Identification of RNF150 as the hub gene associated with microsatellite instability in gastric cancer

Identification of RNF150 as the hub gene associated with microsatellite instability in gastric cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification of RNF150 as the hub gene associated with microsatellite instability in gastric cancer Jun Pan, Qingzhi Lan, Shengbao Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2352597/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Aug, 2023 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose Gastric cancer (GC) is a common digestive tract malignancy with the sixth and third global incidence and cancer-related deaths, respectively. Microsatellite instability (MSI), account for one of the molecular subtypes of GC, plays an important role in GC and is affected by a sophisticated network of gene interactions. In this study, we aimed to explore the expression pattern and clinical performance of RNF150 in GC patients. Methods Weighted gene co-expression network analysis (WGCNA) was exploited to single out the vital module and core genes in TCGA database. We applied the protein–protein interaction (PPI) and survival analysis to propose and confirm RNF150 as the hub gene. Finally, we utilized IHC to explore the expression pattern of RNF150 in GC patients. Results The turquoise module was adopted as core module for the sake of its highest correlation coefficient and higher module significance value. With the highest weight correlation and standard correlation, RNF150 was finally selected as the hub gene for following validation. In validation, data obtained from the test sets showed a lower expression of RNF150 in MSI GC compared to microsatellite stability (MSS) GC. Moreover, survival analysis shows that MSI GC patients with a lower RNF150 expression level displayed a longer OS time. In 10 GC patients, compared with normal gastric tissues, the protein level of RNF150 was virtually upregulated in GC tissue. Furthermore, RNF150 protein level was decreased in MSI GC samples compared to MSS GC samples, which is in accordance with results we obtained in database. Conclusions RNF150 was determined and confirmed as a novel biomarker in MSI GC. It is expected to be an auspicious prognostic biomarker for MSI GC patients. Biological sciences/Cancer Health sciences/Oncology Weighted co-expression network RNF150 microsatellite instability gastric cancer biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Gastric cancer (GC) is a typical digestive tract neoplasm with the sixth and third global incidence and cancer-related deaths, respectively[ 1 ]. Despite improvements in the comprehension of etiology and underlying molecular mechanisms, and declines in morbidity and mortality, the burden caused by GC remains high in several area including China[ 2 ]. Although the treatment of gastric cancer has been greatly improved, the survival of advanced GC patients is still less than expected[ 3 , 4 ]. For this reason, more research should be conducted to improve prognosis of these patients. Microsatellite instability (MSI) is a genetic variation as a result of the inheritance and epigenetic inactivation of DNA mismatch repair genes, which is considered to be associated with the occurrence of cancer[ 5 ]. Although the MSI phenotype is present in many types of tumors, it is most common in colorectal cancer, gastric cancer, and endometrial uterine cancer[ 6 ]. In the gastric tumorous tissues, MSI is mainly related to the methylation of MLH1 promoter CpG island, which leads to a decrease in the expression level of MLH1[ 7 ]. According to statistics, MSI was detected in 10–25% of gastric cancer patients, and the rest were microsatellite stability (MSS) [ 8 ]. The MSI GCs displayed distinct biological features from MSS GCs. Several studies have shown that microsatellite instability is associated with good overall survival of gastric cancer patients[ 9 – 13 ]. However, it’s too soon to get a conclusion about the exact role of MSI in GC patients. Karol el at found that stage I-III GC patients with MSI-H showed longer OS time in spite of positive status of margin[ 14 ], while Stefania Beghelli and colleges declared that MSI in GC is linked to superior outcomes only with stage II patients[ 15 ]. Moreover, two studies demonstrated that the detection of microsatellite instability has limited prognostic value in GC[ 16 , 17 ]. Therefore, the underlying mechanism of MSI GC is still unclear and need further research to reveal. Researchers have widely used bioinformatics methods to analyze microarray data for prediction of key genes in various disease[ 18 ]. Weighted gene co-expression network analysis (WGCNA) is an R package utilized for establishing co-expression gene structures, identifying key modules and hub genes in various diseases[ 19 ]. For instance, a recent research used WGCNA for the identification of LRRC26 and REP15 to be associated with long term outcomes of patients diagnosed with colorectal cancer[ 20 ]. Besides, another study demonstrated that NRP1 might serve as potential prognostic biomarkers for GC patients by WGCNA[ 21 ]. In this study, we applied WGCNA and PPI network analysis to discover novel biomarkers associated with microsatellite instability GC patients. Methods GC datasets Human gastric cancer mRNA expression data and clinical characteristic were obtained from the Cancer Genome Atlas (TCGA) ( https://genome-cancer.ucsc.edu/)an d Gene Expression Omnibus (GEO) database( www.ncbi.nlm.nih.gov/geo ). TCGA-STAD, consisting of 121 MSI GC patients and 258 MSS patients, was defined as the training set, while dataset GSE62254, consisting of 68 MSI GC patients and 232 MSS patients, were used as test set. We used the R package named “Affy” for the preprocessing of raw data in the training set. After a series of processes such as RMA background correction, log2 transformation, quantile normalization, and the median-polish probe set summarization. Finally, data quality was checked by sample clustering. DEGs extracting We divided the GC patients into two groups, MSI and MSS. The R package named ‘limma’ was exploited to identify DEGs between those two groups[ 22 ]. |log 2 fold change (FC)| > 0.2 was decided as cutoff value to identify differentially expressed genes. Co-expression network construction Then we used DEGs acquired above to construct co-expression structure using the ‘WGCNA’ package[ 23 ]. Firstly, we estimated the Pearson’s correlation matrices of paired genes. Secondly, we build up a weighted adjacency matrix by the power function a mn = | cmn | β . The parameter β was a soft threshold, which could lay emphasis on solid correlations and weaken powerless correlations between genes. In our research, β = 4 (scale R 2 = 0.88) is determined to establish a scale-free network (Figure.2A-D). Then, the adjacency was adjusted into topological overlap matrix (TOM) that could estimate the network connectivity of every single gene[ 24 ]. Finally, based on the TOM-based measure of similarities and differences, we used the average linkage hierarchical clustering method to decide the minimum genome size of the gene dendrogram to be 30.[ 25 ]. Identification of significant module We applied two methods to determine important modules related with MSI. Linear regression analysis was performed between MSI status and various gene mRNA level. The p-value was then log 10 transformed into the gene significance (GS). Then we computed average gene significance for every single gene in the module to get the module significance. Next, we conducted principal component analysis, and took the major component as module eigengenes (ME). Finally, the key module was defined as with higher gene significance and higher correlation between module eigengenes and microsatellite instability. GO and KEGG analysis of DEGs To examine the underlying mechanism of DEGs in the core module, we applied ‘clusterProfiler’ R package to conduct pathway analysis such as gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then, package ‘ggplot2’ was exploited to visualize the top ten pathways of GO and KEGG analysis. Finally, we exported genes in the key module and exploit the software Cytoscape to establish protein-protein interaction structure. Gene set enrichment analysis (GSEA) In order to explore the underlying mechanism, we divided 128 MSI samples from TCGA database into low RNF150 group and high RNF150 group based on RNF150 mRNA levels. Next, we used GSEA to explore functional pathway analysis and set the cutoff as p-value < 0.05, gene size more than 30, and |enrichment score (ES)| more than 0.6. Hub gene validation We performed survival analysis using the package named ‘survival’. We used test set GSE62254 to explore the expression pattern of the RNF150 between MSI and MSS patients. GraphPad Prism 8 was applied to analyze these data and visualize results. Statistical significance was estimated by two-tailed Student’s t-tests. P value less than 0.05 indicated statistical significance Validation in clinical samples A total of 10 GC patients from Renmin Hospital of Wuhan University were involved in this study. Paraffin Section tissues and their adjacent normal gastric mucosa were obtained. Our research was accepted by the Ethics Committee of Renmin Hospital of Wuhan University (No. WDRY2021-K002). All procedures are performed under the Declaration of Helsinki. All participants signed informed consent to allow their tissues to be used in the present study. Immunohistochemistry (IHC) staining was conducted to explore the protein level of RNF150. Firstly, graded ethanol solutions were used to deparaffinize and rehydrate the tissue sections. Then, citrate buffer with concentration of 10mM and pH of 6.0 were used for antigen retrieval of the sections. Subsequently, 3% H2O2 solution was used to block the activity of endogenous peroxidase. The tissue sections were incubated with RNF150 antibody (#HPA037987, Atlas Antibodies, USA). 3, 3ʹ- diaminobenzidine (DAB) was used to develop the sections and hematoxylin was used to counterstained. Results DEGs extracting Figure 1 displays the flow of this research. Using training set TCGA-STAD, we selected 4679 DEGs (2156 upregulated and 2523 downregulated in MSI samples) for following research with the cutoff of p-value 0.2. Construction of co-expression network and key modules identification We exploited the package named ‘WGCNA’ to calculate 4679 DEGs into modules and 8 modules were visualized with various colors (Figure. 3A). Two approaches were used to select the core module related to microsatellite instability. First, the ME displayed that the turquoise module is the most significantly linked to MSI (r = -0.39, p = 8.07e-16) (Figure. 3C). Furthermore, these data indicated that the turquoise module had an upregulated MS value (Figure. 3B). Thus, the turquoise module was selected as the core module linked to MSI. Then we acquired the gene list from turquoise module for subsequent analysis. For the reason of investigating the mechanism of the genes we obtained from the turquoise module, we conducted pathway analysis. The top ten pathways in GO analysis were shown in Supplementary Figure S1. Among biological processes, "regulation of cell morphogenesis" was the most significant enrichment, and "collagen-containing extracellular matrix" was the most significant enrichment in cellular components, and "cell adhesion molecule binding" was the most significant enrichment in molecular function (Supplementary Figure. S1A–C). Furthermore, our results show that proteoglycans in cancer were the most significantly enrichment in the KEGG pathway analysis. (Supplementary Figure. S1D). Hub gene identification Hub genes are defined as those who have closer linkage with other genes from the same module. Among the turquoise module, we found the MM of 43 genes were higher than 0.875. Then these 43 genes were chosen as hub gene candidates and exported to establish network of PPI using Cytoscape. Results show that 7 hub gene candidates were closely connected to other genes in the network (Figure. 4). Both with the highest weight correlation and standard correlation, RNF150 was finally defined as the core gene for following research. Hub gene validation The RNF150 mRNA level was downregulated in MSI individuals compared to MSS individuals both in the TCGA-STAD and GSE62254 (Figure. 5A). In GSE62254, the RNF150 mRNA level was significantly upregulated among paired adjacent normal tissues than that in tumor tissues in 98 patients (Figure. 5B). As for the Lauren classification of GC, the RNF150 mRNA level in diffuse-type was significantly downregulated compared to intestinal-type (Figure. 5C). According to the degree of microsatellites instability, GC was defined as MSI-High (MSI-H), MSI-Low (MSI-L) and MSS [ 26 ]. While according to the TCGA database, our results indicated that RNF150 mRNA level could definitely discriminate both MSI-H and MSI-L from MSS, but didn’t show obvious effect among MSI-H and MSI-L (Figure. 5D). Furthermore, according to their TNM stage, we categorized GC patients into stage I, stage II, stage III, stage IV. Results show RNF150 mRNA level increased with tumor development (Figure. 5E). Furthermore, we analyzed the linkage between RNF150 mRNA level and the OS time of GC patient in both TCGA database and GSE62254. First of all, we separated all patients into two groups using cutoff value in TCGA and GSE62254, respectively. Results show that patients with a lower RNF150 expression level showed a longer OS time (Figure. 6A-B). Secondly, we separated MSI and MSS patients into two groups based on their RNF150 expression levels as mentioned above, and results remains the same (Figure. 6C-F). Gene set enrichment analysis For the reason of determining the underlying biological mechanism of RNF150 associated with the KEGG pathway in MSI individuals, we conducted gene set enrichment analysis and found that “heparin binding” was enriched (Supplementary Figure. S2A). Besides, PPI network revealed that RNF150 may play important role in the heparin binding pathway through the SLIT2 and RSPO3 gene (Supplementary Figure. S2B). Correlation analysis also showed that in MSI GCs, the RSPO3 mRNA level was mostly related to RNF150 mRNA level (r = 0.8239, Supplementary Figure. S2C). We also analyzed the correlation between RNF150 and four MSI related genes (MLH1, MSH2, MSH6, PMS2) and results show that there is correlation between them (Supplementary Figure. S3). Validation in clinical GC samples To further explore RNF150 protein level in GC samples, we applied the human protein atlas in our research. The results showed that the staining intensity of RNF150 in gastric tumor tissue was significantly lower than that in normal tissue. (Figure.7A-D). For the sake of improving the reliability of our data, we acquired paraffin embedded section of 10 GC patients, in which 5 patients were MSS and 5 patients were MSI. We obtained tumor tissues and paired adjacent normal tissues simultaneously. Then we conducted IHC for RNF150 on tumor sections and paired adjacent normal sections from 5 MSS GC patients and 5 MSI GC patients. The results showed that the staining intensity of RNF150 in gastric tumor tissue was significantly lower than that in normal tissue (Figure.7E-G). Furthermore, RNF150 mRNA level was decreased in MSI individuals compared with MSS individuals, which is in accordance with results we obtained in database (Figure.7E-G). Discussion In the present study, we are committed to identify hub genes involved in MSI in GC. By applying TCGA database and WGCNA, we identified the turquoise as the key module. Furthermore, we performed GO, KEGG and PPI analysis and discovered the RNF150 as the hub gene. Moreover, by cross validation with GEO datasets, we confirmed that mRNA levels of RNF150 were significantly downregulated in GC than gastric normal mucosa, and low mRNA levels of RNF150 predicted longer survival in GC patients. RNF150 is a member of ring finger protein family. Eukaryotic cells contain a huge amount of RNF proteins, most of which function as E3 ubiquitin ligases[ 27 ]. E3 ubiquitin ligases bind E2 to substrates and transfer ubiquitin molecules from E2 to substrates, is one of the three enzymes required to regulate protein ubiquitination. The other two enzymes are E1 ubiquitin-activating enzyme that hydrolyses ATP and E2 ubiquitin-conjugating enzyme that receives the ubiquitin from E1[ 28 ]. E3 ubiquitin ligases play a vital role in sustaining protein stability by ubiquitinating and degrading proteins misfolded [ 29 ]. Besides, E3 ubiquitin ligases also participated in other biological processes, such as cell proliferation, apoptosis, DNA damage repair, and intracellular vesicle trafficking, etc[ 30 – 34 ]. Although there are few literature reports on RNF150, the role of many RING finger E3 ligases in malignant tumors is well known, including the oncogene MDM2 [ 35 , 36 ] and the suppressor gene BRCA1[ 37 , 38 ]. For instance, MDM2 can cause degradation of p53 through its E3 ligase activity[ 39 ]. According to reports, the MDM2/p53 pathway is associated with the development of GC[ 40 , 41 ]. In addition, BRCA1 can encourage DNA adjustment and simultaneously steady p53 through ubiquitination[ 38 , 42 ]. The study shows that the BRCA1 mRNA level is decreased in gastric cancer tissue and decided whether platinum-based chemotherapy had a response [ 43 , 44 ]. We need to mention limitations in this study. Firstly, studies on RNF150 are very limited so it’s difficult for us to hypothesize the potential mechanism of RNF150 affecting gastric cancer cells. Secondly, the prognostic role of RNF150 needs to be further explored in clinical individuals by different experimental methods such as western blotting and immunohistochemistry. Finally, further cell and animal experiments are needed in the future to explain the underlying mechanism of RNF150 in GC patients. In brief, we recognized RNF150 as the vital gene linked to MSI in GC. Lower mRNA level of RNF150 predicts a longer survival of GC patients. Therefore, we propose that RNF150 is a novel biomarker and this study has important clinical implications for the development of new therapies for GC patients. Conclusion In conclusion, our study identified RNF150 as a novel biomarker in MSI GC. It is expected to be an auspicious prognostic biomarker for MSI GC patients. Declarations Ethics Approval Our research was accepted by the Ethics Committee of Renmin Hospital of Wuhan University (No. WDRY2021-K002). Data availability statement The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Funding statement This study is not funded. Author Contributions All authors made substantial contributions to conception and design of this study. J.P. ran the R software and prepared all the figures. Q.L. wrote the main manuscripttext. J.P. and S.L. revised the manuscript. All authors reviewed the manuscript. Disclosure The authors report no conflicts of interest in this work. References C. Global Burden of Disease Cancer, C. Fitzmaurice, D. Abate, N. Abbas. Murray, Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2017: A Systematic Analysis for the Global Burden of Disease Study, JAMA Oncol, (2019). A. Ferro, B. Peleteiro, M. Malvezzi, C. Bosetti, P. Bertuccio, F. Levi, E. Negri, C. La Vecchia, N. Lunet, Worldwide trends in gastric cancer mortality (1980–2011), with predictions to 2015, and incidence by subtype, Eur J Cancer, 50 (2014) 1330–1344. C. Coutzac, S. Pernot, N. Chaput, A. Zaanan, Immunotherapy in advanced gastric cancer, is it the future?, Crit Rev Oncol Hematol, 133 (2019) 25–32. I.H. Ham, D. Lee, H. Hur, Role of Cancer-Associated Fibroblast in Gastric Cancer Progression and Resistance to Treatments, J Oncol, 2019 (2019) 6270784. H. Yamamoto, Y. Adachi, H. Taniguchi, H. Kunimoto, K. Nosho, H. Suzuki, Y. Shinomura, Interrelationship between microsatellite instability and microRNA in gastrointestinal cancer, World J Gastroenterol, 18 (2012) 2745–2755. Y.E. Maruvka, K.W. Mouw, R. Karlic, P. Parasuraman, A. Kamburov, P. Polak, N.J. Haradhvala, J.M. Hess, E. Rheinbay, Y. Brody, A. Koren, L.Z. Braunstein, A. D'Andrea, M.S. Lawrence, A. Bass, A. Bernards, F. Michor, G. Getz, Analysis of somatic microsatellite indels identifies driver events in human tumors, Nat Biotechnol, 35 (2017) 951–959. H. Suzuki, F. Itoh, M. Toyota, T. Kikuchi, H. Kakiuchi, Y. Hinoda, K. Imai, Distinct methylation pattern and microsatellite instability in sporadic gastric cancer, Int J Cancer, 83 (1999) 309–313. M. D'Errico, E. de Rinaldis, M.F. Blasi, V. Viti, M. Falchetti, A. Calcagnile, F. Sera, C. Saieva, L. Ottini, D. Palli, F. Palombo, A. Giuliani, E. Dogliotti, Genome-wide expression profile of sporadic gastric cancers with microsatellite instability, Eur J Cancer, 45 (2009) 461–469. K. Polom, L. Marano, D. Marrelli, R. De Luca, G. Roviello, V. Savelli, P. Tan, F. Roviello, Meta-analysis of microsatellite instability in relation to clinicopathological characteristics and overall survival in gastric cancer, Br J Surg, 105 (2018) 159–167. N. Baniak, J.L. Senger, S. Ahmed, S.C. Kanthan, R. Kanthan, Gastric biomarkers: a global review, World J Surg Oncol, 14 (2016) 212. B. Li, H.Y. Liu, S.H. Guo, P. Sun, F.M. Gong, B.Q. Jia, Microsatellite instability of gastric cancer and precancerous lesions, Int J Clin Exp Med, 8 (2015) 21138–21144. J. Park, H.M. Yoo, W. Jang, S. Shin, M. Kim, Y. Kim, S.W. Lee, J.G. Kim, Distribution of somatic mutations of cancer-related genes according to microsatellite instability status in Korean gastric cancer, Medicine (Baltimore), 96 (2017) e7224. Y.Y. Choi, J.M. Bae, J.Y. An, I.G. Kwon, I. Cho, H.B. Shin, T. Eiji, M. Aburahmah, H.I. Kim, J.H. Cheong, W.J. Hyung, S.H. Noh, Is microsatellite instability a prognostic marker in gastric cancer? A systematic review with meta-analysis, J Surg Oncol, 110 (2014) 129–135. K. Polom, D. Marrelli, E.C. Smyth, C. Voglino, G. Roviello, V. Pascale, J. Varas, C. Vindigni, F. Roviello, The Role of Microsatellite Instability in Positive Margin Gastric Cancer Patients, Surg Innov, 25 (2018) 99–104. S. Beghelli, G. de Manzoni, S. Barbi, A. Tomezzoli, F. Roviello, C. Di Gregorio, C. Vindigni, L. Bortesi, A. Parisi, L. Saragoni, A. Scarpa, P.S. Moore, Microsatellite instability in gastric cancer is associated with better prognosis in only stage II cancers, Surgery, 139 (2006) 347–356. J.D. Hayden, L. Cawkwell, P. Quirke, M.F. Dixon, A.R. Goldstone, H. Sue-Ling, D. Johnston, I.G. Martin, Prognostic significance of microsatellite instability in patients with gastric carcinoma, Eur J Cancer, 33 (1997) 2342–2346. R.O. Perez, C.E. Jacob, F.L. D'Ottaviano, C. Alvarenga, A.S. Ribeiro, U. Ribeiro, Jr., C.J. Bresciani, B. Zilberstein, J.E. Krieger, A. Habr-Gama, J.J. Gama-Rodrigues, Microsatellite instability in solitary and sporadic gastric cancer, Rev Hosp Clin Fac Med Sao Paulo, 59 (2004) 279–285. F. Qian, J. Guo, Z. Jiang, B. Shen, Translational Bioinformatics for Cholangiocarcinoma: Opportunities and Challenges, Int J Biol Sci, 14 (2018) 920–929. P. Langfelder, S. Horvath, WGCNA: an R package for weighted correlation network analysis, BMC Bioinformatics, 9 (2008) 559. X. Xu, C. Gong, Y. Wang, Y. Hu, H. Liu, Z. Fang, Multi-omics analysis to identify driving factors in colorectal cancer, Epigenomics, (2020). G. Wang, B. Shi, Y. Fu, S. Zhao, K. Qu, Q. Guo, K. Li, J. She, Hypomethylated gene NRP1 is co-expressed with PDGFRB and associated with poor overall survival in gastric cancer patients, Biomed Pharmacother, 111 (2019) 1334–1341. I. Diboun, L. Wernisch, C.A. Orengo, M. Koltzenburg, Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma, BMC Genomics, 7 (2006) 252. S. Horvath, J. Dong, Geometric interpretation of gene coexpression network analysis, PLoS Comput Biol, 4 (2008) e1000117. A.M. Yip, S. Horvath, Gene network interconnectedness and the generalized topological overlap measure, BMC Bioinformatics, 8 (2007) 22. E. Ravasz, A.L. Somera, D.A. Mongru, Z.N. Oltvai, A.L. Barabasi, Hierarchical organization of modularity in metabolic networks, Science, 297 (2002) 1551–1555. B.A. 3Rd, A.P. Venook, L. Cederquist, E. Chan, Y.J. Chen, H.S. Cooper, D. Deming, P.F. Engstrom, P.C. Enzinger, A. Fichera, Colon Cancer, Version 1.2017, NCCN Clinical Practice Guidelines in Oncology, Journal of the National Comprehensive Cancer Network Jnccn, 15 (2017) 370–398. S.J. Campbell, R.A. Edwards, C.C. Leung, D. Neculai, C.D. Hodge, S. Dhe-Paganon, J.N. Glover, Molecular insights into the function of RING finger (RNF)-containing proteins hRNF8 and hRNF168 in Ubc13/Mms2-dependent ubiquitylation, J Biol Chem, 287 (2012) 23900–23910. H. Richly, M. Rape, S. Braun, S. Rumpf, C. Hoege, S. Jentsch, A series of ubiquitin binding factors connects CDC48/p97 to substrate multiubiquitylation and proteasomal targeting, Cell, 120 (2005) 73–84. J. Liu, S. Shaik, X. Dai, Q. Wu, X. Zhou, Z. Wang, W. Wei, Targeting the ubiquitin pathway for cancer treatment, Biochim Biophys Acta, 1855 (2015) 50–60. N. Ishida, T. Nakagawa, S.I. Iemura, A. Yasui, H. Shima, Y. Katoh, Y. Nagasawa, T. Natsume, K. Igarashi, K. Nakayama, Ubiquitylation of Ku80 by RNF126 Promotes Completion of Nonhomologous End Joining-Mediated DNA Repair, Mol Cell Biol, 37 (2017). S. Lipkowitz, A.M. Weissman, RINGs of good and evil: RING finger ubiquitin ligases at the crossroads of tumour suppression and oncogenesis, Nat Rev Cancer, 11 (2011) 629–643. J.M. Wymant, S. Hiscox, A.D. Westwell, S. Urbe, M.J. Clague, A.T. Jones, The Role of BCA2 in the Endocytic Trafficking of EGFR and Significance as a Prognostic Biomarker in Cancer, J Cancer, 7 (2016) 2388–2407. S. Yoshino, T. Hara, H.J. Nakaoka, A. Kanamori, Y. Murakami, M. Seiki, T. Sakamoto, The ERK signaling target RNF126 regulates anoikis resistance in cancer cells by changing the mitochondrial metabolic flux, Cell Discov, 2 (2016) 16019. X. Zhi, D. Zhao, Z. Wang, Z. Zhou, C. Wang, W. Chen, R. Liu, C. Chen, E3 ubiquitin ligase RNF126 promotes cancer cell proliferation by targeting the tumor suppressor p21 for ubiquitin-mediated degradation, Cancer Res, 73 (2013) 385–394. W. Wu, C. Xu, X. Ling, C. Fan, B.P. Buckley, M.V. Chernov, L. Ellis, F. Li, I.G. Munoz, X. Wang, Targeting RING domains of Mdm2-MdmX E3 complex activates apoptotic arm of the p53 pathway in leukemia/lymphoma cells, Cell Death Dis, 6 (2015) e2035. S. Fang, J.P. Jensen, R.L. Ludwig, K.H. Vousden, A.M. Weissman, Mdm2 is a RING finger-dependent ubiquitin protein ligase for itself and p53, J Biol Chem, 275 (2000) 8945–8951. M.D. Stewart, E.D. Duncan, E. Coronado, P.A. DaRosa, J.N. Pruneda, P.S. Brzovic, R.E. Klevit, Tuning BRCA1 and BARD1 activity to investigate RING ubiquitin ligase mechanisms, Protein Sci, 26 (2017) 475–483. R.M. Densham, J.R. Morris, The BRCA1 Ubiquitin ligase function sets a new trend for remodelling in DNA repair, Nucleus, 8 (2017) 116–125. S. Wang, Y. Zhao, A. Aguilar, D. Bernard, C.Y. Yang, Targeting the MDM2-p53 Protein-Protein Interaction for New Cancer Therapy: Progress and Challenges, Cold Spring Harb Perspect Med, 7 (2017). D.A. Freedman, A.J. Levine, Nuclear export is required for degradation of endogenous p53 by MDM2 and human papillomavirus E6, Mol Cell Biol, 18 (1998) 7288–7293. T. Gunther, R. Schneider-Stock, C. Hackel, H.U. Kasper, M. Pross, A. Hackelsberger, H. Lippert, A. Roessner, Mdm2 gene amplification in gastric cancer correlation with expression of Mdm2 protein and p53 alterations, Mod Pathol, 13 (2000) 621–626. R. Roy, J. Chun, S.N. Powell, BRCA1 and BRCA2: different roles in a common pathway of genome protection, Nat Rev Cancer, 12 (2011) 68–78. H.S. Kim, I.G. Hwang, H.Y. Min, Y.J. Bang, W.H. Kim, Clinical significance of BRCA1 and BRCA2 mRNA and protein expression in patients with sporadic gastric cancer, Oncol Lett, 17 (2019) 4383–4392. G. Kim, J. Kim, S.Y. Han, I.G. Hwang, H.S. Kim, H. Min, The effects of BRCA1 expression on the chemosensitivity of gastric cancer cells to platinum agents, Oncol Lett, 17 (2019) 5023–5029. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures.docx Cite Share Download PDF Status: Published Journal Publication published 01 Aug, 2023 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 01 Apr, 2023 Reviews received at journal 31 Mar, 2023 Reviewers agreed at journal 17 Mar, 2023 Reviews received at journal 06 Feb, 2023 Reviewers agreed at journal 24 Jan, 2023 Reviewers invited by journal 24 Jan, 2023 Editor assigned by journal 24 Jan, 2023 Editor invited by journal 17 Dec, 2022 Submission checks completed at journal 17 Dec, 2022 First submitted to journal 07 Dec, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2352597","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":160884956,"identity":"9a59d988-4bc6-49da-86c0-a344ed0f4c19","order_by":0,"name":"Jun Pan","email":"","orcid":"","institution":"Hubei University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Pan","suffix":""},{"id":160884960,"identity":"98cc0550-1f79-4b54-b02d-424c78d1a36d","order_by":1,"name":"Qingzhi Lan","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Qingzhi","middleName":"","lastName":"Lan","suffix":""},{"id":160884962,"identity":"ca895350-4781-433b-9846-1f3aa7b19b71","order_by":2,"name":"Shengbao Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYPACCx4GCSDF2MDAwM/MfPgBAeUgdRIILZLtbGkGxGhhgGsxOM+jIIFPvbz78eePKyokZORnNx97+HWHnZzxYR4GA4Yam2hcWgzP5Bg2njkjwWNw51i6seyZZGOzw7wHHjAcS8ttwKWlIYexsbENqEUix0xaso05cdthvgQDxobDuLX0P38I1iI/I/8bUEt94uZmoHZ8WuQlEgzBWhhu5LBJfmw7nLiBmYAWA4k3hjMbQH65kWYmzXjmuLHEYWAgJ+Dxi3x/+oOPDRU29vIzkp9J/txRLcfff/jwgw81NrhtOYDEYeaBsRJwKAfbgmwW4w88KkfBKBgFo2DkAgCXplnslvI+uQAAAABJRU5ErkJggg==","orcid":"","institution":"Hubei University of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Shengbao","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2022-12-07 06:59:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2352597/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2352597/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-023-39255-7","type":"published","date":"2023-08-01T21:49:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":30592062,"identity":"1fd4ac12-5c34-4316-ad5d-6fcba7a25315","added_by":"auto","created_at":"2022-12-20 22:11:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13088,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow diagram of this study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2352597/v1/e853613b1c6a5d2a649cb673.png"},{"id":30592354,"identity":"a8df09e0-452b-484b-8c79-17d0e2745f04","added_by":"auto","created_at":"2022-12-20 22:19:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28516,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalculation of soft threshold power in the WGCNA.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A). Analysis of the scale-free fit index for various soft threshold powers.\u003c/p\u003e\n\u003cp\u003e(B). Analysis of the mean connectivity for various soft threshold powers.\u003c/p\u003e\n\u003cp\u003e(C). Histogram of connectivity distribution (β = 4).\u003cbr\u003e\n(D). Checking the scale-free topology (β = 4).\u003c/p\u003e\n\u003cp\u003ek: Connectivity; p(k): Possibility of the connectivity. WGCNA, weighted gene co-expression network analysis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2352597/v1/c738c5067802e931d003e374.png"},{"id":30592063,"identity":"eefcbdcd-4e6c-43df-829d-ca186c14a980","added_by":"auto","created_at":"2022-12-20 22:11:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48578,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of modules associated with MSI.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A). Dendrogram of all DEGs clustered based on a dissimilarity measure (1-TOM).\u003c/p\u003e\n\u003cp\u003e(B). Distribution of average gene significance and errors in the modules associated with MSI in GC.\u003c/p\u003e\n\u003cp\u003e(C). Heatmap of the correlation between module eigengenes and MSI.\u003c/p\u003e\n\u003cp\u003eGC, gastric cancer; TOM, topological overlap matrix.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2352597/v1/2ae05d055304874686c8b7fd.png"},{"id":30592065,"identity":"e6165b68-039d-4641-82bf-a05ce560ca6e","added_by":"auto","created_at":"2022-12-20 22:11:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45691,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI network of genes in the turquoise module.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eForty-three genes in the turquoise module were exported to construct a PPI network by Cytoscape. Each node size was proportional to the degree of connectivity in the weighted gene co-expression network.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2352597/v1/5f9d98d5622a93dcaf3503df.png"},{"id":30592355,"identity":"74de8569-8817-40b1-99cb-0ecd5f1e1191","added_by":"auto","created_at":"2022-12-20 22:19:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":21180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHub gene validation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A). RNF150 expression was downregulated in the MSI GCs compared with MSS GCs according to the TCGA and GSE62254 datasets.\u003c/p\u003e\n\u003cp\u003e(B). RNF150 expression was downregulated in tumorous tissues compared with paired adjacent normal tissues in 98 patients in GSE62254.\u003c/p\u003e\n\u003cp\u003e(C). RNF150 expression was downregulated in intestinal type tumorous tissues compared with diffuse type tumorous tissues in GSE62254.\u003c/p\u003e\n\u003cp\u003e(D). The expression of RNF150 in patients with MSS, MSI-Low and MSI-High in the TCGA database.\u003c/p\u003e\n\u003cp\u003e(E). RNF150 expression levels in the different TNM stage of GC patients in GSE62254. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001, ns, not statistically significant; TCGA, The Cancer Genome Atlas; GCs, gastric cancers; MSI: microsatellite instability; MSS microsatellite stability.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2352597/v1/a9ffb05e18173658a3e38fbd.png"},{"id":30592067,"identity":"a9da0840-9b7e-4884-83c3-4fcbb8644ea1","added_by":"auto","created_at":"2022-12-20 22:11:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":47234,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHub gene validation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A). Overall survival analysis of RNF150 with GC patients in GSEE62254.\u003c/p\u003e\n\u003cp\u003e(B). Overall survival analysis of RNF150 with GC patients in TCGA database.\u003c/p\u003e\n\u003cp\u003e(C). Overall survival analysis of RNF150 with MSI GC patients in GSEE62254.\u003c/p\u003e\n\u003cp\u003e(D). Overall survival analysis of RNF150 with MSI GC patients in TCGA database.\u003c/p\u003e\n\u003cp\u003e(E). Overall survival analysis of RNF150 with MSS GC patients in GSEE62254.\u003c/p\u003e\n\u003cp\u003e(F). Overall survival analysis of RNF150 with MSS GC patients in TCGA database.\u003c/p\u003e\n\u003cp\u003eTCGA, The Cancer Genome Atlas; GC, gastric cancer; MSI: microsatellite instability; MSS microsatellite stability.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2352597/v1/e9d0789f1e5b81642433e294.png"},{"id":30592356,"identity":"5c2b3519-5e00-4afe-99d1-1308a179b28b","added_by":"auto","created_at":"2022-12-20 22:19:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":99464,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRNF150 protein level in GC samples.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B). Expression of RNF150 protein in normal gastric tissues from HPA database.\u003c/p\u003e\n\u003cp\u003e(C-D). Expression of RNF150 protein in gastric cancer tissues from HPA database.\u003c/p\u003e\n\u003cp\u003e(E). Expression of RNF150 protein in normal gastric tissues.\u003c/p\u003e\n\u003cp\u003e(F). Expression of RNF150 protein in MSS GC tissues.\u003c/p\u003e\n\u003cp\u003e(G). Expression of RNF150 protein in MSI GC tissues.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-2352597/v1/3df0cc49927e9bb0bc3a567d.png"},{"id":44735377,"identity":"7ee861d4-94a5-4644-afd4-e99bfb6ce086","added_by":"auto","created_at":"2023-10-16 22:24:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1492317,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2352597/v1/142526fe-eea7-4f9e-9b01-0827d25b1329.pdf"},{"id":30592068,"identity":"a5628455-2910-4e47-a2de-5eab71d864ed","added_by":"auto","created_at":"2022-12-20 22:11:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":902359,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-2352597/v1/02ee2ea0d10d934d7b8a1675.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of RNF150 as the hub gene associated with microsatellite instability in gastric cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) is a typical digestive tract neoplasm with the sixth and third global incidence and cancer-related deaths, respectively[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite improvements in the comprehension of etiology and underlying molecular mechanisms, and declines in morbidity and mortality, the burden caused by GC remains high in several area including China[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although the treatment of gastric cancer has been greatly improved, the survival of advanced GC patients is still less than expected[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For this reason, more research should be conducted to improve prognosis of these patients.\u003c/p\u003e \u003cp\u003eMicrosatellite instability (MSI) is a genetic variation as a result of the inheritance and epigenetic inactivation of DNA mismatch repair genes, which is considered to be associated with the occurrence of cancer[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although the MSI phenotype is present in many types of tumors, it is most common in colorectal cancer, gastric cancer, and endometrial uterine cancer[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In the gastric tumorous tissues, MSI is mainly related to the methylation of MLH1 promoter CpG island, which leads to a decrease in the expression level of MLH1[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. According to statistics, MSI was detected in 10\u0026ndash;25% of gastric cancer patients, and the rest were microsatellite stability (MSS) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The MSI GCs displayed distinct biological features from MSS GCs. Several studies have shown that microsatellite instability is associated with good overall survival of gastric cancer patients[\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, it\u0026rsquo;s too soon to get a conclusion about the exact role of MSI in GC patients. Karol el at found that stage I-III GC patients with MSI-H showed longer OS time in spite of positive status of margin[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], while Stefania Beghelli and colleges declared that MSI in GC is linked to superior outcomes only with stage II patients[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, two studies demonstrated that the detection of microsatellite instability has limited prognostic value in GC[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Therefore, the underlying mechanism of MSI GC is still unclear and need further research to reveal.\u003c/p\u003e \u003cp\u003eResearchers have widely used bioinformatics methods to analyze microarray data for prediction of key genes in various disease[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Weighted gene co-expression network analysis (WGCNA) is an R package utilized for establishing co-expression gene structures, identifying key modules and hub genes in various diseases[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For instance, a recent research used WGCNA for the identification of LRRC26 and REP15 to be associated with long term outcomes of patients diagnosed with colorectal cancer[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Besides, another study demonstrated that NRP1 might serve as potential prognostic biomarkers for GC patients by WGCNA[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this study, we applied WGCNA and PPI network analysis to discover novel biomarkers associated with microsatellite instability GC patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGC datasets\u003c/h2\u003e \u003cp\u003eHuman gastric cancer mRNA expression data and clinical characteristic were obtained from the Cancer Genome Atlas (TCGA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genome-cancer.ucsc.edu/)an\u003c/span\u003e\u003cspan address=\"https://genome-cancer.ucsc.edu/)an\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ed Gene Expression Omnibus (GEO) database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ncbi.nlm.nih.gov/geo\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). TCGA-STAD, consisting of 121 MSI GC patients and 258 MSS patients, was defined as the training set, while dataset GSE62254, consisting of 68 MSI GC patients and 232 MSS patients, were used as test set.\u003c/p\u003e \u003cp\u003eWe used the R package named \u0026ldquo;Affy\u0026rdquo; for the preprocessing of raw data in the training set. After a series of processes such as RMA background correction, log2 transformation, quantile normalization, and the median-polish probe set summarization. Finally, data quality was checked by sample clustering.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDEGs extracting\u003c/h2\u003e \u003cp\u003eWe divided the GC patients into two groups, MSI and MSS. The R package named \u0026lsquo;limma\u0026rsquo; was exploited to identify DEGs between those two groups[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. |log\u003csub\u003e2\u003c/sub\u003e fold change (FC)| \u0026gt; 0.2 was decided as cutoff value to identify differentially expressed genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCo-expression network construction\u003c/h2\u003e \u003cp\u003eThen we used DEGs acquired above to construct co-expression structure using the \u0026lsquo;WGCNA\u0026rsquo; package[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Firstly, we estimated the Pearson\u0026rsquo;s correlation matrices of paired genes. Secondly, we build up a weighted adjacency matrix by the power function a\u003csub\u003emn\u003c/sub\u003e = \u003cb\u003e|\u003c/b\u003ecmn\u003cb\u003e|\u003c/b\u003e\u003csup\u003eβ\u003c/sup\u003e. The parameter β was a soft threshold, which could lay emphasis on solid correlations and weaken powerless correlations between genes. In our research, β\u0026thinsp;=\u0026thinsp;4 (scale R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.88) is determined to establish a scale-free network (Figure.2A-D). Then, the adjacency was adjusted into topological overlap matrix (TOM) that could estimate the network connectivity of every single gene[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Finally, based on the TOM-based measure of similarities and differences, we used the average linkage hierarchical clustering method to decide the minimum genome size of the gene dendrogram to be 30.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of significant module\u003c/h2\u003e \u003cp\u003eWe applied two methods to determine important modules related with MSI. Linear regression analysis was performed between MSI status and various gene mRNA level. The p-value was then log\u003csub\u003e10\u003c/sub\u003e transformed into the gene significance (GS). Then we computed average gene significance for every single gene in the module to get the module significance. Next, we conducted principal component analysis, and took the major component as module eigengenes (ME). Finally, the key module was defined as with higher gene significance and higher correlation between module eigengenes and microsatellite instability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGO and KEGG analysis of DEGs\u003c/h2\u003e \u003cp\u003eTo examine the underlying mechanism of DEGs in the core module, we applied \u0026lsquo;clusterProfiler\u0026rsquo; R package to conduct pathway analysis such as gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then, package \u0026lsquo;ggplot2\u0026rsquo; was exploited to visualize the top ten pathways of GO and KEGG analysis. Finally, we exported genes in the key module and exploit the software Cytoscape to establish protein-protein interaction structure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene set enrichment analysis (GSEA)\u003c/h2\u003e \u003cp\u003eIn order to explore the underlying mechanism, we divided 128 MSI samples from TCGA database into low RNF150 group and high RNF150 group based on RNF150 mRNA levels. Next, we used GSEA to explore functional pathway analysis and set the cutoff as p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, gene size more than 30, and |enrichment score (ES)| more than 0.6.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eHub gene validation\u003c/h2\u003e \u003cp\u003eWe performed survival analysis using the package named \u0026lsquo;survival\u0026rsquo;. We used test set GSE62254 to explore the expression pattern of the RNF150 between MSI and MSS patients. GraphPad Prism 8 was applied to analyze these data and visualize results. Statistical significance was estimated by two-tailed Student\u0026rsquo;s t-tests. P value less than 0.05 indicated statistical significance\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eValidation in clinical samples\u003c/h2\u003e \u003cp\u003eA total of 10 GC patients from Renmin Hospital of Wuhan University were involved in this study. Paraffin Section tissues and their adjacent normal gastric mucosa were obtained. Our research was accepted by the Ethics Committee of Renmin Hospital of Wuhan University (No. WDRY2021-K002). All procedures are performed under the Declaration of Helsinki. All participants signed informed consent to allow their tissues to be used in the present study.\u003c/p\u003e \u003cp\u003eImmunohistochemistry (IHC) staining was conducted to explore the protein level of RNF150. Firstly, graded ethanol solutions were used to deparaffinize and rehydrate the tissue sections. Then, citrate buffer with concentration of 10mM and pH of 6.0 were used for antigen retrieval of the sections. Subsequently, 3% H2O2 solution was used to block the activity of endogenous peroxidase. The tissue sections were incubated with RNF150 antibody (#HPA037987, Atlas Antibodies, USA). 3, 3ʹ- diaminobenzidine (DAB) was used to develop the sections and hematoxylin was used to counterstained.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDEGs extracting\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the flow of this research. Using training set TCGA-STAD, we selected 4679 DEGs (2156 upregulated and 2523 downregulated in MSI samples) for following research with the cutoff of \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cb\u003e|\u003c/b\u003elog\u003csub\u003e2\u003c/sub\u003eFC\u003cb\u003e|\u003c/b\u003e \u0026gt; 0.2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of co-expression network and key modules identification\u003c/h2\u003e \u003cp\u003eWe exploited the package named \u0026lsquo;WGCNA\u0026rsquo; to calculate 4679 DEGs into modules and 8 modules were visualized with various colors (Figure. 3A). Two approaches were used to select the core module related to microsatellite instability. First, the ME displayed that the turquoise module is the most significantly linked to MSI (r = -0.39, p\u0026thinsp;=\u0026thinsp;8.07e-16) (Figure. 3C). Furthermore, these data indicated that the turquoise module had an upregulated MS value (Figure. 3B). Thus, the turquoise module was selected as the core module linked to MSI. Then we acquired the gene list from turquoise module for subsequent analysis.\u003c/p\u003e \u003cp\u003eFor the reason of investigating the mechanism of the genes we obtained from the turquoise module, we conducted pathway analysis. The top ten pathways in GO analysis were shown in Supplementary Figure S1. Among biological processes, \"regulation of cell morphogenesis\" was the most significant enrichment, and \"collagen-containing extracellular matrix\" was the most significant enrichment in cellular components, and \"cell adhesion molecule binding\" was the most significant enrichment in molecular function (Supplementary Figure. S1A\u0026ndash;C). Furthermore, our results show that proteoglycans in cancer were the most significantly enrichment in the KEGG pathway analysis. (Supplementary Figure. S1D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eHub gene identification\u003c/h2\u003e \u003cp\u003eHub genes are defined as those who have closer linkage with other genes from the same module. Among the turquoise module, we found the MM of 43 genes were higher than 0.875. Then these 43 genes were chosen as hub gene candidates and exported to establish network of PPI using Cytoscape. Results show that 7 hub gene candidates were closely connected to other genes in the network (Figure. 4). Both with the highest weight correlation and standard correlation, RNF150 was finally defined as the core gene for following research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHub gene validation\u003c/h2\u003e \u003cp\u003eThe RNF150 mRNA level was downregulated in MSI individuals compared to MSS individuals both in the TCGA-STAD and GSE62254 (Figure. 5A). In GSE62254, the RNF150 mRNA level was significantly upregulated among paired adjacent normal tissues than that in tumor tissues in 98 patients (Figure. 5B). As for the Lauren classification of GC, the RNF150 mRNA level in diffuse-type was significantly downregulated compared to intestinal-type (Figure. 5C). According to the degree of microsatellites instability, GC was defined as MSI-High (MSI-H), MSI-Low (MSI-L) and MSS [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. While according to the TCGA database, our results indicated that RNF150 mRNA level could definitely discriminate both MSI-H and MSI-L from MSS, but didn\u0026rsquo;t show obvious effect among MSI-H and MSI-L (Figure. 5D). Furthermore, according to their TNM stage, we categorized GC patients into stage I, stage II, stage III, stage IV. Results show RNF150 mRNA level increased with tumor development (Figure. 5E).\u003c/p\u003e \u003cp\u003eFurthermore, we analyzed the linkage between RNF150 mRNA level and the OS time of GC patient in both TCGA database and GSE62254. First of all, we separated all patients into two groups using cutoff value in TCGA and GSE62254, respectively. Results show that patients with a lower RNF150 expression level showed a longer OS time (Figure. 6A-B). Secondly, we separated MSI and MSS patients into two groups based on their RNF150 expression levels as mentioned above, and results remains the same (Figure. 6C-F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGene set enrichment analysis\u003c/h2\u003e \u003cp\u003eFor the reason of determining the underlying biological mechanism of RNF150 associated with the KEGG pathway in MSI individuals, we conducted gene set enrichment analysis and found that \u0026ldquo;heparin binding\u0026rdquo; was enriched (Supplementary Figure. S2A). Besides, PPI network revealed that RNF150 may play important role in the heparin binding pathway through the SLIT2 and RSPO3 gene (Supplementary Figure. S2B). Correlation analysis also showed that in MSI GCs, the RSPO3 mRNA level was mostly related to RNF150 mRNA level (r\u0026thinsp;=\u0026thinsp;0.8239, Supplementary Figure. S2C). We also analyzed the correlation between RNF150 and four MSI related genes (MLH1, MSH2, MSH6, PMS2) and results show that there is correlation between them (Supplementary Figure. S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eValidation in clinical GC samples\u003c/h2\u003e \u003cp\u003eTo further explore RNF150 protein level in GC samples, we applied the human protein atlas in our research. The results showed that the staining intensity of RNF150 in gastric tumor tissue was significantly lower than that in normal tissue. (Figure.7A-D). For the sake of improving the reliability of our data, we acquired paraffin embedded section of 10 GC patients, in which 5 patients were MSS and 5 patients were MSI. We obtained tumor tissues and paired adjacent normal tissues simultaneously. Then we conducted IHC for RNF150 on tumor sections and paired adjacent normal sections from 5 MSS GC patients and 5 MSI GC patients. The results showed that the staining intensity of RNF150 in gastric tumor tissue was significantly lower than that in normal tissue (Figure.7E-G). Furthermore, RNF150 mRNA level was decreased in MSI individuals compared with MSS individuals, which is in accordance with results we obtained in database (Figure.7E-G).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we are committed to identify hub genes involved in MSI in GC. By applying TCGA database and WGCNA, we identified the turquoise as the key module. Furthermore, we performed GO, KEGG and PPI analysis and discovered the RNF150 as the hub gene. Moreover, by cross validation with GEO datasets, we confirmed that mRNA levels of RNF150 were significantly downregulated in GC than gastric normal mucosa, and low mRNA levels of RNF150 predicted longer survival in GC patients.\u003c/p\u003e \u003cp\u003eRNF150 is a member of ring finger protein family. Eukaryotic cells contain a huge amount of RNF proteins, most of which function as E3 ubiquitin ligases[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. E3 ubiquitin ligases bind E2 to substrates and transfer ubiquitin molecules from E2 to substrates, is one of the three enzymes required to regulate protein ubiquitination. The other two enzymes are E1 ubiquitin-activating enzyme that hydrolyses ATP and E2 ubiquitin-conjugating enzyme that receives the ubiquitin from E1[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. E3 ubiquitin ligases play a vital role in sustaining protein stability by ubiquitinating and degrading proteins misfolded [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Besides, E3 ubiquitin ligases also participated in other biological processes, such as cell proliferation, apoptosis, DNA damage repair, and intracellular vesicle trafficking, etc[\u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Although there are few literature reports on RNF150, the role of many RING finger E3 ligases in malignant tumors is well known, including the oncogene MDM2 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and the suppressor gene BRCA1[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. For instance, MDM2 can cause degradation of p53 through its E3 ligase activity[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. According to reports, the MDM2/p53 pathway is associated with the development of GC[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In addition, BRCA1 can encourage DNA adjustment and simultaneously steady p53 through ubiquitination[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The study shows that the BRCA1 mRNA level is decreased in gastric cancer tissue and decided whether platinum-based chemotherapy had a response [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe need to mention limitations in this study. Firstly, studies on RNF150 are very limited so it\u0026rsquo;s difficult for us to hypothesize the potential mechanism of RNF150 affecting gastric cancer cells. Secondly, the prognostic role of RNF150 needs to be further explored in clinical individuals by different experimental methods such as western blotting and immunohistochemistry. Finally, further cell and animal experiments are needed in the future to explain the underlying mechanism of RNF150 in GC patients.\u003c/p\u003e \u003cp\u003eIn brief, we recognized RNF150 as the vital gene linked to MSI in GC. Lower mRNA level of RNF150 predicts a longer survival of GC patients. Therefore, we propose that RNF150 is a novel biomarker and this study has important clinical implications for the development of new therapies for GC patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study identified RNF150 as a novel biomarker in MSI GC. It is expected to be an auspicious prognostic biomarker for MSI GC patients.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur research was accepted by the Ethics Committee of Renmin Hospital of Wuhan University (No. WDRY2021-K002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is not funded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made substantial contributions to conception and design of this study. J.P. ran the R software and prepared all the figures. Q.L. wrote the main manuscripttext. J.P. and S.L. revised the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no conflicts of interest in this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eC. Global Burden of Disease Cancer, C. Fitzmaurice, D. Abate, N. Abbas. Murray, Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2017: A Systematic Analysis for the Global Burden of Disease Study, JAMA Oncol, (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Ferro, B. Peleteiro, M. Malvezzi, C. Bosetti, P. Bertuccio, F. Levi, E. Negri, C. La Vecchia, N. Lunet, Worldwide trends in gastric cancer mortality (1980\u0026ndash;2011), with predictions to 2015, and incidence by subtype, Eur J Cancer, 50 (2014) 1330\u0026ndash;1344.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. Coutzac, S. Pernot, N. Chaput, A. Zaanan, Immunotherapy in advanced gastric cancer, is it the future?, Crit Rev Oncol Hematol, 133 (2019) 25\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eI.H. Ham, D. Lee, H. Hur, Role of Cancer-Associated Fibroblast in Gastric Cancer Progression and Resistance to Treatments, J Oncol, 2019 (2019) 6270784.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Yamamoto, Y. Adachi, H. Taniguchi, H. Kunimoto, K. Nosho, H. Suzuki, Y. Shinomura, Interrelationship between microsatellite instability and microRNA in gastrointestinal cancer, World J Gastroenterol, 18 (2012) 2745\u0026ndash;2755.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY.E. Maruvka, K.W. Mouw, R. Karlic, P. Parasuraman, A. Kamburov, P. Polak, N.J. Haradhvala, J.M. Hess, E. Rheinbay, Y. Brody, A. Koren, L.Z. Braunstein, A. D'Andrea, M.S. Lawrence, A. Bass, A. Bernards, F. Michor, G. Getz, Analysis of somatic microsatellite indels identifies driver events in human tumors, Nat Biotechnol, 35 (2017) 951\u0026ndash;959.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Suzuki, F. Itoh, M. Toyota, T. Kikuchi, H. Kakiuchi, Y. Hinoda, K. Imai, Distinct methylation pattern and microsatellite instability in sporadic gastric cancer, Int J Cancer, 83 (1999) 309\u0026ndash;313.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. D'Errico, E. de Rinaldis, M.F. Blasi, V. Viti, M. Falchetti, A. Calcagnile, F. Sera, C. Saieva, L. Ottini, D. Palli, F. Palombo, A. Giuliani, E. Dogliotti, Genome-wide expression profile of sporadic gastric cancers with microsatellite instability, Eur J Cancer, 45 (2009) 461\u0026ndash;469.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. Polom, L. Marano, D. Marrelli, R. De Luca, G. Roviello, V. Savelli, P. Tan, F. Roviello, Meta-analysis of microsatellite instability in relation to clinicopathological characteristics and overall survival in gastric cancer, Br J Surg, 105 (2018) 159\u0026ndash;167.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN. Baniak, J.L. Senger, S. Ahmed, S.C. Kanthan, R. Kanthan, Gastric biomarkers: a global review, World J Surg Oncol, 14 (2016) 212.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB. Li, H.Y. Liu, S.H. Guo, P. Sun, F.M. Gong, B.Q. Jia, Microsatellite instability of gastric cancer and precancerous lesions, Int J Clin Exp Med, 8 (2015) 21138\u0026ndash;21144.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Park, H.M. Yoo, W. Jang, S. Shin, M. Kim, Y. Kim, S.W. Lee, J.G. Kim, Distribution of somatic mutations of cancer-related genes according to microsatellite instability status in Korean gastric cancer, Medicine (Baltimore), 96 (2017) e7224.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY.Y. Choi, J.M. Bae, J.Y. An, I.G. Kwon, I. Cho, H.B. Shin, T. Eiji, M. Aburahmah, H.I. Kim, J.H. Cheong, W.J. Hyung, S.H. Noh, Is microsatellite instability a prognostic marker in gastric cancer? A systematic review with meta-analysis, J Surg Oncol, 110 (2014) 129\u0026ndash;135.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. Polom, D. Marrelli, E.C. Smyth, C. Voglino, G. Roviello, V. Pascale, J. Varas, C. Vindigni, F. Roviello, The Role of Microsatellite Instability in Positive Margin Gastric Cancer Patients, Surg Innov, 25 (2018) 99\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Beghelli, G. de Manzoni, S. Barbi, A. Tomezzoli, F. Roviello, C. Di Gregorio, C. Vindigni, L. Bortesi, A. Parisi, L. Saragoni, A. Scarpa, P.S. Moore, Microsatellite instability in gastric cancer is associated with better prognosis in only stage II cancers, Surgery, 139 (2006) 347\u0026ndash;356.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ.D. Hayden, L. Cawkwell, P. Quirke, M.F. Dixon, A.R. Goldstone, H. Sue-Ling, D. Johnston, I.G. Martin, Prognostic significance of microsatellite instability in patients with gastric carcinoma, Eur J Cancer, 33 (1997) 2342\u0026ndash;2346.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR.O. Perez, C.E. Jacob, F.L. D'Ottaviano, C. Alvarenga, A.S. Ribeiro, U. Ribeiro, Jr., C.J. Bresciani, B. Zilberstein, J.E. Krieger, A. Habr-Gama, J.J. Gama-Rodrigues, Microsatellite instability in solitary and sporadic gastric cancer, Rev Hosp Clin Fac Med Sao Paulo, 59 (2004) 279\u0026ndash;285.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eF. Qian, J. Guo, Z. Jiang, B. Shen, Translational Bioinformatics for Cholangiocarcinoma: Opportunities and Challenges, Int J Biol Sci, 14 (2018) 920\u0026ndash;929.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP. Langfelder, S. Horvath, WGCNA: an R package for weighted correlation network analysis, BMC Bioinformatics, 9 (2008) 559.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eX. Xu, C. Gong, Y. Wang, Y. Hu, H. Liu, Z. Fang, Multi-omics analysis to identify driving factors in colorectal cancer, Epigenomics, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG. Wang, B. Shi, Y. Fu, S. Zhao, K. Qu, Q. Guo, K. Li, J. She, Hypomethylated gene NRP1 is co-expressed with PDGFRB and associated with poor overall survival in gastric cancer patients, Biomed Pharmacother, 111 (2019) 1334\u0026ndash;1341.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eI. Diboun, L. Wernisch, C.A. Orengo, M. Koltzenburg, Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma, BMC Genomics, 7 (2006) 252.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Horvath, J. Dong, Geometric interpretation of gene coexpression network analysis, PLoS Comput Biol, 4 (2008) e1000117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA.M. Yip, S. Horvath, Gene network interconnectedness and the generalized topological overlap measure, BMC Bioinformatics, 8 (2007) 22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eE. Ravasz, A.L. Somera, D.A. Mongru, Z.N. Oltvai, A.L. Barabasi, Hierarchical organization of modularity in metabolic networks, Science, 297 (2002) 1551\u0026ndash;1555.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB.A. 3Rd, A.P. Venook, L. Cederquist, E. Chan, Y.J. Chen, H.S. Cooper, D. Deming, P.F. Engstrom, P.C. Enzinger, A. Fichera, Colon Cancer, Version 1.2017, NCCN Clinical Practice Guidelines in Oncology, Journal of the National Comprehensive Cancer Network Jnccn, 15 (2017) 370\u0026ndash;398.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS.J. Campbell, R.A. Edwards, C.C. Leung, D. Neculai, C.D. Hodge, S. Dhe-Paganon, J.N. Glover, Molecular insights into the function of RING finger (RNF)-containing proteins hRNF8 and hRNF168 in Ubc13/Mms2-dependent ubiquitylation, J Biol Chem, 287 (2012) 23900\u0026ndash;23910.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Richly, M. Rape, S. Braun, S. Rumpf, C. Hoege, S. Jentsch, A series of ubiquitin binding factors connects CDC48/p97 to substrate multiubiquitylation and proteasomal targeting, Cell, 120 (2005) 73\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Liu, S. Shaik, X. Dai, Q. Wu, X. Zhou, Z. Wang, W. Wei, Targeting the ubiquitin pathway for cancer treatment, Biochim Biophys Acta, 1855 (2015) 50\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN. Ishida, T. Nakagawa, S.I. Iemura, A. Yasui, H. Shima, Y. Katoh, Y. Nagasawa, T. Natsume, K. Igarashi, K. Nakayama, Ubiquitylation of Ku80 by RNF126 Promotes Completion of Nonhomologous End Joining-Mediated DNA Repair, Mol Cell Biol, 37 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Lipkowitz, A.M. Weissman, RINGs of good and evil: RING finger ubiquitin ligases at the crossroads of tumour suppression and oncogenesis, Nat Rev Cancer, 11 (2011) 629\u0026ndash;643.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ.M. Wymant, S. Hiscox, A.D. Westwell, S. Urbe, M.J. Clague, A.T. Jones, The Role of BCA2 in the Endocytic Trafficking of EGFR and Significance as a Prognostic Biomarker in Cancer, J Cancer, 7 (2016) 2388\u0026ndash;2407.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Yoshino, T. Hara, H.J. Nakaoka, A. Kanamori, Y. Murakami, M. Seiki, T. Sakamoto, The ERK signaling target RNF126 regulates anoikis resistance in cancer cells by changing the mitochondrial metabolic flux, Cell Discov, 2 (2016) 16019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eX. Zhi, D. Zhao, Z. Wang, Z. Zhou, C. Wang, W. Chen, R. Liu, C. Chen, E3 ubiquitin ligase RNF126 promotes cancer cell proliferation by targeting the tumor suppressor p21 for ubiquitin-mediated degradation, Cancer Res, 73 (2013) 385\u0026ndash;394.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eW. Wu, C. Xu, X. Ling, C. Fan, B.P. Buckley, M.V. Chernov, L. Ellis, F. Li, I.G. Munoz, X. Wang, Targeting RING domains of Mdm2-MdmX E3 complex activates apoptotic arm of the p53 pathway in leukemia/lymphoma cells, Cell Death Dis, 6 (2015) e2035.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Fang, J.P. Jensen, R.L. Ludwig, K.H. Vousden, A.M. Weissman, Mdm2 is a RING finger-dependent ubiquitin protein ligase for itself and p53, J Biol Chem, 275 (2000) 8945\u0026ndash;8951.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM.D. Stewart, E.D. Duncan, E. Coronado, P.A. DaRosa, J.N. Pruneda, P.S. Brzovic, R.E. Klevit, Tuning BRCA1 and BARD1 activity to investigate RING ubiquitin ligase mechanisms, Protein Sci, 26 (2017) 475\u0026ndash;483.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR.M. Densham, J.R. Morris, The BRCA1 Ubiquitin ligase function sets a new trend for remodelling in DNA repair, Nucleus, 8 (2017) 116\u0026ndash;125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Wang, Y. Zhao, A. Aguilar, D. Bernard, C.Y. Yang, Targeting the MDM2-p53 Protein-Protein Interaction for New Cancer Therapy: Progress and Challenges, Cold Spring Harb Perspect Med, 7 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD.A. Freedman, A.J. Levine, Nuclear export is required for degradation of endogenous p53 by MDM2 and human papillomavirus E6, Mol Cell Biol, 18 (1998) 7288\u0026ndash;7293.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT. Gunther, R. Schneider-Stock, C. Hackel, H.U. Kasper, M. Pross, A. Hackelsberger, H. Lippert, A. Roessner, Mdm2 gene amplification in gastric cancer correlation with expression of Mdm2 protein and p53 alterations, Mod Pathol, 13 (2000) 621\u0026ndash;626.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR. Roy, J. Chun, S.N. Powell, BRCA1 and BRCA2: different roles in a common pathway of genome protection, Nat Rev Cancer, 12 (2011) 68\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH.S. Kim, I.G. Hwang, H.Y. Min, Y.J. Bang, W.H. Kim, Clinical significance of BRCA1 and BRCA2 mRNA and protein expression in patients with sporadic gastric cancer, Oncol Lett, 17 (2019) 4383\u0026ndash;4392.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG. Kim, J. Kim, S.Y. Han, I.G. Hwang, H.S. Kim, H. Min, The effects of BRCA1 expression on the chemosensitivity of gastric cancer cells to platinum agents, Oncol Lett, 17 (2019) 5023\u0026ndash;5029.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Weighted co-expression network, RNF150, microsatellite instability, gastric cancer, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-2352597/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2352597/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eGastric cancer (GC) is a common digestive tract malignancy with the sixth and third global incidence and cancer-related deaths, respectively. Microsatellite instability (MSI), account for one of the molecular subtypes of GC, plays an important role in GC and is affected by a sophisticated network of gene interactions. In this study, we aimed to explore the expression pattern and clinical performance of RNF150 in GC patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWeighted gene co-expression network analysis (WGCNA) was exploited to single out the vital module and core genes in TCGA database. We applied the protein\u0026ndash;protein interaction (PPI) and survival analysis to propose and confirm RNF150 as the hub gene. Finally, we utilized IHC to explore the expression pattern of RNF150 in GC patients.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe turquoise module was adopted as core module for the sake of its highest correlation coefficient and higher module significance value. With the highest weight correlation and standard correlation, RNF150 was finally selected as the hub gene for following validation. In validation, data obtained from the test sets showed a lower expression of RNF150 in MSI GC compared to microsatellite stability (MSS) GC. Moreover, survival analysis shows that MSI GC patients with a lower RNF150 expression level displayed a longer OS time. In 10 GC patients, compared with normal gastric tissues, the protein level of RNF150 was virtually upregulated in GC tissue. Furthermore, RNF150 protein level was decreased in MSI GC samples compared to MSS GC samples, which is in accordance with results we obtained in database.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eRNF150 was determined and confirmed as a novel biomarker in MSI GC. It is expected to be an auspicious prognostic biomarker for MSI GC patients.\u003c/p\u003e","manuscriptTitle":"Identification of RNF150 as the hub gene associated with microsatellite instability in gastric cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-20 22:11:03","doi":"10.21203/rs.3.rs-2352597/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-04-01T04:54:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-03-31T07:36:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2a68ca3d-73a4-466d-a01b-8c26f25773e0","date":"2023-03-18T01:48:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-02-06T20:14:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"363580e2-a0f5-4eeb-8d4c-50bb0c0a7ebe","date":"2023-01-24T23:06:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-01-24T21:25:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-01-24T21:23:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-12-17T14:47:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-12-17T14:23:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2022-12-07T06:48:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a00743c2-ba84-42ab-9a2f-40831fbb850a","owner":[],"postedDate":"December 20th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":17783971,"name":"Biological sciences/Cancer"},{"id":17783972,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2023-10-16T22:09:44+00:00","versionOfRecord":{"articleIdentity":"rs-2352597","link":"https://doi.org/10.1038/s41598-023-39255-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2023-08-01 21:49:47","publishedOnDateReadable":"August 1st, 2023"},"versionCreatedAt":"2022-12-20 22:11:03","video":"","vorDoi":"10.1038/s41598-023-39255-7","vorDoiUrl":"https://doi.org/10.1038/s41598-023-39255-7","workflowStages":[]},"version":"v1","identity":"rs-2352597","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2352597","identity":"rs-2352597","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}