Unveiling the Prognostic Significance of RNA Editing-Related Genes in Colon Cancer: Evidence from Bioinformatics and Experiment

doi:10.21203/rs.3.rs-5291963/v1

Download PDF

Research Article

Unveiling the Prognostic Significance of RNA Editing-Related Genes in Colon Cancer: Evidence from Bioinformatics and Experiment

https://doi.org/10.21203/rs.3.rs-5291963/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

RNA editing is recognized as a crucial factor in cancer biology. Its potential application in predicting the prognosis of colon adenocarcinoma (COAD) remains unexplored.

Methods

RNA editing data of COAD patients were downloaded from Synapse database. LASSO regression was used to construct risk model and verified by Receiver Operating Characteristic (ROC) curve. GO and KEGG enrichment analyses were performed to delineate the biological significance of the differentially expressed genes. Finally, differential analysis and immunohistochemistry were used to verify the expression of adenosine deaminase 1 (ADAR1).

Results

We evaluated a total of 4079 RNA editing sites in 514 COAD patients from Synapse database. A prognostic signature was constructed based on five genes were significantly associated with the prognosis of COAD patients including GNL3L, NUP43, MAGT1, EMP2 and ARSD. Univariate and multivariate Cox regression analysis revealed that RNA editing-related genes (RERGs)-related signature was an independent risk factor for COAD. Moreover, Experimental evidence shows that ADAR1 is highly expressed in colon adenocarcinoma and silencing ADAR1 can inhibit cancer cell proliferation.

Conclusions

We established a prognostic model based on five RERGs with strong predictive value. This model not only serves as a foundation for a novel prognostic tool but also facilitates the identification of potential drug candidates for treating COAD.

colon adenocarcinoma

RNA editing

prognostic model

ADAR1

COAD stands as a prevalent malignant tumor [1, 2]. The latest data released by the International Agency for Research on Cancer (IARC) of the World Health Organization in 2020 shows that COAD is the third most common cancer and the second highest cancer-related death worldwide[3]. The tumor, lymph node, and metastasis (TNM) staging system is commonly used to predict the prognosis of tumors. However, relying solely on the TNM staging system is insufficient in practice to accurately predict the prognosis of colon cancer patients. More and more evidence suggests that genetic biomarkers have become an essential tool [4, 5]. Early intervention and reduction of disease burden can be achieved by searching for prognostic biomarkers.

The pathogenesis of COAD is intricately linked to lifestyle choices influencing various epigenetic modifications[6–8]. Changes in epigenetic features can directly disrupt signaling pathways and promote the development of cancer [9]. Metabolic reprogramming is an important factor in promoting tumor development [10, 11]. In addition to the Warburg effect, Adenosine to inosine RNA editing is also involved in metabolic reprogramming in various tumors, including breast cancer[12], thyroid cancer[13] and colon cancer[14]. RNA editing is a novel epigenetic mechanism that involves the addition, loss, or conversion of bases in the coding region of transcribed RNA. And in some cases, these changes can accelerate the occurrence of tumors[15]. The most common way of RNA editing is mediated by ADAR1, which converts adenosine into inosine (A→I editing). [16]. One of the major targets of ADAR1 dysregulated in various cancers. Additionally, RNA editing assumes a pivotal role role in regulating tumorigenesis and ADAR1 may become a new target for treating COAD. Therefore, A risk prognostic model related to RNA editing was constructed based on the RNA editing status of COAD patients. This prognostic model may provide new targets for the diagnosis and treatment of COAD.

Data Acquisition and Trait analysis

The RNA editing data of COAD were obtained from the Synapse (https://www.synapse.org), and gene expression and clinical information of COAD were downloaded from the TCGA database (https://portal.gdc.cancer.gov/). Gene editing profiles were analyzed using the CMplot package.

Construction and survival analysis of risk prediction model

The training set and test set are randomly allocated through the createDataPartition package. A risk prediction model was constructed by conducting lasso regression analysis on the training and testing sets. Based on the expression of risk genes, the patients in the training and testing sets were categorized into high and low risk groups. The survminer packages were used to analyze overall survival and disease progression free survival (PFS) in high and low risk patients. Patients were further divided into high and low expression groups according to the median value of RNA editing expression of risk genes for survival analysis.

Validation of risk prediction model

To verify the accuracy of the risk model, we used a calibration curve plotted by the rms package and a nomogram plotted by the regplot package to assess patient outcomes. Meanwhile, we used the decision curve of the multi-factor COX regression model plotted by the ggDCA package and the ROC curve plotted by timeROC package to evaluate the accuracy of the risk model.

Differential gene enrichment analysis in high and low risk groups

Identify differentially expressed genes between high-risk and low-risk groups using the limma package, and conduct gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using the clusterProfile package. To further reduce bias due to differential genes, we performed Gene Set Enrichment Analysis (GSEA) [17] by using clusterProfiler R package. To further eliminate the bias caused by differential genes, we conducted GSEA analysis using clusterProfiler package.

Immunohistochemistry

Immunohistochemistry (IHC) was performed on paraffin-embedded colon adenocarcinoma sections obtained from Jianghan university of Diagnostic Pathology Center. Sections were deparaffinized with xylene and rehydrated through a graded series of alcohol to water. Endogenous peroxidase activity was blocked with 3% hydrogen peroxide (H₂O₂) at room temperature, followed by blocking with goat serum. After washing with phosphate-buffered saline (PBS) three times, sections were incubated with ADAR1 antibody overnight at 4°C. Subsequent to PBS washing, the sections were incubated with a secondary antibody. The slides were then developed with diaminobenzidine (DAB) and counterstained with hematoxylin. The IHC sections were further scanned and analyzed using a light microscope and ImageJ software.

cell culture and transfection

The HT29 cell line was grown in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) at 37°C in an incubator with a 5% CO2 atmosphere. The cells were seeded into a 96-well plate and transfected during their logarithmic growth phase. Four siRNAs targeting ADAR1 were used: siRNA1 with the sense sequence CCAGUAGUUUCCUGCUUAATT and the antisense sequence UUAAGCAGGAAACUACUGGTT; siRNA2 with the sense sequence CUGCGACUAUCUCUUCAAUTT and the antisense sequence AUUGAAGAGAUAGUCGCAGTT; siRNA3 with the sense sequence CAAGGCCCGAGAUAUAAAUTT and the antisense sequence AUUUAUAUCUCGGGCCUUGTT; siRNA4 with the sense sequence GCAGGAUGCAGCUAUGAAATT and the antisense sequence UUUCAUAGCUGCAUCCUGCTT. The transfection protocol involved mixing 5 µl of serum-free medium, 4 mM of siRNA, and 0.16 µl of Lipo8000 transfection reagent. This mixture was gently combined and added to the cells to achieve a final volume of 100 µl per well.

RNA extraction

RNA was extracted using the BIOLOGY RNA Extraction Kit (Catalog No. BOG203). Briefly, cells were lysed with RLT Plus buffer and collected in a 1.5 mL centrifuge tube. An equal volume of 70% ethanol was added, and the mixture was applied to the adsorption column (RA) followed by centrifugation. Proteinase K (RW1) was then added, followed by another centrifugation step. Subsequently, 500 µL of wash buffer (RW) was added, and the column was centrifuged at 13,000 rpm for 30 seconds. This wash step was repeated once. After removing the adsorption column (RA), 30 µL of RNase-free water was added, and the column was incubated at room temperature for 1 minute before centrifugation at 13,000 rpm for 1 minute to elute the RNA.

RNA reverse transcription

The isolated RNA was then added to the All-in-One First-Strand Synthesis MasterMix (Catalog No. BOLG1025S) and incubated at 50°C for 15 minutes, followed by a 5-minute incubation at 85°C to terminate the reaction, yielding cDNA.

Real time quantitative PCR

Quantitative PCR (qPCR) was performed using the Taq SYBR® Green qPCR Premix (Catalog No. BOLG2016). The reaction mixture consisted of 10 µL of Taq SYBR® Green qPCR Premix, 0.2 µM of ADAR1 forward primer (5'-agcccaagttcgtttaccaagc-3'), 0.2 µM of ADAR1 reverse primer (5'-ttcctgcttgccttgcttcttg-3'), 100 ng of DNA template, and nuclease-free water to a final volume of 20 µL. The PCR cycling conditions were an initial denaturation at 95°C for 30 seconds, followed by 40 cycles of 95°C for 10 seconds, and 60°C for 30 seconds for annealing and extension. The gene expression of ADAR1 was calculated using the 2^-ΔCT method.

CCK8 assay

Following a 48-hour incubation post-transfection, the cells were ready for CCK8 assays. To each well containing 100 µl of cell culture medium, 10 µl of CCK8 solution was added. The cells were then further incubated for 1 hour in the cell culture incubator. After this period, the absorbance of the solution in each well was measured at a wavelength of 450 nm using a plate reader.

Statistical analysis

All statistical analyses presented in this manuscript were performed utilizing R software, version 4.3.3. For immunohistochemical analysis, ImageJ software, version 1.8.0, was employed to derive the quantitative results. A threshold of P < 0.05 was applied to determine statistical significance.

Gene editing and model selection of COAD patients

By using patient gene-editing data from TCGA data in synapse database, lasso regression analysis is performed to construct a risk model. The formula is GNL3L|chrX:54588713* 11.5451657436054 + NUP43|chr6:150047362* 16.8615803892493 + MAGT1|chrX:77083183* 12.6341759937368 + EMP2|chr16:10625356* 17.2409941962885 + ARSD|chrX:2825076* 7.5060543627754. The dynamic selection process diagram of lasso regression variables was observed by lambda curve (Fig. 1A). Select the appropriate lambda value by cross-validation (Fig. 1B). The prognostic genes were screened by univariate cox regression analysis, and the loop maps (Fig. 1C) and Manhattan maps (Fig. 1D) were plotted according to their P-values.

Identification of Differentially Expressed RNA Editing Sites in COAD Patients

We evaluated a total of 4079 RNA editing sites in the tissues of 514 COAD patients from the TCGA database (Fig. 1A and B). These results indicated that RNA editing was upregulated in the chromosomes of COAD patients, these SNPs were distributed across the whole genome. The high RNA editing level in tumors was attributed to DNA alterations (mutation load).

Effects of different risks on Mortality

Patients in the training set and the testing set were divided into high and low risk groups according to the expression of risk genes mentioned above. The effect of different risk genes on the 8-year overall mortality was significantly different. (P < 0.001 for interaction of COAD patients with different risk genes as a continuous variable, Fig. 2A). The mortality of training set and the testing set with high and low risk were also significantly different (P < 0.001 and P = 0.021, Fig. 2B and C). PFS refers to the time from diagnosis to disease progression or death, and is an important indicator of patient prognosis [18]. Figure 2D shows the PFS of low risk group was significantly higher than the high risk group (9.2 vs. 7.8 years; P < 0.001). Figures 2A-D illustrate that patients with COAD in the low-risk category experienced more favorable outcomes and extended survival compared to those in the high-risk category. This suggests that the developed risk assessment model is capable of accurately forecasting COAD prognosis.

To further evaluated the prognostic significance of RERGs, the prognosis of COAD patients was found to be significantly influenced by five genes, including GNL3L, NUP43, MAGT1, EMP2 and ARSD. The heatmaps of five RERGs expression were presented in Fig. 2E (all patients), 2G (training set group) and 2I (testing set group). Additionally, the scatterplots depicted in Figs. 2F, 2H, and 2J reveal that individuals categorized as low-risk exhibit longer survival durations compared to those in the high-risk group. The findings imply that higher expression levels of genes GNL3L, NUP43, MAGT1, EMP2, and ARSD are associated with the high-risk group, which also correlates with a greater number of fatalities.

Survival analysis of RNA editing based on risk gene in COAD patients

To delve deeper into the prognostic relevance of the RERGs, Kaplan-Meier (K-M) survival curves were constructed. The analysis demonstrated a significant correlation between the survival chances of COAD patients and the expression of certain genes, specifically ARSD, EMP2, GNL3L, and NUP43. (P < 0.001, P = 0.003, P = 0.002 and P < 0.001) (Fig. 3A-D).

To confirm the influence of RNA editing on the survival outcomes in COAD, we conducted both univariate and multivariate Cox regression analyses. The findings indicated that RNA editing is linked to a worsened prognosis for COAD patients, as illustrated in Figs. 4A and 4B. In addition, to assess the model's predictive accuracy, we scrutinized the correlation between the model's components and various clinical parameters of COAD, as shown in Figs. 4C and 4D. The forest plot indicated that there is a statistically significant link between the risk scores and the age of COAD patients, with an increased risk score observed in those over 65 years old (Fig. 4C, P = 0.022). Conversely, the risk scores were not significantly associated with gender (Fig. 4D, P = 0.062).

The calibration curve data demonstrated a high degree of congruence between the nomogram's predicted outcomes and the actual survival rates at 1, 2, and 3 years in the risk prediction model, as depicted in Fig. 5A. The decision curve analysis for the cohorts further suggested that our nomogram could potentially outperform alternative models in forecasting the survival of COAD patients.Recognizing the significance of the RERG Risk score in the survival of COAD patients, we developed a nomogram that integrates the RERG Risk score along with clinicopathological characteristics. This tool is designed to predict the 1-, 2-, and 3-year overall survival rates for these patients. (Fig. 5B). A decision curve analysis of the nomogram was performed (Fig. 5C). The straight pink line represents that none patients have the clinicopathological features, and the red curve represents the clinical benefit of the nomogram. In addition, we conducted a comparison of the predictive efficacy of these biomarkers against others, utilizing the Area Under the Curve (AUC) as a metric for assessing prognostic accuracy. A higher AUC value signifies a more robust predictive capacity. The outcomes of the ROC curve analysis revealed that the AUC for risk, nomogram, age, and stage were 0.865, 0.883, 0.763, and 0.690, respectively. This data suggests that the nomogram possesses superior sensitivity and specificity in forecasting the 3-year prognosis of COAD, as illustrated in Fig. 5D.

Functional analyses of the DEGs signature in COAD with high and low risk

Differentially expressed genes (DEGs) between high risk group and low risk group were presented in the heat map (Fig. 6A) and the volcano plot (Fig. 6B). We found five DEGs were highly expressed. To clarify the potential mechanism that DEGs affects the prognosis of COAD with high and low risk, we analyzed the GO gene sets and KEGG gene sets between high-risk and low-risk groups using GSEA [15] (Fig. 6C and D). Our analysis identified significant enrichment of several pathways, including Staphylococcus aureus infection signaling, NOD-like receptor signaling, GABAergic synapse signaling, Fc gamma R-mediated phagocytosis, and thiamine metabolism signaling pathways. These pathways are commonly implicated in the advancement of tumors and may provide insights into how an increased number of DEGs are indicative of a poorer prognosis for COAD patients. These results above indicated that our predictive model could have better reliability and accuracy, and might be a prospective prognostic indicator for COAD patients. In addition, we found that many signaling pathway were significantly enriched in the high risk group (Fig. 6E) and the low risk group (Fig. 6F). These discoveries indicate that the DEGs signature is strongly linked to immune responses, highlighting the necessity for further investigation to elucidate the underlying connections..

ADAR1-based RNA editing has become a powerful tool for engineering RNA, correcting disease-causing mutations, and regulating protein function. The expression of ADAR1 was positively correlated with risk values of COAD patients (R = 0.27, P = 0.062, Fig. 7A). The expression of ADAR1 protein in primary tumor group was significantly higher than normal group (Fig. 7B). We found that immunohistochemical staining of ADAR1 was significantly enriched in COAD samples (Fig. 7C and D), suggesting that ADAR1 is involved in disease progression with RNA editing.

knockdown of ADAR1 inhibits the proliferation of colorectal cancer cells

To elucidate the function of ADAR1 in colon carcinogenesis, we initially employed RNA interference (RNAi) to suppress ADAR1 expression in the HT29 cell line. As depicted in Fig. 8, transfection with siRNA 1 and siRNA 2 led to a significant downregulation of both ADAR1 mRNA (Fig. 8A). Subsequent CCK8 assays demonstrated that the OD values for the ADAR1-silenced cells were markedly lower compared to the control cells (Fig. 8B), suggesting that ADAR1 knockdown substantially inhibits the proliferation of colon cancer cells.

COAD ranks among the prevalent malignant neoplasms. For those in the later stages of stage II and stage III, undergoing surgery along with adjuvant chemotherapy is often advised to mitigate the likelihood of disease recurrence. However, even after radical surgery, roughly 50% of early-stage patients still experience relapse and metastasis[19]. Consequently, there is an imperative for the development of a personalized prognostic model that facilitates tailored treatment and precision medicine approaches for individuals with COAD.

The genesis and advancement of COAD involve the aggregation of numerous genetic and epigenetic changes within the colonic tissue[9]. RNA editing has come to the forefront as a pivotal epigenetic mechanism that contributes to the development and progression of multiple types of cancer[20]. The presence of ADAR1, functioning as either an oncogene or a tumor suppressor, has the potential to modify the properties of tumors, fostering a phenotype that is more aggressive[8]. A number of research studies have shown that RNA editing may serve as a potential therapeutic target across various solid malignancies [21–25]. Consequently, we developed and confirmed a prognostic model leveraging RERGs to forecast the outcomes for COAD patients.

In our quest to identify genes with differential expression tied to RNA editing in COAD, we gathered and scrutinized data from TCGA. This analysis was conducted using univariate Cox regression, multivariate Cox regression, and the LASSO algorithm. Five RERGs(GNL3L, NUP43, MAGT1, EMP2, and ARSD) were identified and utilized to formulate a prognostic risk assessment model. Guanine nucleotide-binding protein-like 3-like (GNL3L), a recently characterized nucleolar GTPase, is known to modulate the mitotic cycle of eukaryotic cells, thereby influencing cellular proliferation and apoptosis[26, 27]. Studies have highlighted GNL3L's substantial involvement in a spectrum of cancers, including renal, colorectal, esophageal, hepatic, rectal, and breast malignancies[28–30]. Overexpression of GNL3L was positively correlated with the progression of colon cancer and inhibition of GNL3L was an efficient therapeutic approach[28]. Overall, GNL3L could be a crucial marker for predicting patient outcomes and is believed to have an immunomodulatory effect in various cancers[31]. Magnesium transporter 1 (MAGT1), known for its high selectivity in transporting magnesium across the plasma membrane[32, 33]. is particularly noteworthy in T-lymphocytes, where it facilitates a swift influx of magnesium post-receptor activation. This function underscores MAGT1's role in mediating immune responses[32, 34, 35]. Recent study has demonstrated that an increase in the expression of the magnesium transporter MAGT1 has been associated with the development of drug resistance in colon cancer[36, 37]. Epithelial membrane protein-2 (EMP2), classified as a tetraspanin, has been detected in breast cancer metastasis and circulating tumor cells [38–40]. Nonetheless, the specific contribution of EMP2 to the pathogenesis of COAD remains largely unexplored. In recent studies, EMP2 has been observed to influence the recruitment of uterine natural killer (NK) cells in mice, suggesting an immunological impact[41]. Arylsulfatase D (ARSE), a member of the sulfatase enzyme family, is integral to the metabolic processes involving sphingolipids[42],whose aberrations have been reported to contribute to chemotherapy resistance in various cancers[43]. ARSD had been implicated in regulation the growth and metastasis of colon cancer[44]. The gene Nuclear Pore 43 (NUP43) is responsible for the production of the Nup107-160 complex, a component that resides at the centromere during mitosis and is instrumental in governing the processes of mitosis and the segregation of chromosomes [45]. NUP43 upregulation was related to DNA amplification[46]. An earlier investigation established that NUP43 functions as an oncogenic promoter in tumor progression[47]. To elucidate the predictive impact of these gene biomarkers on COAD, we advanced a nomogram for forecasting the prognosis of COAD patients. The findings confirmed that these RERGs serve as effective prognostic indicators, exhibiting commendable predictive performance.

The RNA editing enzyme ADAR1 has been found to be irregularly expressed across a spectrum of cancers. Recent studies have shown that elevated levels of ADAR1 are linked to different types of cancer, such as hepatocellular carcinoma, esophageal carcinoma, and colorectal carcinoma[48–50]. ADAR1's regulatory influence is exerted through nonsynonymous substitutions in oncoproteins and alterations in the microRNA interference pathway. These alterations, which can include modifications to protein codes and properties, suggest that increased ADAR1 activity can enhance the invasive and aggressive behavior of colorectal cancer cells[51]. The accuracy of our prediction mode model were further validated with ADAR1. These results indicated that the key role of RNA editing in predicting COAD and high-risk is linked to worse prognosis.

In this study, we conducted an analysis employing GSEA to compare the KEGG gene sets between the high-risk and low-risk cohorts. We found that staphylococcus aureus infection signaling, NOD-like receptor signaling pathway, GABAergic synapse signaling pathway, Fc gamma R-mediated phagocytosis signaling pathway and thiamine metabolism signaling pathway were significantly enriched through KEGG. Most of these enriched signaling pathways were related to immunity. It was known that immune infiltration was associated with COAD[52]. A growing body of evidence has highlighted the link between the immune status in colon cancer and patient outcomes[53]. Staphylococcus aureus, a gram-positive bacterium and an opportunistic pathogen in humans[54] poses a risk for serious infections, particularly when the immune defenses are weakened[55]. NOD-like receptors (NLRs) represent a recently identified class of pattern recognition receptors pivotal in modulating both innate and adaptive immune responses[56]. Their active participation in the regulation of inflammatory signals and the engagement of the adaptive immune system has been associated with their influence on disease progression and tumor development. Notably, the expression of NLRP3 mRNA has been found to be markedly elevated in COAD tumor tissues[57]. Gamma-aminobutyric acid (GABA), primarily recognized for its inhibitory role in the central nervous system by reducing neuronal excitability, is now understood to have additional roles. It is increasingly recognized as an influential factor in the regulation of cancer cell proliferation, spread, and the antitumor immune response[58]. GABA is synthesized and secreted by both mouse and human B cells, and research indicates that GABA derived from B cells or plasma cells is a critical determinant in the regulation of macrophage and CD8 + T cell responses, as well as tumor growth in a murine model of colon cancer[59]. Fc gamma R-mediated phagocytosis receptors are integral to the immune system, bridging the innate and adaptive immune components. They achieve this by linking the specific detection of IgG antibodies to the activation of phagocytic leukocytes, thereby enhancing the immune response against pathogens and other threats[60]. Thiamine (vitamin B1) was related with the immune system and diverse cancers[61, 62]. Our results suggested that the RERGs may mediate the development of tumors through the immune response. Thus, RNA editing may be involved in COAD through multiple targets and signaling pathways.

To sum up, we have developed a prognostic assessment model leveraging five RERGs that exhibit significant predictive power. This research offers a novel approach to personalized therapeutic strategies for individuals afflicted with COAD.

Acknowledgements

No acknowledgement.

Author contributions

Conceptualization: Zhengcong Deng; Methodology: Zhengcong Deng, Xueqin Jin, Xiang Wang; Formal analysis and investigation: Bingxue Liu, Xiang Wang; Writing - original draft preparation: Bingxue Liu, Lixia Huang; Writing - review and editing: Xueqin Jin, Lixia Huang; Supervision: Lixia Huang, Xiang Wang.

Funding

No funding was received.

Availability of data and materials

The data generated in the present study may be found in the Synapse at the following URL: https://www.synapse.org..

Ethics approval and consent to participate

This study was approved by the ethics committee of Jianghan university.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Author details

1. Wuhan Donghu New Technology Development Zone Disease Prevention and Control Center, Wuhan city, Hubei Province, China. Postal code: 430200.

2. Hubei Third People's Hospital, Wuhan city, Hubei Province, China. Postal code: 430033.

3. Medical School, Jianghan University, Wuhan City, Hubei Province, China. Postal code: 430056.

4. Wuhan University of Arts and Science, Wuhan City, Hubei Province, China. Postal code: 430000.

Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. Ca Cancer J Clin. 2021;71(1):7-33.
Siegel RL, Miller KD, Goding SA, et al. Colorectal cancer statistics, 2020. Ca Cancer J Clin. 2020;70(3):145-64.
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Ca Cancer J Clin. 2021;71(3):209-49.
Vacante M, Borzi AM, Basile F, Biondi A. Biomarkers in colorectal cancer: current clinical utility and future perspectives. World J Clin Cases. 2018;6(15):869-81.
Koncina E, Haan S, Rauh S, Letellier E. Prognostic and predictive molecular biomarkers for colorectal cancer: updates and challenges. Cancers (Basel). 2020;12(2).
Feinberg AP, Tycko B. The history of cancer epigenetics. Nat Rev Cancer. 2004;4(2):143-53.
Jenuwein T, Allis CD. Translating the histone code. Science. 2001;293(5532):1074-80.
Takeda S, Shigeyasu K, Okugawa Y, et al. Activation of azin1 rna editing is a novel mechanism that promotes invasive potential of cancer-associated fibroblasts in colorectal cancer. Cancer Lett. 2019;444127-35.
Okugawa Y, Grady WM, Goel A. Epigenetic alterations in colorectal cancer: emerging biomarkers. Gastroenterology. 2015;149(5):1204-25.
Martinez-Reyes I, Chandel NS. Cancer metabolism: looking forward. Nat Rev Cancer. 2021;21(10):669-80.
Faubert B, Solmonson A, Deberardinis RJ. Metabolic reprogramming and cancer progression. Science. 2020;368(6487).
Li Y, Wang NX, Yin C, Jiang SS, Li JC, Yang SY. Rna editing enzyme adar1 regulates mettl3 in an editing dependent manner to promote breast cancer progression via mettl3/arhgap5/ythdf1 axis. Int J Mol Sci. 2022;23(17).
Ramirez-Moya J, Miliotis C, Baker AR, Gregory RI, Slack FJ, Santisteban P. An adar1-dependent rna editing event in the cyclin-dependent kinase cdk13 promotes thyroid cancer hallmarks. Mol Cancer. 2021;20(1):115.
Takahashi K, Shigeyasu K, Kondo Y, et al. Rna editing is a valuable biomarker for predicting carcinogenesis in ulcerative colitis. J Crohns Colitis. 2023;17(5):754-66.
Qi L, Chan TH, Tenen DG, Chen L. Rna editome imbalance in hepatocellular carcinoma. Cancer Res. 2014;74(5):1301-6.
Nishikura K. Functions and regulation of rna editing by adar deaminases. Annu Rev Biochem. 2010;79321-49.
Du X, Zhang Y. Integrated analysis of immunity- and ferroptosis-related biomarker signatures to improve the prognosis prediction of hepatocellular carcinoma. Front Genet. 2020;11614888.
Balawardena J, Skandarajah T, Rathnayake W, Joseph N. Breast cancer survival in sri lanka. Jco Glob Oncol. 2020;6589-99.
Miller KD, Siegel RL, Lin CC, et al. Cancer treatment and survivorship statistics, 2016. Ca Cancer J Clin. 2016;66(4):271-89.
Baysal BE, Sharma S, Hashemikhabir S, Janga SC. Rna editing in pathogenesis of cancer. Cancer Res. 2017;77(14):3733-9.
Ge F, Cao X, Jiang Y. A-to-i rna editing shows dramatic up-regulation in osteosarcoma and broadly regulates tumor-related genes by altering microrna target regions. J Appl Genet. 2023;64(3):493-505.
Chen H, Yao J, Bao R, et al. Cross-talk of four types of rna modification writers defines tumor microenvironment and pharmacogenomic landscape in colorectal cancer. Mol Cancer. 2021;20(1):29.
Li X, Ma S, Deng Y, Yi P, Yu J. Targeting the rna m(6)a modification for cancer immunotherapy. Mol Cancer. 2022;21(1):76.
Wong TL, Loh JJ, Lu S, et al. Adar1-mediated rna editing of scd1 drives drug resistance and self-renewal in gastric cancer. Nat Commun. 2023;14(1):2861.
Baker AR, Slack FJ. Adar1 and its implications in cancer development and treatment. Trends Genet. 2022;38(8):821-30.
Bourne HR, Sanders DA, Mccormick F. The gtpase superfamily: conserved structure and molecular mechanism. Nature. 1991;349(6305):117-27.
Caldon CE, Yoong P, March PE. Evolution of a molecular switch: universal bacterial gtpases regulate ribosome function. Mol Microbiol. 2001;41(2):289-97.
Kannathasan T, Kuo WW, Chen MC, et al. Chemoresistance-associated silencing of mir-4454 promotes colorectal cancer aggression through the gnl3l and nf-kappab pathway. Cancers (Basel). 2020;12(5).
Thoompumkal IJ, Rehna K, Anbarasu K, Mahalingam S. Leucine zipper down-regulated in cancer-1 (ldoc1) interacts with guanine nucleotide binding protein-like 3-like (gnl3l) to modulate nuclear factor-kappa b (nf-kappab) signaling during cell proliferation. Cell Cycle. 2016;15(23):3251-67.
Thoompumkal IJ, Subba RM, Kumaraswamy A, Krishnan R, Mahalingam S. Gnl3l is a nucleo-cytoplasmic shuttling protein: role in cell cycle regulation. Plos One. 2015;10(8):e135845.
Liu P, Guo W, Su Y, et al. Multi-omics analysis of gnl3l expression, prognosis, and immune value in pan-cancer. Cancers (Basel). 2022;14(19).
Deason-Towne F, Perraud AL, Schmitz C. The mg2+ transporter magt1 partially rescues cell growth and mg2+ uptake in cells lacking the channel-kinase trpm7. Febs Lett. 2011;585(14):2275-8.
Quamme GA. Molecular identification of ancient and modern mammalian magnesium transporters. Am J Physiol Cell Physiol. 2010;298(3):C407-29.
Li FY, Chaigne-Delalande B, Kanellopoulou C, et al. Second messenger role for mg2+ revealed by human t-cell immunodeficiency. Nature. 2011;475(7357):471-6.
Chaigne-Delalande B, Li FY, O'Connor GM, et al. Mg2+ regulates cytotoxic functions of nk and cd8 t cells in chronic ebv infection through nkg2d. Science. 2013;341(6142):186-91.
Cazzaniga A, Moscheni C, Trapani V, et al. The different expression of trpm7 and magt1 impacts on the proliferation of colon carcinoma cells sensitive or resistant to doxorubicin. Sci Rep. 2017;740538.
Locatelli L, Cazzaniga A, Fedele G, et al. A comparison of doxorubicin-resistant colon cancer lovo and leukemia hl60 cells: common features, different underlying mechanisms. Curr Issues Mol Biol. 2021;43(1):163-75.
Fu M, Maresh EL, Helguera GF, et al. Rationale and preclinical efficacy of a novel anti-emp2 antibody for the treatment of invasive breast cancer. Mol Cancer Ther. 2014;13(4):902-15.
Obermayr E, Sanchez-Cabo F, Tea MK, et al. Assessment of a six gene panel for the molecular detection of circulating tumor cells in the blood of female cancer patients. Bmc Cancer. 2010;10666.
Chen Q, Yao L, Burner D, et al. Epithelial membrane protein 2: a novel biomarker for circulating tumor cell recovery in breast cancer. Clin Transl Oncol. 2019;21(4):433-42.
Williams CJ, Chu A, Jefferson WN, et al. Epithelial membrane protein 2 (emp2) deficiency alters placental angiogenesis, mimicking features of human placental insufficiency. J Pathol. 2017;242(2):246-59.
Trojani A, Di Camillo B, Tedeschi A, et al. Gene expression profiling identifies arsd as a new marker of disease progression and the sphingolipid metabolism as a potential novel metabolism in chronic lymphocytic leukemia. Cancer Biomark. 2011;11(1):15-28.
Ogretmen B. Sphingolipid metabolism in cancer signalling and therapy. Nat Rev Cancer. 2018;18(1):33-50.
Han X, Wang Z, Zhang L, et al. Slf1 polymorphism predicts response to oxaliplatin-based adjuvant chemotherapy in patients with colon cancer. Am J Cancer Res. 2021;11(4):1522-39.
Loiodice I, Alves A, Rabut G, et al. The entire nup107-160 complex, including three new members, is targeted as one entity to kinetochores in mitosis. Mol Biol Cell. 2004;15(7):3333-44.
Tian C, Zhou S, Yi C. High nup43 expression might independently predict poor overall survival in luminal a and in her2+ breast cancer. Future Oncol. 2018;14(15):1431-42.
Liu YY, Jiao WY, Li T, Bao YY. Mirna-409-5p dysregulation promotes imatinib resistance and disease progression in children with chronic myeloid leukemia. Eur Rev Med Pharmacol Sci. 2019;23(19):8468-75.
Chen L, Li Y, Lin CH, et al. Recoding rna editing of azin1 predisposes to hepatocellular carcinoma. Nat Med. 2013;19(2):209-16.
Qin YR, Qiao JJ, Chan TH, et al. Adenosine-to-inosine rna editing mediated by adars in esophageal squamous cell carcinoma. Cancer Res. 2014;74(3):840-51.
Shigeyasu K, Okugawa Y, Toden S, et al. Azin1 rna editing confers cancer stemness and enhances oncogenic potential in colorectal cancer. JCI Insight. 2018;3(12).
Han SW, Kim HP, Shin JY, et al. Rna editing in rhoq promotes invasion potential in colorectal cancer. J Exp Med. 2014;211(4):613-21.
Peng D, Wang L, Li H, et al. An immune infiltration signature to predict the overall survival of patients with colon cancer. Iubmb Life. 2019;71(11):1760-70.
Wu D, Ding Y, Wang T, et al. Significance of tumor-infiltrating immune cells in the prognosis of colon cancer. Onco Targets Ther. 2020;134581-9.
Lowy FD. Staphylococcus aureus infections. N Engl J Med. 1998;339(8):520-32.
Lowy FD. How staphylococcus aureus adapts to its host. N Engl J Med. 2011;364(21):1987-90.
Saxena M, Yeretssian G. Nod-like receptors: master regulators of inflammation and cancer. Front Immunol. 2014;5327.
Liu S, Fan W, Gao X, et al. Estrogen receptor alpha regulates the wnt/beta-catenin signaling pathway in colon cancer by targeting the nod-like receptors. Cell Signal. 2019;6186-92.
Huang D, Alexander PB, Li QJ, Wang XF. Gabaergic signaling beyond synapses: an emerging target for cancer therapy. Trends Cell Biol. 2023;33(5):403-12.
Zhang B, Vogelzang A, Miyajima M, et al. B cell-derived gaba elicits il-10(+) macrophages to limit anti-tumour immunity. Nature. 2021;599(7885):471-6.
Kusner DJ, Hall CF, Jackson S. Fc gamma receptor-mediated activation of phospholipase d regulates macrophage phagocytosis of igg-opsonized particles. J Immunol. 1999;162(4):2266-74.
Peterson CT, Rodionov DA, Osterman AL, Peterson SN. B vitamins and their role in immune regulation and cancer. Nutrients. 2020;12(11).
Jonus HC, Hanberry BS, Khatu S, et al. The adaptive regulation of thiamine pyrophosphokinase-1 facilitates malignant growth during supplemental thiamine conditions. Oncotarget. 2018;9(83):35422-38.

No competing interests reported.

Download PDF

Editor assigned by journal
19 Oct, 2024
Submission checks completed at journal
19 Oct, 2024
First submitted to journal
18 Oct, 2024

You are reading this latest preprint version

Unveiling the Prognostic Significance of RNA Editing-Related Genes in Colon Cancer: Evidence from Bioinformatics and Experiment

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials and methods

Data Acquisition and Trait analysis

Construction and survival analysis of risk prediction model

Validation of risk prediction model

Differential gene enrichment analysis in high and low risk groups

Immunohistochemistry

cell culture and transfection

RNA extraction

RNA reverse transcription

Real time quantitative PCR

CCK8 assay

Statistical analysis

Results

Gene editing and model selection of COAD patients

Identification of Differentially Expressed RNA Editing Sites in COAD Patients

Effects of different risks on Mortality

Survival analysis of RNA editing based on risk gene in COAD patients

Functional analyses of the DEGs signature in COAD with high and low risk

knockdown of ADAR1 inhibits the proliferation of colorectal cancer cells

Discussion

Declarations

References

Additional Declarations

Status:

Version 1