucleic Acids (mRNAs) and long noncoding Ribo-Nucleic Acids (lncRNAs) in plasma exosomes of patients with Gastric Cancer (GC) and Healthy (N).

Figure 2: Hsa_circ_0007476 (exo_circ_56660) in plasma exosomes of patients with Gastric Cancer (GC) and Healthy. Note: ns: Not significant.

Establish of exosomal ceRNA network

According to the data of DEmRNAs, DElncRNAs, DEcircRNAs and their binding miRNAs, a competitiveness endogenous RNA network was constructed, with 192 mRNA nodes (involving 22 mRNA), 32 lncRNA nodes (1 lncRNA involved), 28 circRNA nodes (involving 1 circRNA) and 151 miRNA nodes (Figure 3). The top 10 pivot genes with the highest betweenness scores were distinguished by cytoscape software, including 5 mRNAs, 4 miRNAs and 1 circRNA (Figure 4).

Figure 3: The competitiveness endogenous Ribo-Nucleic Acid (RNA) regulatory network related to micro RNAs (miRNA).

Figure 4: The first 10 pivot genes with the highest betweenness scores in exosomal competing endogenous RNAs (ceRNAs). Note: ZEB 2: Zinc finger E-Box-Binding Homeobox 2; LCOR: Ligand Dependent Nuclear Receptor Corepressor; MFSD 14A: Major Facilitator Superfamily Domain containing 14A.

GO annotation analysis

The Gene ontology of overlapping genes is as shown (Figure 5). The biological processes of differential exosomal mRNA included cytoplasmic translation, ribosome (nucleoprotein complex, large subunit), ribosomal Ribo-Nucleic Acid (rRNA) processing, rRNA metabolic process, ribosome (large subunit, ribonucleoprotein complex) assembly and non-coding RNA (ncRNA) processing. As we know, the occurrence of intestinal gastric cancer goes through a multi-step process, involving mutations such as Tumor Protein (P53) gene, while diffuse gastric cancer involves the reduction or deletion of cadherin, which is also detected in the biological processes involved in these differential genes, but not on the top 10 range [16-20]. The paths involved in the former are as exhibited (Table 1). The cell components where the genes located were mainly in the ribosome, its subunit (large and small subunit) and polyribosomes formed by ribosomes, focal adhesion, cell-substrate junction, proton-transporting Adenosine Triphosphatase (ATPase) (two-sector) and Adenosine Triphosphate (ATP) synthase complex (catalytic domain), endoplasmic reticulum membrane (its cytoplasmic side, rough endoplasmic reticulum and its membrane). Molecular functions included protein translation and modification, cadherin binding, rotational mechanism and proton channel activity, etc.

Figure 5: Gene Ontology (GO) annotations. A: The bar chart; B: The bubble plot.

Table 1: The paths involved in the p53 gene.

Enrichment analysis of KEGG

The KEGG pathway shows that the DEmRNA is mainly concentrated in the mRNA monitoring pathway (involving mRNA Protein Phosphatase-2 Regulatory Subunit B'Gamma (PPP-2 R5C), RNA-binding Protein with Serine-rich Domain 1 (RNPS1), Sin3A Associated Protein 18 (SAP18)), proteasome (involving mRNA, Proteasome Maturation Protein (POMP) and Proteasome Activator Complex Subunit 1 (PSME 1)), ribosome (assuming mRNA FAU, RPL35A and RPS15A), as shown (Figure 6).

Figure 6: Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A: Column chart; B: Bubble map.

Discussion

Gastric Cancer (GC) is one of most often malignancy of the alimentary system. The mortality of GC is high and the prognosis is unsatisfactory because of the detection often in the late stage of diagnosis [21]. Therefore, it is crucial to reduce the incidence rate and mortality of GC by early detection and the use of effective screening methods [22,23].

Exosomes are small vesicles excreted by living cells, with typical lipid bilayer membrane, carrying a variety of meaningful message such as proteins, lipids, nucleic acids, etc., play a significant role in the transmission of intercellular substances and information and gradually become a biomarker for early diagnosis of many diseases [24-26]. Nucleic acids include DNA and RNA (mRNA, miRNA, lncRNA and tRNA), which provides a good data source for us to establish a competitive endogenous regulatory network of multiple RNAs [27,28].

In this study, 62 DEmRNA, 3 DElncRNAs and 1 DEcircRNAs were searched out and the ceRNA system was built with their binding miRNA prediction data. Then, first 10 pivot genes with the highest betweenness scores were distinguished by cytoscape software including 5 mRNAs (EMSY, ZEB 2, LCOR, MFSD 14A and ERBIN), 4 miRNAs (hsa-miR-363-3p, -137, -27a-3p and -23b-3p) and 1 circRNA (hsa_circ_0000038). The GO annotation and the enrichment analysis of KEGG pathway showed that ribosome biogenesis played a major role in the exosome of gastric cancer.

It is reported that ZEB 2 expression was be relevant to the efficacy of platinum chemotherapy and the miR-338-5p-ZEB2 axis has diagnostic and prognostic value [29]. ZEB 2, as an epithelial mesenchymal transformation regulator, inhibited by miR-200c, could inhibit the metastasis of non-small cell lung cancer [30]. The highly expressed lncRNA ZEB 2 Antisense RNA 1 (ZEB 2 AS1) in GC patients had a poor prognosis [31]. The high expression of LCoR was considered as a poor prognosis marker in GC [32]. ERBB2 Interacting Protein (ERBB2IP) was upregulated gene, which might be in connection with the resistance to intraperitoneal paclitaxel in GC with peritoneal metastasis [33]. Hsa-miR-363-3p was probably a therapeutic targets and down-regulated in Tibetan with GC [34]. MiR-137 was negative correlation with Enhancer of Zeste Homolog 2 (EZH 2) and its expression was down-regulated in GC [35]. Hsa-miR-27a-3p might serve critical roles in early GC [36]. However, the role of EMSY, MFSD 14A, hsa-miR-23b-3p and hsa_circ_0000038 in the development and diagnosis of GC were still unclear, which could be used as potential targets for future research.

Conclusion

To sum up, this study used bioinformatics methods to identify exosomal DeRNA (mRNA, lncRNA and circRNA) related to GC occurrence, constructed corresponding ceRNA networks and speculate that ribosome biogenesis played a major role in the exosome of gastric cancer, which provided a molecular basis for further research on GC progress and metastasis and provided new liquid biopsy biomarkers for early diagnosis of GC. However, due to the small sample size of disease exosomes, further clinical multicenter validation is required. Different types of gastric cancer, different pathogenesis of adherent cancer and non-adhesive cancer and their differences in ceRNA also need to be clarified.

Author Contribution

Ming-ming He and Yuan Zhong: Data collection, data analysis, manuscript writing. Tao Lei, You-li Jian and Xian-kui Cheng: Data analysis. Chun-yan Lv: Project development and critical revision of manuscript.

Funding

This study was sustained by the Department of Science and Technology Key R and D projects in Sichuan Provincial (grant number: 2022YFS0200).

Acknowledgement

At the point of finishing this paper, I'd like to express my sincere thanks to all those who have lent me hands in the course of my writing this paper. I'd like to thank those leaders, teachers and working staff especially those in Chengdu University. Without their help, it would be much harder for me to finish my study and this paper.

References

1. Information Committee of the Korean Gastric Cancer Association. Korean Gastric Cancer Association-led nationwide survey on surgically treated gastric cancers in 2019. J Gastric Cancer. 2021;21(3):221-235.

   [Crossref] [Google Scholar] [PubMed]

2. Hamashima C, Systematic Review Group and Guideline Development Group for Gastric Cancer Screening Guidelines. Update version of the Japanese guidelines for gastric cancer screening. Jpn J Clin Oncol. 2018;48(7):673-683.

   [Crossref] [Google Scholar] [PubMed]

3. Park SH, Kang MJ, Yun EH, Jung KW. Epidemiology of gastric cancer in Korea: trends in incidence and survival based on Korea Central Cancer Registry Data (1999-2019). J Gastric Cancer. 2022;22(3):160-168.

   [Crossref] [Google Scholar] [PubMed]

4. Chen M, Chen K, Hou H, Li W, Wang X, Dao Q, et al. Incidence and mortality trends in gastric cancer in the United States, 1992-2019. Int J Cancer. 2023;152(9):1827-1836.

   [Crossref] [Google Scholar] [PubMed]

5. Khazaei F, Rezakhani L, Alizadeh M, Mahdavian E, Khazaei M. Exosomes and exosome-loaded scaffolds: Characterization and application in modern regenerative medicine. Tissue Cell. 2023;80:102007.

   [Crossref] [Google Scholar] [PubMed]

6. Rangel-Ramírez VV, González-Sánchez HM, Lucio-García C. Exosomes: from biology to immunotherapy in infectious diseases. Infect Dis (Lond). 2023;55(2):79-107.

   [Crossref] [Google Scholar] [PubMed]

7. Richards KE, Zeleniak AE, Fishel ML, Wu J, Littlepage LE, Hill R. Cancer-associated fibroblast exosomes regulate survival and proliferation of pancreatic cancer cells. Oncogene. 2017;36(13):1770-1778.

   [Crossref] [Google Scholar] [PubMed]

8. Huang L, Wang F, Wang X, Su C, Wu S, Yang C, et al. M2-like macrophage-derived exosomes facilitate metastasis in non‐small-cell lung cancer by delivering integrin αVβ3. MedComm. 2023;4(1):e191.

   [Crossref] [Google Scholar] [PubMed]

9. Nambara S, Masuda T, Hirose K, Hu Q, Tobo T, Ozato Y, et al. Rab27b, a regulator of exosome secretion, is associated with peritoneal metastases in gastric cancer. Cancer Genomics Proteomics. 2023;20(1):30-39.

   [Crossref] [Google Scholar] [PubMed]

10. Zhang Z, Wu H, Chong W, Shang L, Jing C, Li L. Liquid biopsy in gastric cancer: Predictive and prognostic biomarkers. Cell Death Dis. 2022;13(10):903.

    [Crossref] [Google Scholar] [PubMed]

11. Chaudhry GE, Akim AM, Safdar N, Yasmin A, Sung YY, Muhammad TS. Cancer and disease diagnosis-Biosensor as potential diagnostic tool for biomarker detection. J Adv Pharm Technol Res. 2022;13(4):243-247.

    [Crossref] [Google Scholar] [PubMed]

12. Rzhevskiy AS, Kapitannikova AY, Butnaru DV, Shpot EV, Joosse SA, Zvyagin AV, et al. Liquid biopsy in diagnosis and prognosis of non-metastatic prostate cancer. Biomedicines. 2022;10(12):3115.

    [Crossref] [Google Scholar] [PubMed]

13. Zhu K, Wang Q, Wang L. Analysis of Competitive Endogenous RNA Regulatory Network of Exosomal Breast Cancer Based on exoRBase. Evol Bioinform Online. 2022;18:11769343221113286.

    [Crossref] [Google Scholar] [PubMed]

14. Wu ZH, Li C, Zhang YJ, Lin R. Bioinformatics study revealed significance of exosome transcriptome in hepatocellular carcinoma diagnosis. Front Cell Dev Biol. 2022;10:813701.

    [Crossref] [Google Scholar] [PubMed]

15. Lai H, Li Y, Zhang H, Hu J, Liao J, Su Y, et al. exoRBase 2.0: an atlas of mRNA, lncRNA and circRNA in extracellular vesicles from human biofluids. Nucleic Acids Res. 2022;50(D1):D118-D128.

    [Crossref] [Google Scholar] [PubMed]

16. Yang D, Shi M, You Q, Zhang Y, Hu Z, Xu J, et al. Tumor-and metastasis-promoting roles of miR-488 inhibition via HULC enhancement and EZH2-mediated p53 repression in gastric cancer. Cell Biol Toxicol. 2023;39(4):1341-1358.

    [Crossref] [Google Scholar] [PubMed]

17. Yao T, Kajiwara M, Kuroiwa S, Iwashita A, Oya M, Kabashima A, et al. Malignant transformation of gastric hyperplastic polyps: alteration of phenotypes, proliferative activity, and p53 expression. Hum Pathol. 2002;33(10):1016-1022.

    [Crossref] [Google Scholar] [PubMed]

18. Luinetti O, Fiocca R, Villani L, Alberizzi P, Ranzani GN, Solcia E. Genetic pattern, histological structure, and cellular phenotype in early and advanced gastric cancers: evidence for structure-related genetic subsets and for loss of glandular structure during progression of some tumors. Hum Pathol. 1998;29(7):702-709.

    [Crossref] [Google Scholar] [PubMed]

19. Jinawath N, Furukawa Y, Hasegawa S, Li M, Tsunoda T, Satoh S, et al. Comparison of gene-expression profiles between diffuse-and intestinal-type gastric cancers using a genome-wide cDNA microarray. Oncogene. 2004;23(40):6830-6844.

    [Crossref] [Google Scholar] [PubMed]

20. Rogers WM, Dobo E, Norton JA, Van Dam J, Jeffrey RB, Huntsman DG, et al. Risk-reducing total gastrectomy for germline mutations in E-cadherin (CDH1): pathologic findings with clinical implications. Am J Surg Pathol. 2008;32(6):799-809.

    [Crossref] [Google Scholar] [PubMed]

21. Venkatasamy A, Guérin E, Reichardt W, Devignot V, Chenard MP, Miguet L, et al. Morpho-functional analysis of patient-derived xenografts reveals differential impact of gastric cancer and chemotherapy on the tumor ecosystem, affecting immune check point, metabolism, and sarcopenia. Gastric Cancer. 2023;26(2):220-233.

    [Crossref] [Google Scholar] [PubMed]

22. Jiang T, Mei L, Yang X, Sun T, Wang Z, Ji Y. Biomarkers of gastric cancer: current advancement. Heliyon. 2022;8(10):e10899.

    [Crossref] [Google Scholar] [PubMed]

23. Xu H, Li W. Early detection of gastric cancer in China: progress and opportunities. Cancer Biol Med. 2022;19(12):1622-1628.

    [Crossref] [Google Scholar] [PubMed]

24. Raez LE, Brice K, Dumais K, Lopez-Cohen A, Wietecha D, Izquierdo PA, et al. Liquid Biopsy Versus Tissue Biopsy to Determine Front Line Therapy in Metastatic Non-Small Cell Lung Cancer (NSCLC). Clin Lung Cancer. 2023;24(2):120-129.

    [Crossref] [Google Scholar] [PubMed]

25. Fujii N, Yashiro M, Hatano T, Fujikawa H, Motomura H. CD9-positive Exosomes Derived from Cancer-associated Fibroblasts Might Inhibit the Proliferation of Malignant Melanoma Cells. Anticancer Res. 2023;43(1):25-33.

    [Crossref] [Google Scholar] [PubMed]

26. Zhang X, Hou H, Jiang M, Zhang X. Aberrant circulating tumor DNA methylation and exosomal microRNA biomarkers for early detection of colorectal cancer. Mol Biol Rep. 2023;50(3):2743-2750.

    [Crossref] [Google Scholar] [PubMed]

27. Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP. A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell. 2011;146(3):353-358.

    [Crossref] [Google Scholar] [PubMed]

28. Smillie CL, Sirey T, Ponting CP. Complexities of post-transcriptional regulation and the modeling of ceRNA crosstalk. Crit Rev Biochem Mol Biol. 2018;53(3):231-245.

    [Crossref] [Google Scholar] [PubMed]

29. Wei X, Zhu J, Zhang Y, Zhao Q, Wang H, Gu K. miR-338-5p-ZEB 2 axis in Diagnostic, Therapeutic Predictive and Prognostic Value of Gastric Cancer. J Cancer. 2021;12(22):6756-6772.

    [Crossref] [Google Scholar] [PubMed]

30. Jiao A, Sui M, Zhang L, Sun P, Geng D, Zhang W, et al. MicroRNA-200c inhibits the metastasis of non-small cell lung cancer cells by targeting ZEB 2, an epithelial-mesenchymal transition regulator. Mol Med Rep. 2021;24(2):573.

    [Crossref] [Google Scholar] [PubMed]

31. Wang F, Zhu W, Yang R, Xie W, Wang D. LncRNA ZEB2-AS1 contributes to the tumorigenesis of gastric cancer via activating the Wnt/β-catenin pathway. Mol Cell Biochem. 2019;456(1-2):73-83.

    [Crossref] [Google Scholar] [PubMed]

32. You W, Ouyang J, Cai Z, Chen Y, Wu X. Comprehensive analyses of immune subtypes of stomach adenocarcinoma for mRNA vaccination. Front Immunol. 2022;13:827506.

    [Crossref] [Google Scholar] [PubMed]

33. Huang KK, Huang J, Wu JK, Lee M, Tay ST, Kumar V, et al. Long-read transcriptome sequencing reveals abundant promoter diversity in distinct molecular subtypes of gastric cancer. Genome Biol. 2021;22(1):44.

    [Crossref] [Google Scholar] [PubMed]

34. Luo Y, Zhang C, Tang F, Zhao J, Shen C, Wang C, et al. Bioinformatics identification of potentially involved microRNAs in Tibetan with gastric cancer based on microRNA profiling. Cancer Cell Int. 2015;15(1):1-8.

    [Crossref] [Google Scholar] [PubMed]

35. Weng XQ, Wang W. miR-137 modulates human gastric cancer cell proliferation, apoptosis, and migration by targeting EZH2. Crit Rev Eukaryot Gene Expr. 2022;32(4):31-40.

    [Crossref] [Google Scholar] [PubMed]

36. Liang Y, Zhao Y, Li L, Wei H, Huang T, Zhang H, et al. MicroRNA profiles in five pairs of early gastric cancer tissues and adjacent non-cancerous tissues. Oncol Lett. 2021;22(2):1-2.

    [Crossref] [Google Scholar] [PubMed]