Identification and validation of a novel redox- related differentially expressed lncRNA prognostic signature for predicting clinical immunotherapy response in gastric cancer

Identification and validation of a novel redox- related differentially expressed lncRNA prognostic signature for predicting clinical immunotherapy response 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 and validation of a novel redox- related differentially expressed lncRNA prognostic signature for predicting clinical immunotherapy response in gastric cancer Guisen Peng, Di Wu, Lidong Shan, Weicheng Lu, Mingjie Hu, Mulin Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2843204/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Redox responses modulated by intracellular long noncoding RNA (lncRNA) can be involved in tumorigenesis and progression. However, the role of redox-related lncRNAs (RRlncRNAs) in gastric cancer (GC) development remains mostly unknown. Our research aims to establish and validate novel prognostic and immune infiltration markers for GC by constructing a prognostic model of RRlncRNAs. We downloaded the transcriptomic and mutational data for 407 GC pa-tients from The Cancer Genome Atlas (TCGA) database and randomized them 1:1 into a training and validation set to show that redox-related lncRNAs affect GC patients' prognosis. Subse-quently, the prognostic model was constructed for the screened RRlncRNAs using the Least Absolute Shrinkage and Selection Operator (LASSO) and the multivariate COX regression algo-rithm. Then, Survival analyses were performed on the train and test sets. The overall survival rate of GC patients was significantly correlated with the signatures of eight RRlncRNAs, including AC103702.2, AL138756.1, AL356417.2, CFAP61-AS1, RHPN1-AS1, CDK6-AS1, LINC02864, and AL355574.1. Meanwhile, we validated the model's accuracy through nomograms, Decision Curve Analysis (DCA), and comparisons using models from other studies. The results demonstrated that our model is more effective and outperforms the signature of Jiang et al. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of gene enrichment in high-risk patients shows significant enrichment in immune-related pathways. Waterfall plots of gene mutations, tumor mutation burden (TMB), and tumor immune dysfunction and exclusion (TIDE) showed significant differences in immune function between high- and low-risk groups. Then, we divided the 407 GC patients into two clusters using a consensus clustering algorithm and found significant differences in their immune microenvironment through immune cell difference anal-ysis, ESTIMATEScore, and gene set enrichment analysis (GSEA). Taken together, we conclude that the prognostic model constructed by RRlncRNAs can significantly affect the prognosis of GC patients and may alter their tumor progression by modulating the immune microenvironment in vivo. Our study found eight RRlncRNA-associated signatures, representing promising new markers for immunotherapy and diagnosis in GC patients. Biological sciences/Computational biology and bioinformatics Biological sciences/Computational biology and bioinformatics/Data mining Biological sciences/Computational biology and bioinformatics/Data processing Biological sciences/Computational biology and bioinformatics/Databases Biological sciences/Computational biology and bioinformatics/Functional clustering Biological sciences/Cancer/Cancer microenvironment Biological sciences/Cancer/Tumour biomarkers Biological sciences/Cancer/Tumour immunology Biological sciences/Cancer Biological sciences/Cancer/Gastrointestinal cancer Biological sciences/Cancer/Gastrointestinal cancer/Gastric cancer Biological sciences/Molecular biology/Non coding rnas Biological sciences/Molecular biology/Non coding rnas/Long non coding rnas Health sciences/Oncology/Cancer/Gastrointestinal cancer/Gastric cancer Health sciences/Oncology/Cancer/Cancer microenvironment Health sciences/Oncology/Cancer/Cancer therapy Health sciences/Oncology/Cancer/Tumour biomarkers Health sciences/Oncology/Cancer/Tumour immunology Health sciences/Oncology/Cancer/Cancer models Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction GC is a prevalent worldwide malignancy, with the fifth-highest incidence and fourth-highest mortality rate 1 . GC development possesses complex molecular mechanisms 2 . Recently, studies have identified several potential prognostic and immunological biomarkers of GC, which facilitate the diagnosis and treatment of GC. Nevertheless, the prognostic survival of advanced GC is still not promising. Therefore, identifying biomarkers with higher sensitivity and specificity for GC treatment is essential 3 – 5 . Redox homeostasis is the balance of redox equivalents, a critical reaction process in the physiological and pathological processes of the body. Studies have shown that the production and elimination of redox equivalents, such as reactive oxygen species (ROS) like superoxide (O 2 − ), is unbalanced, leading to a certain level of oxidative stress in vivo 6 . For example, cancer cells have a higher production of ROS than normal cells under the premise of altered mitochondrial function, leading to an imbalance in cellular redox levels 7 . Thus, it is necessary to develop in vivo defense systems to prevent an imbalance in cellular redox levels and inhibit the development of cancer cells 8 . Therefore, studies targeting ROS production, depletion, and in vivo antioxidant capacity in GC patients may lead to therapeutic benefits. Long noncoding RNAs (lncRNAs) are a new class of RNA molecules longer than 200 nt and are important components of the noncoding genome 9 , 10 . Numerous studies have shown that lncRNAs are involved in various biological processes, including DNA methylation, histone modification, post-transcriptional regulation of RNA, and translation regulation of proteins, and they regulate various physiological and pathological processes 11 – 13 . In addition, several other studies have found that lncRNAs play oncogenic roles in various malignancies, including GC. Additionally, recent studies revealed that RHPN1-AS1 affects the prognostic characteristics of patients with hepatocellular carcinoma by acting as a facilitator in some cancers as a redox-related lncRNA and combining with other hypoxia-related lncRNA 14 – 16 . Therefore, targeting lncRNAs involved in regulating redox function, such as RHPN1-AS1, has great potential in diagnosing and treating GC. Currently, curative resection is the standard of care for GC. Chemotherapy with platinum compounds, fluoropyrimidines, docetaxel, and other drugs is the standard of care for unresectable GC 17 – 20 . However, recent groundbreaking studies have targeted immune checkpoint inhibitors (ICIs). Anti-cytotoxic T-lymphocyte antigen 4 (CTLA4) mAb (ipilimumab) and anti-programmed death-1 (PD-1) mAb (nivolumab and pembrolizumab), among others, have emerged as the latest targets for immunotherapy, pioneering a new path in cancer treatment paradigms 21 – 24 . Inhibition of the PD-1/programmed death ligand 1 (PD-L1) axis with ICI is a novel therapeutic modality for treating advanced GC 25 , 26 . Although PD-1 mAb holds great therapeutic promise for advanced GC, there are many shortcomings. This makes the development of new therapeutic markers targeting ICI necessary and critical. Based on the above research basis, we plan to demonstrate the involvement of redox-related lncRNA in the immune microenvironment of GC patients, thus providing a theoretical basis for novel clinical immunotherapy for GC. Results 1. Redox-related genes are closely associated with lncRNA, and the latter significantly affects GC patient prognosis To investigate whether lncRNAs regulate redox genes involved in tumor progression in GC patients, we first used Pearson correlation analysis to predict lncRNAs associated with RRGs. The results found 1911 lncRNAs with |Cor| > 0.4 and P < 0.001 for 487 RRGs in the TCGA database of GC patients, visualized as a Sankey plot (Fig. 1 A, Supplementary Table 1). Then, we performed a differential analysis of lncRNAs from GC patients with a threshold of logFC > 1 and P < 0 .05. The analysis yielded 3625 differentially expressed lncRNAs. A total of 3335 lncRNAs were upregulated, 290 were down-regulated, and the outcomes were visualized as a volcano plot (Fig. 1 B). Venn diagram showed 736 intersecting lncRNAs for redox-related lncRNAs (RRlncRNA) and differentially expressed lncRNAs (DElncRNA) for GC patients (Fig. 1 C). We ran univariate Cox regression on these DERRlncRNAs and obtained 17 DERRlncRNAs with significant prognostic significance. The results are presented as a forest plot (Fig. 1 D). Then, using LASSO to screen 17 suitable DERRlncRNAs as variables, multivariate Cox regression was carried out to prevent model overfitting (Fig. 1 E Supplementary Fig. 1). The results led to eight DERRlncRNAs signatures containing AC103702.2, AL138756.1, AL356417.2, CFAP61-AS1, RHPN1-AS1, CDK6-AS1, LINC02864, and AL355574.1. Risk score = AC103702.2 ∙ (-0.23) + AL138756.1 ∙ 1.03 + AL356417.2 ∙ 0.81 + CFAP61-AS1 ∙ 0.31 + RHPN1-AS1 ∙ (-0.70) + CDK6-AS1 ∙ 0.91 + LINC02864 ∙ 0.64 + AL355574.1 ∙ (-1.01). We correlated these lncRNAs with the key RRGs. The results showed that some lncRNAs were highly correlated with the RRGs, and the differences were statistically significant (Fig. 1 F, Supplementary Fig. 2). It demonstrates that RRGs may influence GC patients' prognosis by regulating lncRNAs. In the end, we conducted a progression-free survival (PFS) analysis of the two groups, which indicated that the prognosis of the high-risk patients survived significantly worse. (Fig. 1 G). The above results suggest that DERRlncRNAs may influence GC patients' prognosis through their underlying mechanisms. 2. Prognostic model constructed by 8 DERRlncRNAs has good predictive value in both the train and test groups In order to explore the scientific validity of the model we have constructed, we have carried out some validation of the model's performance. For testing and validation, we divided patients from the TCGA database 1:1 into the train and test groups. (Supplementary Table 2). We performed prognostic survival analysis on the eight DERRlncRNA-constructed models obtained from the above analyses. K-M survival analysis of the training group revealed that the overall survival (OS) of high-risk patients was inferior statistically (Fig. 2 A). AUC values of the ROC curves demonstrated that the probability of survival for GC patients at one, three, and five years was 0.749, 0.831, and 0.914, respectively. (Fig. 2 B). Additionally, a significant increase in the death of patients was accompanied by an increase in the risk score (Fig. 2 C, D). A heatmap was generated using R software, and the results showed the expression of eight DERRlncRNAs in high- and low-risk patients. (Fig. 2 E). Results of the same analysis in the training group against the external independent test group showed a clear distinction between the prognosis of patients (Fig. 2 F-J). In conclusion, these results suggest that DERRlncRNAs may influence GC patients' prognosis. Finally, we correlated the risk scores of the model with the TMN staging and clinicopathological staging of GC patients and plotted box plots to visualize. The results showed that the N stages of TMN staging were remarkably relevant to the model's risk scores, and clinicopathological staging correlated with risk scores (Fig. 2 K, L). These results suggest that a prognostic model constructed from eight DERRlncRNAs can significantly influence GC patients' prognosis and is highly predictive. 3 Value validation and comparison of prognostic models indicated higher predictive value To validate the scientific validity and advantages of a new prognostic model constructed with eight DERRlncRNAs, we employed a series of instruments to evaluate the predictive merit of the signature. In both univariate and multivariate analysis, the model's risk scores can be individually or jointly linked to the age and stage of disease of the patients, given the prognostic impact on the patients. Results for univariate Cox (Fig. 3 A) and multivariate Cox (Fig. 3 B) regression are presented as forest plots. The concordance index (C-index) result showed that model risk scores have greater predictive power than factors such as pathological stage, age, and grade (Fig. 3 C). To make our model more clinically applicable, we have used the DCA curve to assess the model's value. The outcome found that the net benefit of the Nomogram and model risk were significantly greater than the benefit of age, gender, classification, staging, and intervention treatment (Fig. 3 D). Subsequently, the prognostic Nomogram, including stage and risk score, was presented. The result showed the prediction for GC patients' survival probability at one, three, and five years and calibration curve results showed that it has good performance and can be a valuable tool for predicting GC patients' prognostic assessment (Fig. 3 E, F). We compared the signature with other studies' signatures for C-index, K-M survival curves, and ROC curves, and the results showed that our signature has a high predictive value compared to the signatures of Jiang et al. (Fig. 3 G-K, Supplementary Table 3). Finally, we conducted survival analysis for both groups of patients in stages I-II and III-IV, respectively. The outcome revealed that the survival prognosis of high-risk patients was significantly worse (Fig. 3 L, M). The above results indicated that our signature shows great validity in prognostic estimation. 4 GC patients with different risks differed in their immune function characteristics We used GO and KEGG functional and pathway enrichment analysis to investigate the specific mechanisms and functions of DERRlncRNA acting on GC patients. Firstly, we defined FoldChange > 1.5 and P < 0.05 as the threshold values and the differentially expressed genes (DEGs) of patients in high- and low-risk groups were screened in the TCGA database based on risk scores and analyzed by GO and KEGG. (Supplementary Table 4). The DEGs were significantly enriched in immune response and other immune-related signatures (Fig. 4 A, B). Recent research has shown that using antibodies against genetically altered proteins to treat cancer specifically kills cancer cells by targeting mutated protein fragments that act as surface antigens on cancer cells 27 – 29 . Based on this, we obtained tumor mutation statistics of GC patients from the TCGA database and collated them. Then, we analyzed both group patients' mutation frequencies and visualized the top 20 genes in terms of mutation frequency in a waterfall plot, showing that most of the genes of high-risk patients were mutated at a markedly lower frequency (Fig. 4 C, D). Interestingly, the CSMD1 and SPTA1 genes were mutated more frequently in high-risk patients. Studies have been published that link immunotherapy to immune checkpoint inhibitors (ICI) and TMB in patients with GC 30 , 31 . Next, a heatmap of immune function analysis was plotted, showing both group patients' scores for the ssGSEA score immune function genomic, with significant differences between high- and low-risk patients on immune pathways such as Type_Ⅱ_IFN response (Fig. 4 E). Then, we analyzed the TMB of patients in both groups and created violin plots, which revealed that the TMB of patients in low risk was significantly higher. (Fig. 4 F). High- and low-TMB and risk GC patients' survival analysis showed that lower tumor mutations had a poorer prognosis, and lower tumor mutation combined with high-risk patients had the worst prognosis (Fig. 4 G, H). We hypothesized this might be the case because clinical immunotherapy is more effective for high-TMB patients, while low-TMB patients continue to have poor prognoses. Finally, we performed TIDE scores for both group GC patients, and the result indicated that high-risk patients scored higher (Fig. 4 I). It was consistent with our hypothesis that high-risk patients had a higher potential for immune escape and had poorer outcomes with immunotherapy. The above results show that the model constructed by DERRlncRNAs was closely related to the tumor immune response in GC patients, suggesting that it can be used as a prognostic marker for clinical immunotherapy. 5. Significant differences in prognosis and immune microenvironment of GC patients after clustering according to consensus clustering of DERRlncRNAs To further explore the underlying mechanisms by which DERRlncRNAs affect the prognosis of GC patients, we used consensus clustering to classify patients in the TCGA database. "Consensus Cluster Plus" was employed to assess the clustering stability of the expression of DERRlncRNAs, with no crossover between GC samples in clusters 1 and 2 when k = 2. (Fig. 5 A, Supplementary Fig. 3, Supplementary Table 5). PCA plot demonstrated a remarkable difference between the two clusters, reflecting the specificity of the sample (Fig. 5 B). Meanwhile, the histogram illustrated that AC103702.2, RHPN1-AS1, and LINC02864 were distinctly differentially expressed in both clusters (Fig. 5 C). Subsequently, we carried out a K-M survival analysis for both clusters, which showed that cluster 2 had a significantly poorer prognosis (Fig. 5 D). In conjunction with the previous study, we planned to explore whether there are differences in the immune microenvironment and immune infiltration after clustering. Differential analysis of the immune cells of the two clusters was performed, and the violin plot result showed significant differences in CD8, T cells regulatory (Tregs) among the two clusters (Fig. 5 E). In addition, we found that the monocyte lineage also differed significantly between the two clusters (Fig. 5 F). Analysis of tumor microenvironment scores using the "estimate" package showed that cluster 2 had significantly higher ESTIMATESocre and Immunescore than cluster 1 (Fig. 5 G, H). Based on the above study, we performed a GSEA enrichment analysis of genes from cluster 2 patients, and the results showed significant enrichment in the KEGG T CELL RECEPTOR SIGNALING PATHWAY and other immune-related signaling pathways (Fig. 5 I-K). The above clustering analysis indicated that DERRlncRNAs might modulate the immune microenvironment or immune cell infiltration in GC patients, further influencing their prognosis. At the same time, it further demonstrates that our model can be used as a novel marker for clinical immunotherapy. Discussion GC is one of the most prevalent digestive malignancies, leading to massive deaths among cancer patients worldwide 1 . In recent decades, significant efforts have been dedicated to developing GC clinical treatments and interventions, including chemotherapy, radiotherapy, and immunotherapy 32 . The latest research identified lncRNAs as novel targets mediating tumorigenesis and immune suppression in the tumor microenvironment. 33 . In addition, ROS produced by various inflammatory cells in the tumor microenvironment, such as superoxide and hydrogen peroxide, regulate the functions that affect cancer cells and neighboring immune cells. 34 . Lin et al. found that hypoxia-induced HIF-1α/lncRNA-PMAN inhibits ferroptosis by promoting cytoplasmic translocation of ELAVL1 in peritoneal dissemination of gastric cancer 35 . In addition, lncRNA can upregulate HIF-1α expression by increasing the expression of HIF-1α at the transcriptional and post-transcriptional levels 36 . He et al. found that MSC-regulated lncRNA MACC1-AS1 promotes stemness and chemoresistance through fatty acid oxidation in gastric cancer 37 . Even though research has clarified the various functions of redox in the emergence of various cancers, it has primarily focused on the actions of a single redox gene. The characterization of cancers involving integrated RRGs, and the relationship and functions of RRlncRNAs in gastric cancer, are not yet fully understood. Based on this, a scientifically validated analysis of redox modification types and patterns in GC tumors will contribute to a deeper understanding of the role of redox in GC. Our study aims to establish a signature of RRlncRNAs to forecast GC patients' prognosis and promote more effective immunotherapeutic strategies. This study investigated RRlncRNAs, using Pearson's coefficient to predict lncRNAs involved in regulating redox homeostasis in GC cells (cor > 0.4, p < 0.001). A total of 736 DERRlncRNAs were generated by intersecting these lncRNAs with DElncRNAs from the TCGA database gene matrix. Cox and LASSO regressions were used to construct eight DERRlncRNAs prognostic models. We randomized patients in the TCGA database 1:1 into training and test groups to ensure the model's accuracy. We performed K-M survival analysis, ROC curves, median risk scores, and clinical staging analysis based on the eight DERRlncRNAs signatures constructed. The model's prognostic value was also validated and evaluated with other studies. The findings showed that the signature had a high predictive value, and the risk significantly correlated with the clinicopathological staging of GC patients and compared favorably with results from other studies. Consistent with previous studies, high ROS in tumors can alter the expression of oncogenes and oncogenes through epigenetic modifications, transcription factors, and noncoding RNA modifications that accumulate in cancer cells due to abnormal metabolism or oncogenic mutations, thus affecting the prognosis of tumor patients 38 , 39 . RHPN1-AS1 was discovered as a lncRNA in the signature of hypoxia-associated lnRNAs that predicted the prognosis and value of immunotherapy in hepatocellular carcinoma. This signature consisted of eight prognostic DERRlncRNAs (HCC) 16 . Another study showed that RHPN1-AS1 knockdown significantly inhibited uveal melanoma (UM) cell proliferation and migration in vitro and in vivo. RHPN1-AS1 may be an oncoRNA for UM and a candidate prognostic biomarker and target for new therapies for malignant UM 15 . Mao et al. developed a prognostic signature of gastric adenocarcinoma (GA) containing three lncRNAs, including AC103702.2 and three mRNAs, with better accuracy than the conventional TNM pathological staging system 40 . AL138756.1 has been reported as autophagy or cuproptosis-associated lncRNA predicting prognosis in colorectal or bladder cancer 41 , 42 . Yuan et al. reported that seven Platelets (PLT)-associated lncRNAs, including the biomarker AL356417.2, affect prognosis and immunotherapy. These lncRNAs could be potential biomarkers and therapeutic targets for patients with GC 43 . According to research, CFAP61-AS1 is a glycolysis-associated lncRNA with the potential to predict prognosis in GC and serve as a new biological target for clinical immunotherapy when combined with the traits of other lncRNAs 44 . Yang et al. identified CDK6-AS1, a lncRNA cell cycle protein-dependent kinase 6, as a potential biomarker candidate for poor prognosis in GC and a predictor of chemotherapeutic drug sensitivity 45 . According to Geng et al., the expression levels of five ferroptosis-related lncRNAs, including AL355574.1 (which was experimentally validated to confirm its expression levels in GC), can be used to predict GC prognosis and may aid in the selection of the most promising therapeutic options for GC patients 46 . The function of lncRNA in the development of gastric cancer has been extensively studied in the last decade and has received much attention as a biomarker for early screening, diagnosis, treatment, prognosis, and drug response due to its high specificity and sensitivity 47 . Redox reactions play a crucial role in the pathological progression of cancer 34 , 48 . Tumor cells regulate a complex system to control ROS production and participate in vivo responses that regulate tumor redox homeostasis and affect various stromal cells associated with tumor metabolism and immunity 49 . Takahashi et al. reported that NRF2 directly controls TRPA1 expression, providing an orthogonal mechanism of protection against oxidative stress and a typical ROS neutralization mechanism, revealing oxidative stress defense options that could be used for targeted cancer therapy 50 . Cancer cells in advanced tumors often exhibit multiple genetic alterations and high oxidative stress, which may be preferentially eliminated by pharmacological ROS damage. Modulating redox-regulated responses in tumor cells may be an active strategy to reduce or eliminate these cells 8 . Recent studies have shown that redox homeostasis is regulated by lncRNA 51 , 52 . A growing number of researchers are attempting to pinpoint the crucial lncRNA redox regulatory networks. Chen et al. reported that the Ablation of long noncoding RNA MALAT1 activates the antioxidant pathway and alleviates sepsis in mice 53 . Lin et al. reported that Hypoxia-induced HIF-1α/lncRNA-PMAN inhibits ferroptosis by promoting the cytoplasmic translocation of ELAVL1 in peritoneal dissemination from GC 35 . Here, we report for the first time a novel RRlncRNAs marker for predicting prognosis and immune response in the GC population. The present study contributes a to further understanding of lncRNAs and their interactions with redox homeostasis, providing potential targets for future therapies. Recent studies have reported how dysregulation of metabolites such as ROS modulates redox balance and the cancer cell process and how metabolic microRNA and ncRNA are transmitted to the intercellular stroma through exosome mediators to shape the cancer microenvironment 54 . A study found that redox metabolizing enzyme glutathione peroxidase 2 is a metabolic promoter of the tumor immune microenvironment and immune checkpoint inhibitor response 55 . Therefore, an exploration focusing on redox's detailed mechanisms and functions in cancer will help pave the way for identifying redox induction as a promising therapeutic approach. By boosting the patient's immune system, immunotherapy has proven successful in making cancer curable in various malignancies. Significant advances in immune checkpoint inhibitors (ICIs) have begun to transform clinical practice in the treatment and prognosis of gastric cancer, and combination therapies with other modalities (e.g., targeted therapies) are promising to move immunotherapy into the front line. Immune-related biomarkers have a high predictive value in immunotherapy for gastric cancer 56 . The TMEscore was found to have good diagnostic value for patients with metastatic gastric cancer (mGC). High tumor mutational load (TMB-H) is linked to increased objective remission rates (ORR) and progression-free survival for some cancers treated with immunotherapy (PFS). The latest GC clinical trial identified high TMB as a possible predictive marker for OS in AGC patients receiving toripalimab monotherapy 57 . TIDE is a computational approach to predicting immunotherapy that mimics tumor immune evasion mechanisms 58 . Immune checkpoint inhibitors programmed cell death 1/programmed death ligand 1 (PD-1/PD-L1) antibodies, cytotoxic T lymphocyte-associated protein 4 (CTLA-4) antibodies, and chimeric antigen receptor T (CAR-T) in ACT. These therapeutic strategies have significant anti-tumor efficacy in solid and hematological tumors while targeting other immune cells offers a new direction for immunotherapy in GC 59 . In this study, patients in the high-risk group were significantly enriched for genes in immune-related pathways. Low-risk patients possessed a better TMB, possibly due to their benefit from clinical immunotherapy, resulting in lower risk values. TIDE scores were higher in high-risk patients than in low-risk patients. Analysis of patients after clustering showed that monocyte lineages were significantly higher in patients in cluster 2 than in cluster 1. ESTIMATE and immune scores were significantly higher in cluster 2 than in cluster 1. Gene GSEA analysis in cluster 2 was enriched in immune-related pathways such as T and NK cells. The above results suggest that redox-related lncRNA markers could provide potential clues for patients to select more effective anti-tumor immunotherapies. However, further validation is needed to understand our model's utility in predicting immunotherapy response in GC tumors. Our study also has some restrictions. The report is primarily composed of integrated bioinformatics. There are not enough validated experiments to validate these findings. The accuracy of related lncRNA signatures in GC patients' prognosis and immune regulation remains vital in clinical practice. If these issues are addressed, this study will provide a new marker and theoretical basis for GC immunotherapy. Methods 1. Data Resources We downloaded transcriptomic, mutational, and clinical data from the TCGA database ( https://portal.gdc.cancer.gov/ ). Furthermore, the workflow type belonged to the HTSeq-FPKM format with log2 normalization. In order to transfer annotate, the human gene transfer format (gtf) files were searched in the Ensembl database ( http://asia.ensembl.org/index.html ). Besides, the redox-related gene sets were searched in MSigDB ( https://www.gsea-msigdb.org/gsea/msigdb ) and GeneCards database ( https://www.genecards.org/ ) using "redox" as a keyword. Finally, 487 redox-related genes (RRGs) were obtained. 2. Screening Conditions for LncRNAs The R package "limma" was used for the analysis of the lncRNA matrix with ∣logFC∣>1 and FDR < 0.05 as thresholds to determine differentially expressed lncRNA (DElncRNA). Also, expression of redox-related genomes (RRGs) was extracted, and the redox-related lncRNAs (RRlncRNAs) were identified by Pearson Test (∣Cor∣ > 0.4, p. adj < 0.001). Finally, the Sankey plot was drawn using the "ggalluvial" package to visualize the co-expression relationship between lncRNAs and RRGs in GC patients. 3. Construction of Risk Model for Differentially Expressed Redox-related LncRNAs (DERRlnRNAs). The expression matrix of DERRlncRNAs in GC patients was merged with their clinical survival matrix. Univariate and multivariate Cox regressions were conducted on DERRlncRNAs. The DERRlncRNAs relevant to the prognosis of GC patients were finalized using LASSO regression to prevent overfitting, with P < 0.05 as significant. Then, the GC samples from TCGA were randomly and equally split into train and test groups. Median risk scores and survival analysis were performed for the train and test groups, respectively. The risk score formula is shown below: $$\text{R}\text{i}\text{s}\text{k} \text{s}\text{c}\text{o}\text{r}\text{e} =\sum _{i=1}^{n}{Coef}_{i}*{x}_{i}$$ \({Coef}_{i}\) represents coefficient, \({x}_{i}\) represents the normalized count for each hub lncRNA. According to the median risk value of the training group, GC patients were divided into high- and low-risk groups. 4. Validation of the Survival-Predicting Signature. Kaplan-Meier (K-M) survival analysis was performed using the R packages "survival" and "survminer" to test the predictive power of the model for survival. Median risk values were assessed using the "ggrisk" package. We combined clinical data containing age, gender, etc., with the patient's risk scores and removed samples lacking clinical data. Multivariate ROC curves were then plotted to validate and compare the efficacy of the developed signature with prognostic factors. The area under the ROC curve (AUC) was employed to evaluate the signature's accuracy, and this signature was also compared with three published models for predicting prognosis in GC patients 60 – 62 . Finally, the Nomogram plot was plotted with the "rms" package, and the accuracy of Nomogram plots was measured using calibration curves. "ggDCA" and "rms" packages were used to construct DCA curves and C-index plots, respectively. 5. Consensus Clustering When GC patients were clustered using the "ConsensusClusterPlus" package, the strongest separation occurred between groups of patients when k = 2. 6. Gene Set Enrichment Analysis (GSEA), GO, and KEGG Enrichment According to the consensus clustering algorithm outcomes, the transcriptome files of all samples were divided into two clusters, A and B. Enrichment analysis of subgroup data in GSEA (version 4.3.2) to investigate the enrichment of immune pathways between the two groups. The P. adj < 0.05 was considered significant in GO and KEGG analysis. 7. Tumor Mutation Burden and The Immune Microenvironment The "maftools" package was used to construct a waterfall map of gene mutations. The "CIBERSORT" "estimate" packages were used to analyze the differences between the two clusters' immune microenvironment and infiltration. Finally, the "ggpubr" was used to plot TMB, TIDE, and other immune scores for different groups of patients. TIDE analysis tool for GC patients ( http://tide.dfci.harvard.edu/ ). 8. Statistical analysis Data was collated via PERL and analyzed via the R programming package. P < 0.05 was considered to be statistically significant. Declarations Author contributions statement Conception and design: H.W., M.L.; Data analysis and interpretation: G.P., D.W., L.S., W.L.; Manuscript writing: H.W., G.P., M.H.; Final approval of manuscript: All authors. All authors read and approved the final manuscript. Funding The work was partially supported by the Natural Science Foundation of Bengbu Medical College (2022byfy002). Conflicts of Interest The authors declare no conflict of interest. Availability of Data and Materials The datasets used and analyzed during the current study available from the corresponding author on reasonable request. References Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71 , 209-249, doi:10.3322/caac.21660 (2021). Zheng, L., Wang, L., Ajani, J. & Xie, K. Molecular basis of gastric cancer development and progression. Gastric Cancer : Official Journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association 7 , 61-77 (2004). Wang, X. et al. Bmi-1 regulates stem cell-like properties of gastric cancer cells via modulating miRNAs. Journal of Hematology & Oncology 9 , 90, doi:10.1186/s13045-016-0323-9 (2016). Gan, L. et al. 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Journal of Oncology 2021 , 6718443, doi:10.1155/2021/6718443 (2021). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.xlsx SupplementaryTable2.xlsx SupplementaryTable3.xlsx SupplementaryTable4.xlsx SupplementaryTable5.xlsx SupplementaryFigure1.docx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-2843204","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":199947524,"identity":"256714d4-0e85-4710-afd5-bc4abdda7877","order_by":0,"name":"Guisen Peng","email":"","orcid":"","institution":"Bengbu Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guisen","middleName":"","lastName":"Peng","suffix":""},{"id":199947525,"identity":"11aa9d3a-adad-4d69-9c55-748ef01b211a","order_by":1,"name":"Di Wu","email":"","orcid":"","institution":"Bengbu Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Wu","suffix":""},{"id":199947526,"identity":"8a71685c-bc53-46f0-98be-559e52c894a7","order_by":2,"name":"Lidong Shan","email":"","orcid":"","institution":"Bengbu Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lidong","middleName":"","lastName":"Shan","suffix":""},{"id":199947527,"identity":"f87d8a3e-d3cc-48f5-9311-4eb0b81f5b78","order_by":3,"name":"Weicheng Lu","email":"","orcid":"","institution":"First Affiliated Hospital of Bengbu Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Weicheng","middleName":"","lastName":"Lu","suffix":""},{"id":199947528,"identity":"ed5df51c-bf26-4ab0-a22f-245fd263471d","order_by":4,"name":"Mingjie Hu","email":"","orcid":"","institution":"Bengbu Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingjie","middleName":"","lastName":"Hu","suffix":""},{"id":199947529,"identity":"3f145406-1a04-4c08-b948-8906a9f12c04","order_by":5,"name":"Mulin Liu","email":"","orcid":"","institution":"First Affiliated Hospital of Bengbu Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mulin","middleName":"","lastName":"Liu","suffix":""},{"id":199947530,"identity":"4d21337e-747a-418c-a495-29414383a790","order_by":6,"name":"Huazhang Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDACCQY2EMXDxt7/8EFCRQ3xWmT4ec4wGzw4c4x4LTaSM3LYJB+2MBPWYXC7/dmDnztqeQwO5B6rSGxgY+Bv707Ar+XOgXTD3jPHgVrOpd1I3CHDIHHm7Aa8WsxuJByT4G07xmNwsMHsRuIZNgYDiVxCWhLbJP+CtBxmMCtIbGMmRksymzRvWw2PZBuPGQNRWuxvpLFJy7Yd4OHnYUuWSDhzjIegXyRnpD+TfNtWZ88m//jgxx8VNXL87b34tUDBYTiLhxjlIFBHrMJRMApGwSgYiQAA1bVLeosAW3oAAAAASUVORK5CYII=","orcid":"","institution":"Bengbu Medical College","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Huazhang","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2023-04-21 01:59:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2843204/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2843204/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":37094437,"identity":"a7b89f65-8519-4fa3-9218-c2b035d98c39","added_by":"auto","created_at":"2023-05-16 15:04:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1626053,"visible":true,"origin":"","legend":"\u003cp\u003eDERRlncRNA significantly correlates with the prognosis of GC patients based on the TCGA database. (A) 487 RRGs with 1911 LncRNAs in the Sankey diagram, each linkage represents |Cor| \u0026gt; 0.4, P \u0026gt; 0.001 from GC patients in the TCGA database. (B) Volcano plot of DElncRNAs in TCGA database of GC patients, bounded by logFC = 1, P = 0.05, red dots represent upregulated lncRNAs, and blue dots represent down-regulated lncRNAs (C) Venn diagram of RRlncRNAs producing intersections with DElncRNAs, with 736 intersecting lncRNAs. (D) From left to right, the lncRNA forest plots obtained by one-way Cox analysis are the lncRNA name p-value risk factors and their 95% confidence intervals. (E) lambda plot of the LASSO regression with decreasing compression parameters and increasing absolute values of the coefficients as λ. (F) Heatmap of correlation between eight prognosis-related DERRLncRNAs and RRGs after multifactorial Cox regression and LASSO regression, \"*\" represents P \u0026lt; 0.05, \"**\" represents P \u0026lt; 0.01, \"***\" represents P \u0026lt; 0.001, the color in the box about red means the stronger correlation. (G) K-M survival curve analysis for high- and low-risk patients.\u003c/p\u003e","description":"","filename":"FIGURE1.png","url":"https://assets-eu.researchsquare.com/files/rs-2843204/v1/7d38f2376247199a0212b37d.png"},{"id":37094438,"identity":"1ec362b0-08be-4d7b-a33f-05d7901bf6fd","added_by":"auto","created_at":"2023-05-16 15:04:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":455678,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic models constructed with 8 DERRlncRNAs significantly affect the prognosis of GC patients in the TCGA train and test groups. (A, F) K-M survival curves for high- and low-risk groups in the training and testing sets. (B, G) ROC curves for sensitivity and specificity of risk models used to predict 1-, 3- and 5-year survival in the training set and testing set. (C-D, H-I) Scatter plot of DERRlncRNAs risk scores and patient survival, with red and blue dots representing high- and low-risk patients in the training set and testing set. Risk scores correlate with N -stage (K) and clinicopathological stage (L) for GC patients in the TCGA dataset.\u003c/p\u003e","description":"","filename":"FIGURE2.png","url":"https://assets-eu.researchsquare.com/files/rs-2843204/v1/016e03a69474dfdbbf50686c.png"},{"id":37094439,"identity":"d548cf21-2da6-410f-84fb-48d2c84b5822","added_by":"auto","created_at":"2023-05-16 15:04:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":404959,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the predictive value of a model based on 8 DERRlncRNAs.\u003cstrong\u003e \u003c/strong\u003eForest plots from univariate Cox (A) and multivariate Cox (B) regression analyses. (C) C-index analysis of patients' clinical traits, with different colored lines representing different traits and risk scores having the highest predictive value. (D) DCA curve analysis of patient clinical traits, Nomogram, and model risk values have the highest clinical benefit rates (E, F) The Nomogram plots and corresponding calibration curves for the patients' clinical traits and risk scores had survival rates of 0.628, 0.217,0.101 at 1, 3, and 5 years, respectively, while the three calibration curves had a good fit. (G-K) Comparison of C-index, K-M, and ROC curves with Jiang, Feng, and Zhao's signatures. C-index values are shown at the top of the bar chart, and AUC values are shown at the bottom right of the ROC curve. K-M survival analysis of patients at clinicopathological stage I-II (L), survival analysis of patients at stage III-IV (M).\u003c/p\u003e","description":"","filename":"FIGURE3.png","url":"https://assets-eu.researchsquare.com/files/rs-2843204/v1/470f42c125b3af3f946462b7.png"},{"id":37096342,"identity":"5bc1245d-9a2e-41b9-bbe0-b8cd11fe028a","added_by":"auto","created_at":"2023-05-16 15:20:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":519373,"visible":true,"origin":"","legend":"\u003cp\u003eImmunological characteristics of patients in the high- and low-risk groups of the model.GO (A) and KEGG (B) enrichment analysis of DEGs in patients in high- and low-risk groups, horizontal coordinates represent enriched gene counts, vertical coordinates are gene sets, the color of the dots represents the class of the set, and the size represents the P value. CC (Cellular Component), BP (Biological Process), MF (Molecular Function). Waterfall plot of mutations in high- (C) and low-risk (D) groups. (E) Heatmap of the immune function set for high and low-risk groups. * represents P \u0026lt; 0.05, ** represents P \u0026lt; 0.01, *** represents P \u0026lt; 0.001. (F) Violin plot of TMB scores for high- and low-risk groups. (G, H) K-M survival analysis of high-risk, low-risk, high-TMB, and low-TMB GC patients. (I) TIDE scores for high- and low-risk groups.\u003c/p\u003e","description":"","filename":"FIGURE4.png","url":"https://assets-eu.researchsquare.com/files/rs-2843204/v1/450c191e9ad6a3233e8f93e3.png"},{"id":37095252,"identity":"336082da-4660-4e78-aa1b-16bae574cb05","added_by":"auto","created_at":"2023-05-16 15:12:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":705020,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant differences in the tumor microenvironment and tumor immune infiltration in GC patients based on DERRlncRNAs clustering. (A) Consensus clustering of GC patients according to DERRlncRNAs based on TCGA datasets. (B) The PCA plot displayed a significant difference between the two clusters of samples. (C) Histogram of model DERRlncRNAs expression in two clusters of patients, \"*\" meaning shown in Figure 4. (D) K-M survival analysis of two clusters of patients, and patients in cluster 2 have a worse prognosis. (E, F) Violin diagram of the analysis of immune cell differences in two clusters of patients. The differences in T cells CD8, T cells regulatory (Tregs), and monocyte lineage were statistically significant (p\u0026lt;0.05). (G, H) Box plot of ESTIMATEScore of the immune microenvironment of patients in both clusters, with significantly higher ESTIMATEScore and ImmuneSocre in cluster 2. (I-K) GSEA of cluster 2.\u003c/p\u003e","description":"","filename":"FIGURE5.png","url":"https://assets-eu.researchsquare.com/files/rs-2843204/v1/6c06a137675b3bfbd59d5b12.png"},{"id":40564626,"identity":"8efc803c-b4db-473e-be57-606db36c03c6","added_by":"auto","created_at":"2023-07-25 20:22:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2500385,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2843204/v1/af15bd56-366f-4390-8e8a-73a1fd93e298.pdf"},{"id":37096983,"identity":"966dafa5-0f98-4429-9395-ef870824a273","added_by":"auto","created_at":"2023-05-16 15:28:45","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":411998,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2843204/v1/5b52a806f883772277a81400.xlsx"},{"id":37095247,"identity":"937074d8-97fa-46ba-b886-a7ff945331e7","added_by":"auto","created_at":"2023-05-16 15:12:45","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":26916,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2843204/v1/e0bb4a3e3f43e05b47cbadc4.xlsx"},{"id":37096344,"identity":"4711319c-437a-4231-b08c-0a6e9759b2aa","added_by":"auto","created_at":"2023-05-16 15:20:45","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":42533,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2843204/v1/d36cd4404b15e244a65a452b.xlsx"},{"id":37095249,"identity":"3cad5c8a-61f2-462d-80c3-2bd268b2c73f","added_by":"auto","created_at":"2023-05-16 15:12:45","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":57908,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2843204/v1/78eeda765bd511aff25b5746.xlsx"},{"id":37095251,"identity":"0bf17fa9-6031-41e7-bee1-295b435041c6","added_by":"auto","created_at":"2023-05-16 15:12:45","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":12856,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2843204/v1/940e61b1d5ec6fc69f291fda.xlsx"},{"id":37094447,"identity":"38ab4487-61c3-498b-a808-629a0bfd7bfc","added_by":"auto","created_at":"2023-05-16 15:04:45","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":895750,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-2843204/v1/7c392ef899d5e07b368ce61e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification and validation of a novel redox- related differentially expressed lncRNA prognostic signature for predicting clinical immunotherapy response in gastric cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGC is a prevalent worldwide malignancy, with the fifth-highest incidence and fourth-highest mortality rate\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. GC development possesses complex molecular mechanisms\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Recently, studies have identified several potential prognostic and immunological biomarkers of GC, which facilitate the diagnosis and treatment of GC. Nevertheless, the prognostic survival of advanced GC is still not promising. Therefore, identifying biomarkers with higher sensitivity and specificity for GC treatment is essential\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRedox homeostasis is the balance of redox equivalents, a critical reaction process in the physiological and pathological processes of the body. Studies have shown that the production and elimination of redox equivalents, such as reactive oxygen species (ROS) like superoxide (O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), is unbalanced, leading to a certain level of oxidative stress in vivo\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. For example, cancer cells have a higher production of ROS than normal cells under the premise of altered mitochondrial function, leading to an imbalance in cellular redox levels\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Thus, it is necessary to develop in vivo defense systems to prevent an imbalance in cellular redox levels and inhibit the development of cancer cells\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Therefore, studies targeting ROS production, depletion, and in vivo antioxidant capacity in GC patients may lead to therapeutic benefits.\u003c/p\u003e \u003cp\u003eLong noncoding RNAs (lncRNAs) are a new class of RNA molecules longer than 200 nt and are important components of the noncoding genome\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Numerous studies have shown that lncRNAs are involved in various biological processes, including DNA methylation, histone modification, post-transcriptional regulation of RNA, and translation regulation of proteins, and they regulate various physiological and pathological processes\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In addition, several other studies have found that lncRNAs play oncogenic roles in various malignancies, including GC. Additionally, recent studies revealed that RHPN1-AS1 affects the prognostic characteristics of patients with hepatocellular carcinoma by acting as a facilitator in some cancers as a redox-related lncRNA and combining with other hypoxia-related lncRNA\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Therefore, targeting lncRNAs involved in regulating redox function, such as RHPN1-AS1, has great potential in diagnosing and treating GC.\u003c/p\u003e \u003cp\u003eCurrently, curative resection is the standard of care for GC. Chemotherapy with platinum compounds, fluoropyrimidines, docetaxel, and other drugs is the standard of care for unresectable GC\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, recent groundbreaking studies have targeted immune checkpoint inhibitors (ICIs). Anti-cytotoxic T-lymphocyte antigen 4 (CTLA4) mAb (ipilimumab) and anti-programmed death-1 (PD-1) mAb (nivolumab and pembrolizumab), among others, have emerged as the latest targets for immunotherapy, pioneering a new path in cancer treatment paradigms\u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Inhibition of the PD-1/programmed death ligand 1 (PD-L1) axis with ICI is a novel therapeutic modality for treating advanced GC\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Although PD-1 mAb holds great therapeutic promise for advanced GC, there are many shortcomings. This makes the development of new therapeutic markers targeting ICI necessary and critical.\u003c/p\u003e \u003cp\u003eBased on the above research basis, we plan to demonstrate the involvement of redox-related lncRNA in the immune microenvironment of GC patients, thus providing a theoretical basis for novel clinical immunotherapy for GC.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003e1. Redox-related genes are closely associated with lncRNA, and the latter significantly affects GC patient prognosis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo investigate whether lncRNAs regulate redox genes involved in tumor progression in GC patients, we first used Pearson correlation analysis to predict lncRNAs associated with RRGs. The results found 1911 lncRNAs with |Cor| \u0026gt; 0.4 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for 487 RRGs in the TCGA database of GC patients, visualized as a Sankey plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, Supplementary Table\u0026nbsp;1). Then, we performed a differential analysis of lncRNAs from GC patients with a threshold of logFC\u0026thinsp;\u0026gt;\u0026thinsp;1 and P\u0026thinsp;\u0026lt;\u0026thinsp;0 .05. The analysis yielded 3625 differentially expressed lncRNAs. A total of 3335 lncRNAs were upregulated, 290 were down-regulated, and the outcomes were visualized as a volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Venn diagram showed 736 intersecting lncRNAs for redox-related lncRNAs (RRlncRNA) and differentially expressed lncRNAs (DElncRNA) for GC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). We ran univariate Cox regression on these DERRlncRNAs and obtained 17 DERRlncRNAs with significant prognostic significance. The results are presented as a forest plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Then, using LASSO to screen 17 suitable DERRlncRNAs as variables, multivariate Cox regression was carried out to prevent model overfitting (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE Supplementary Fig.\u0026nbsp;1). The results led to eight DERRlncRNAs signatures containing AC103702.2, AL138756.1, AL356417.2, CFAP61-AS1, RHPN1-AS1, CDK6-AS1, LINC02864, and AL355574.1.\u003c/p\u003e \u003cp\u003eRisk score\u0026thinsp;=\u0026thinsp;AC103702.2 ∙ (-0.23)\u0026thinsp;+\u0026thinsp;AL138756.1 ∙ 1.03\u0026thinsp;+\u0026thinsp;AL356417.2 ∙ 0.81\u0026thinsp;+\u0026thinsp;CFAP61-AS1 ∙ 0.31\u0026thinsp;+\u0026thinsp;RHPN1-AS1 ∙ (-0.70)\u0026thinsp;+\u0026thinsp;CDK6-AS1 ∙ 0.91\u0026thinsp;+\u0026thinsp;LINC02864 ∙ 0.64\u0026thinsp;+\u0026thinsp;AL355574.1 ∙ (-1.01).\u003c/p\u003e \u003cp\u003eWe correlated these lncRNAs with the key RRGs. The results showed that some lncRNAs were highly correlated with the RRGs, and the differences were statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, Supplementary Fig.\u0026nbsp;2). It demonstrates that RRGs may influence GC patients' prognosis by regulating lncRNAs. In the end, we conducted a progression-free survival (PFS) analysis of the two groups, which indicated that the prognosis of the high-risk patients survived significantly worse. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). The above results suggest that DERRlncRNAs may influence GC patients' prognosis through their underlying mechanisms.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003e2. Prognostic model constructed by 8 DERRlncRNAs has good predictive value in both the train and test groups\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn order to explore the scientific validity of the model we have constructed, we have carried out some validation of the model's performance. For testing and validation, we divided patients from the TCGA database 1:1 into the train and test groups. (Supplementary Table\u0026nbsp;2). We performed prognostic survival analysis on the eight DERRlncRNA-constructed models obtained from the above analyses. K-M survival analysis of the training group revealed that the overall survival (OS) of high-risk patients was inferior statistically (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). AUC values of the ROC curves demonstrated that the probability of survival for GC patients at one, three, and five years was 0.749, 0.831, and 0.914, respectively. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Additionally, a significant increase in the death of patients was accompanied by an increase in the risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D). A heatmap was generated using R software, and the results showed the expression of eight DERRlncRNAs in high- and low-risk patients. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Results of the same analysis in the training group against the external independent test group showed a clear distinction between the prognosis of patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF-J).\u003c/p\u003e \u003cp\u003eIn conclusion, these results suggest that DERRlncRNAs may influence GC patients' prognosis. Finally, we correlated the risk scores of the model with the TMN staging and clinicopathological staging of GC patients and plotted box plots to visualize. The results showed that the N stages of TMN staging were remarkably relevant to the model's risk scores, and clinicopathological staging correlated with risk scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK, L). These results suggest that a prognostic model constructed from eight DERRlncRNAs can significantly influence GC patients' prognosis and is highly predictive.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e3 Value validation and comparison of prognostic models indicated higher predictive value\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo validate the scientific validity and advantages of a new prognostic model constructed with eight DERRlncRNAs, we employed a series of instruments to evaluate the predictive merit of the signature. In both univariate and multivariate analysis, the model's risk scores can be individually or jointly linked to the age and stage of disease of the patients, given the prognostic impact on the patients. Results for univariate Cox (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and multivariate Cox (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) regression are presented as forest plots. The concordance index (C-index) result showed that model risk scores have greater predictive power than factors such as pathological stage, age, and grade (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). To make our model more clinically applicable, we have used the DCA curve to assess the model's value. The outcome found that the net benefit of the Nomogram and model risk were significantly greater than the benefit of age, gender, classification, staging, and intervention treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Subsequently, the prognostic Nomogram, including stage and risk score, was presented. The result showed the prediction for GC patients' survival probability at one, three, and five years and calibration curve results showed that it has good performance and can be a valuable tool for predicting GC patients' prognostic assessment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, F). We compared the signature with other studies' signatures for C-index, K-M survival curves, and ROC curves, and the results showed that our signature has a high predictive value compared to the signatures of Jiang et al. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG-K, Supplementary Table\u0026nbsp;3). Finally, we conducted survival analysis for both groups of patients in stages I-II and III-IV, respectively. The outcome revealed that the survival prognosis of high-risk patients was significantly worse (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL, M). The above results indicated that our signature shows great validity in prognostic estimation.\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e4 GC patients with different risks differed in their immune function characteristics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe used GO and KEGG functional and pathway enrichment analysis to investigate the specific mechanisms and functions of DERRlncRNA acting on GC patients. Firstly, we defined FoldChange\u0026thinsp;\u0026gt;\u0026thinsp;1.5 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the threshold values and the differentially expressed genes (DEGs) of patients in high- and low-risk groups were screened in the TCGA database based on risk scores and analyzed by GO and KEGG. (Supplementary Table\u0026nbsp;4). The DEGs were significantly enriched in \u003cem\u003eimmune response\u003c/em\u003e and other immune-related signatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). Recent research has shown that using antibodies against genetically altered proteins to treat cancer specifically kills cancer cells by targeting mutated protein fragments that act as surface antigens on cancer cells\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Based on this, we obtained tumor mutation statistics of GC patients from the TCGA database and collated them. Then, we analyzed both group patients' mutation frequencies and visualized the top 20 genes in terms of mutation frequency in a waterfall plot, showing that most of the genes of high-risk patients were mutated at a markedly lower frequency (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). Interestingly, the CSMD1 and SPTA1 genes were mutated more frequently in high-risk patients. Studies have been published that link immunotherapy to immune checkpoint inhibitors (ICI) and TMB in patients with GC\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Next, a heatmap of immune function analysis was plotted, showing both group patients' scores for the ssGSEA score immune function genomic, with significant differences between high- and low-risk patients on immune pathways such as \u003cem\u003eType_Ⅱ_IFN response\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Then, we analyzed the TMB of patients in both groups and created violin plots, which revealed that the TMB of patients in low risk was significantly higher. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). High- and low-TMB and risk GC patients' survival analysis showed that lower tumor mutations had a poorer prognosis, and lower tumor mutation combined with high-risk patients had the worst prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG, H). We hypothesized this might be the case because clinical immunotherapy is more effective for high-TMB patients, while low-TMB patients continue to have poor prognoses. Finally, we performed TIDE scores for both group GC patients, and the result indicated that high-risk patients scored higher (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI). It was consistent with our hypothesis that high-risk patients had a higher potential for immune escape and had poorer outcomes with immunotherapy. The above results show that the model constructed by DERRlncRNAs was closely related to the tumor immune response in GC patients, suggesting that it can be used as a prognostic marker for clinical immunotherapy.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003e5. Significant differences in prognosis and immune microenvironment of GC patients after clustering according to consensus clustering of DERRlncRNAs\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further explore the underlying mechanisms by which DERRlncRNAs affect the prognosis of GC patients, we used consensus clustering to classify patients in the TCGA database. \"Consensus Cluster Plus\" was employed to assess the clustering stability of the expression of DERRlncRNAs, with no crossover between GC samples in clusters 1 and 2 when k\u0026thinsp;=\u0026thinsp;2. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, Supplementary Fig.\u0026nbsp;3, Supplementary Table\u0026nbsp;5). PCA plot demonstrated a remarkable difference between the two clusters, reflecting the specificity of the sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Meanwhile, the histogram illustrated that AC103702.2, RHPN1-AS1, and LINC02864 were distinctly differentially expressed in both clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Subsequently, we carried out a K-M survival analysis for both clusters, which showed that cluster 2 had a significantly poorer prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). In conjunction with the previous study, we planned to explore whether there are differences in the immune microenvironment and immune infiltration after clustering. Differential analysis of the immune cells of the two clusters was performed, and the violin plot result showed significant differences in CD8, T cells regulatory (Tregs) among the two clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). In addition, we found that the monocyte lineage also differed significantly between the two clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Analysis of tumor microenvironment scores using the \"estimate\" package showed that cluster 2 had significantly higher ESTIMATESocre and Immunescore than cluster 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG, H). Based on the above study, we performed a GSEA enrichment analysis of genes from cluster 2 patients, and the results showed significant enrichment in the \u003cem\u003eKEGG T CELL RECEPTOR SIGNALING PATHWAY\u003c/em\u003e and other immune-related signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI-K). The above clustering analysis indicated that DERRlncRNAs might modulate the immune microenvironment or immune cell infiltration in GC patients, further influencing their prognosis. At the same time, it further demonstrates that our model can be used as a novel marker for clinical immunotherapy.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGC is one of the most prevalent digestive malignancies, leading to massive deaths among cancer patients worldwide \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In recent decades, significant efforts have been dedicated to developing GC clinical treatments and interventions, including chemotherapy, radiotherapy, and immunotherapy\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The latest research identified lncRNAs as novel targets mediating tumorigenesis and immune suppression in the tumor microenvironment. \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In addition, ROS produced by various inflammatory cells in the tumor microenvironment, such as superoxide and hydrogen peroxide, regulate the functions that affect cancer cells and neighboring immune cells.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Lin et al. found that hypoxia-induced HIF-1α/lncRNA-PMAN inhibits ferroptosis by promoting cytoplasmic translocation of ELAVL1 in peritoneal dissemination of gastric cancer \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In addition, lncRNA can upregulate HIF-1α expression by increasing the expression of HIF-1α at the transcriptional and post-transcriptional levels\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. He et al. found that MSC-regulated lncRNA MACC1-AS1 promotes stemness and chemoresistance through fatty acid oxidation in gastric cancer\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Even though research has clarified the various functions of redox in the emergence of various cancers, it has primarily focused on the actions of a single redox gene. The characterization of cancers involving integrated RRGs, and the relationship and functions of RRlncRNAs in gastric cancer, are not yet fully understood. Based on this, a scientifically validated analysis of redox modification types and patterns in GC tumors will contribute to a deeper understanding of the role of redox in GC. Our study aims to establish a signature of RRlncRNAs to forecast GC patients' prognosis and promote more effective immunotherapeutic strategies.\u003c/p\u003e \u003cp\u003eThis study investigated RRlncRNAs, using Pearson's coefficient to predict lncRNAs involved in regulating redox homeostasis in GC cells (cor \u0026gt; 0.4, p \u0026lt; 0.001). A total of 736 DERRlncRNAs were generated by intersecting these lncRNAs with DElncRNAs from the TCGA database gene matrix. Cox and LASSO regressions were used to construct eight DERRlncRNAs prognostic models. We randomized patients in the TCGA database 1:1 into training and test groups to ensure the model's accuracy. We performed K-M survival analysis, ROC curves, median risk scores, and clinical staging analysis based on the eight DERRlncRNAs signatures constructed. The model's prognostic value was also validated and evaluated with other studies. The findings showed that the signature had a high predictive value, and the risk significantly correlated with the clinicopathological staging of GC patients and compared favorably with results from other studies. Consistent with previous studies, high ROS in tumors can alter the expression of oncogenes and oncogenes through epigenetic modifications, transcription factors, and noncoding RNA modifications that accumulate in cancer cells due to abnormal metabolism or oncogenic mutations, thus affecting the prognosis of tumor patients\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. RHPN1-AS1 was discovered as a lncRNA in the signature of hypoxia-associated lnRNAs that predicted the prognosis and value of immunotherapy in hepatocellular carcinoma. This signature consisted of eight prognostic DERRlncRNAs (HCC)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Another study showed that RHPN1-AS1 knockdown significantly inhibited uveal melanoma (UM) cell proliferation and migration in vitro and in vivo. RHPN1-AS1 may be an oncoRNA for UM and a candidate prognostic biomarker and target for new therapies for malignant UM\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Mao et al. developed a prognostic signature of gastric adenocarcinoma (GA) containing three lncRNAs, including AC103702.2 and three mRNAs, with better accuracy than the conventional TNM pathological staging system\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. AL138756.1 has been reported as autophagy or cuproptosis-associated lncRNA predicting prognosis in colorectal or bladder cancer\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Yuan et al. reported that seven Platelets (PLT)-associated lncRNAs, including the biomarker AL356417.2, affect prognosis and immunotherapy. These lncRNAs could be potential biomarkers and therapeutic targets for patients with GC\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. According to research, CFAP61-AS1 is a glycolysis-associated lncRNA with the potential to predict prognosis in GC and serve as a new biological target for clinical immunotherapy when combined with the traits of other lncRNAs\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Yang et al. identified CDK6-AS1, a lncRNA cell cycle protein-dependent kinase 6, as a potential biomarker candidate for poor prognosis in GC and a predictor of chemotherapeutic drug sensitivity\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. According to Geng et al., the expression levels of five ferroptosis-related lncRNAs, including AL355574.1 (which was experimentally validated to confirm its expression levels in GC), can be used to predict GC prognosis and may aid in the selection of the most promising therapeutic options for GC patients\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The function of lncRNA in the development of gastric cancer has been extensively studied in the last decade and has received much attention as a biomarker for early screening, diagnosis, treatment, prognosis, and drug response due to its high specificity and sensitivity\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRedox reactions play a crucial role in the pathological progression of cancer\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Tumor cells regulate a complex system to control ROS production and participate in vivo responses that regulate tumor redox homeostasis and affect various stromal cells associated with tumor metabolism and immunity\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Takahashi et al. reported that NRF2 directly controls TRPA1 expression, providing an orthogonal mechanism of protection against oxidative stress and a typical ROS neutralization mechanism, revealing oxidative stress defense options that could be used for targeted cancer therapy\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Cancer cells in advanced tumors often exhibit multiple genetic alterations and high oxidative stress, which may be preferentially eliminated by pharmacological ROS damage. Modulating redox-regulated responses in tumor cells may be an active strategy to reduce or eliminate these cells\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Recent studies have shown that redox homeostasis is regulated by lncRNA\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. A growing number of researchers are attempting to pinpoint the crucial lncRNA redox regulatory networks. Chen et al. reported that the Ablation of long noncoding RNA MALAT1 activates the antioxidant pathway and alleviates sepsis in mice\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Lin et al. reported that Hypoxia-induced HIF-1α/lncRNA-PMAN inhibits ferroptosis by promoting the cytoplasmic translocation of ELAVL1 in peritoneal dissemination from GC\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Here, we report for the first time a novel RRlncRNAs marker for predicting prognosis and immune response in the GC population. The present study contributes a to further understanding of lncRNAs and their interactions with redox homeostasis, providing potential targets for future therapies.\u003c/p\u003e \u003cp\u003eRecent studies have reported how dysregulation of metabolites such as ROS modulates redox balance and the cancer cell process and how metabolic microRNA and ncRNA are transmitted to the intercellular stroma through exosome mediators to shape the cancer microenvironment\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. A study found that redox metabolizing enzyme glutathione peroxidase 2 is a metabolic promoter of the tumor immune microenvironment and immune checkpoint inhibitor response\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Therefore, an exploration focusing on redox's detailed mechanisms and functions in cancer will help pave the way for identifying redox induction as a promising therapeutic approach. By boosting the patient's immune system, immunotherapy has proven successful in making cancer curable in various malignancies. Significant advances in immune checkpoint inhibitors (ICIs) have begun to transform clinical practice in the treatment and prognosis of gastric cancer, and combination therapies with other modalities (e.g., targeted therapies) are promising to move immunotherapy into the front line. Immune-related biomarkers have a high predictive value in immunotherapy for gastric cancer\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. The TMEscore was found to have good diagnostic value for patients with metastatic gastric cancer (mGC). High tumor mutational load (TMB-H) is linked to increased objective remission rates (ORR) and progression-free survival for some cancers treated with immunotherapy (PFS). The latest GC clinical trial identified high TMB as a possible predictive marker for OS in AGC patients receiving toripalimab monotherapy\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. TIDE is a computational approach to predicting immunotherapy that mimics tumor immune evasion mechanisms\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Immune checkpoint inhibitors programmed cell death 1/programmed death ligand 1 (PD-1/PD-L1) antibodies, cytotoxic T lymphocyte-associated protein 4 (CTLA-4) antibodies, and chimeric antigen receptor T (CAR-T) in ACT. These therapeutic strategies have significant anti-tumor efficacy in solid and hematological tumors while targeting other immune cells offers a new direction for immunotherapy in GC\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. In this study, patients in the high-risk group were significantly enriched for genes in immune-related pathways.\u003c/p\u003e \u003cp\u003eLow-risk patients possessed a better TMB, possibly due to their benefit from clinical immunotherapy, resulting in lower risk values. TIDE scores were higher in high-risk patients than in low-risk patients. Analysis of patients after clustering showed that monocyte lineages were significantly higher in patients in cluster 2 than in cluster 1. ESTIMATE and immune scores were significantly higher in cluster 2 than in cluster 1. Gene GSEA analysis in cluster 2 was enriched in immune-related pathways such as T and NK cells. The above results suggest that redox-related lncRNA markers could provide potential clues for patients to select more effective anti-tumor immunotherapies. However, further validation is needed to understand our model's utility in predicting immunotherapy response in GC tumors.\u003c/p\u003e \u003cp\u003eOur study also has some restrictions. The report is primarily composed of integrated bioinformatics. There are not enough validated experiments to validate these findings. The accuracy of related lncRNA signatures in GC patients' prognosis and immune regulation remains vital in clinical practice. If these issues are addressed, this study will provide a new marker and theoretical basis for GC immunotherapy.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003ch2\u003e1. Data Resources\u003c/h2\u003e\u003cp\u003eWe downloaded transcriptomic, mutational, and clinical data from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Furthermore, the workflow type belonged to the HTSeq-FPKM format with log2 normalization. In order to transfer annotate, the human gene transfer format (gtf) files were searched in the Ensembl database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://asia.ensembl.org/index.html\u003c/span\u003e\u003cspan address=\"http://asia.ensembl.org/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Besides, the redox-related gene sets were searched in MSigDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using \"redox\" as a keyword. Finally, 487 redox-related genes (RRGs) were obtained.\u003c/p\u003e\u003ch2\u003e2. Screening Conditions for LncRNAs\u003c/h2\u003e\u003cp\u003eThe R package \"limma\" was used for the analysis of the lncRNA matrix with ∣logFC∣\u0026gt;1 and FDR \u0026lt; 0.05 as thresholds to determine differentially expressed lncRNA (DElncRNA). Also, expression of redox-related genomes (RRGs) was extracted, and the redox-related lncRNAs (RRlncRNAs) were identified by Pearson Test (∣Cor∣ \u0026gt; 0.4, p. adj \u0026lt; 0.001). Finally, the Sankey plot was drawn using the \"ggalluvial\" package to visualize the co-expression relationship between lncRNAs and RRGs in GC patients.\u003c/p\u003e\u003cp\u003e \u003cb\u003e3. Construction of Risk Model for Differentially Expressed Redox-related LncRNAs (DERRlnRNAs).\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe expression matrix of DERRlncRNAs in GC patients was merged with their clinical survival matrix. Univariate and multivariate Cox regressions were conducted on DERRlncRNAs. The DERRlncRNAs relevant to the prognosis of GC patients were finalized using LASSO regression to prevent overfitting, with P \u0026lt; 0.05 as significant. Then, the GC samples from TCGA were randomly and equally split into train and test groups. Median risk scores and survival analysis were performed for the train and test groups, respectively. The risk score formula is shown below:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{R}\\text{i}\\text{s}\\text{k} \\text{s}\\text{c}\\text{o}\\text{r}\\text{e} =\\sum _{i=1}^{n}{Coef}_{i}*{x}_{i}$$\u003c/div\u003e \u003c/div\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({Coef}_{i}\\)\u003c/span\u003e \u003c/span\u003e represents coefficient, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the normalized count for each hub lncRNA. According to the median risk value of the training group, GC patients were divided into high- and low-risk groups.\u003c/p\u003e\u003cp\u003e \u003cb\u003e4. Validation of the Survival-Predicting Signature.\u003c/b\u003e \u003c/p\u003e\u003cp\u003eKaplan-Meier (K-M) survival analysis was performed using the R packages \"survival\" and \"survminer\" to test the predictive power of the model for survival. Median risk values were assessed using the \"ggrisk\" package. We combined clinical data containing age, gender, etc., with the patient's risk scores and removed samples lacking clinical data. Multivariate ROC curves were then plotted to validate and compare the efficacy of the developed signature with prognostic factors. The area under the ROC curve (AUC) was employed to evaluate the signature's accuracy, and this signature was also compared with three published models for predicting prognosis in GC patients\u003csup\u003e\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e–\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Finally, the Nomogram plot was plotted with the \"rms\" package, and the accuracy of Nomogram plots was measured using calibration curves. \"ggDCA\" and \"rms\" packages were used to construct DCA curves and C-index plots, respectively.\u003c/p\u003e\u003ch3\u003e5. Consensus Clustering\u003c/h3\u003e\u003cp\u003eWhen GC patients were clustered using the \"ConsensusClusterPlus\" package, the strongest separation occurred between groups of patients when k = 2.\u003c/p\u003e\u003ch2\u003e6. Gene Set Enrichment Analysis (GSEA), GO, and KEGG Enrichment\u003c/h2\u003e\u003cp\u003eAccording to the consensus clustering algorithm outcomes, the transcriptome files of all samples were divided into two clusters, A and B. Enrichment analysis of subgroup data in GSEA (version 4.3.2) to investigate the enrichment of immune pathways between the two groups. The P. adj \u0026lt; 0.05 was considered significant in GO and KEGG analysis.\u003c/p\u003e\u003ch2\u003e7. Tumor Mutation Burden and The Immune Microenvironment\u003c/h2\u003e\u003cp\u003eThe \"maftools\" package was used to construct a waterfall map of gene mutations. The \"CIBERSORT\" \"estimate\" packages were used to analyze the differences between the two clusters' immune microenvironment and infiltration. Finally, the \"ggpubr\" was used to plot TMB, TIDE, and other immune scores for different groups of patients. TIDE analysis tool for GC patients (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003e8. Statistical analysis\u003c/h2\u003e\u003cp\u003eData was collated via PERL and analyzed via the R programming package. P \u0026lt; 0.05 was considered to be statistically significant.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor contributions statement\u003c/p\u003e\n\u003cp\u003eConception and design: H.W., M.L.; Data analysis and interpretation: G.P., D.W., L.S., W.L.; Manuscript writing: H.W., G.P., M.H.; Final approval of manuscript: All authors. All authors read and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe work was partially supported by the Natural Science Foundation of Bengbu Medical College (2022byfy002).\u003c/p\u003e\n\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\n\u003cp\u003eAvailability of Data and Materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung, H.\u003cem\u003e et al.\u003c/em\u003e Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 209-249, doi:10.3322/caac.21660 (2021).\u003c/li\u003e\n\u003cli\u003eZheng, L., Wang, L., Ajani, J. \u0026amp; Xie, K. 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A novel cuproptosis-related lncRNA nomogram to improve the prognosis prediction of gastric cancer. \u003cem\u003eFrontiers In Oncology\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 957966, doi:10.3389/fonc.2022.957966 (2022).\u003c/li\u003e\n\u003cli\u003eZhao, Z.\u003cem\u003e et al.\u003c/em\u003e Necroptosis-Related lncRNAs: Predicting Prognosis and the Distinction between the Cold and Hot Tumors in Gastric Cancer. \u003cem\u003eJournal of Oncology\u003c/em\u003e \u003cstrong\u003e2021\u003c/strong\u003e, 6718443, doi:10.1155/2021/6718443 (2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2843204/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2843204/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRedox responses modulated by intracellular long noncoding RNA (lncRNA) can be involved in tumorigenesis and progression. However, the role of redox-related lncRNAs (RRlncRNAs) in gastric cancer (GC) development remains mostly unknown. Our research aims to establish and validate novel prognostic and immune infiltration markers for GC by constructing a prognostic model of RRlncRNAs. We downloaded the transcriptomic and mutational data for 407 GC pa-tients from The Cancer Genome Atlas (TCGA) database and randomized them 1:1 into a training and validation set to show that redox-related lncRNAs affect GC patients' prognosis. Subse-quently, the prognostic model was constructed for the screened RRlncRNAs using the Least Absolute Shrinkage and Selection Operator (LASSO) and the multivariate COX regression algo-rithm. Then, Survival analyses were performed on the train and test sets. The overall survival rate of GC patients was significantly correlated with the signatures of eight RRlncRNAs, including AC103702.2, AL138756.1, AL356417.2, CFAP61-AS1, RHPN1-AS1, CDK6-AS1, LINC02864, and AL355574.1. Meanwhile, we validated the model's accuracy through nomograms, Decision Curve Analysis (DCA), and comparisons using models from other studies. The results demonstrated that our model is more effective and outperforms the signature of Jiang et al. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of gene enrichment in high-risk patients shows significant enrichment in immune-related pathways. Waterfall plots of gene mutations, tumor mutation burden (TMB), and tumor immune dysfunction and exclusion (TIDE) showed significant differences in immune function between high- and low-risk groups. Then, we divided the 407 GC patients into two clusters using a consensus clustering algorithm and found significant differences in their immune microenvironment through immune cell difference anal-ysis, ESTIMATEScore, and gene set enrichment analysis (GSEA). Taken together, we conclude that the prognostic model constructed by RRlncRNAs can significantly affect the prognosis of GC patients and may alter their tumor progression by modulating the immune microenvironment in vivo. Our study found eight RRlncRNA-associated signatures, representing promising new markers for immunotherapy and diagnosis in GC patients.\u003c/p\u003e","manuscriptTitle":"Identification and validation of a novel redox- related differentially expressed lncRNA prognostic signature for predicting clinical immunotherapy response in gastric cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-16 15:04:39","doi":"10.21203/rs.3.rs-2843204/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13c16f0d-41af-44d2-a0d5-c56b69e3a2bb","owner":[],"postedDate":"May 16th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":21464048,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":21464049,"name":"Biological sciences/Computational biology and bioinformatics/Data mining"},{"id":21464050,"name":"Biological sciences/Computational biology and bioinformatics/Data processing"},{"id":21464051,"name":"Biological sciences/Computational biology and bioinformatics/Databases"},{"id":21464052,"name":"Biological sciences/Computational biology and bioinformatics/Functional clustering"},{"id":21464053,"name":"Biological sciences/Cancer/Cancer microenvironment"},{"id":21464054,"name":"Biological sciences/Cancer/Tumour biomarkers"},{"id":21464055,"name":"Biological sciences/Cancer/Tumour immunology"},{"id":21464056,"name":"Biological sciences/Cancer"},{"id":21464057,"name":"Biological sciences/Cancer/Gastrointestinal cancer"},{"id":21464058,"name":"Biological sciences/Cancer/Gastrointestinal cancer/Gastric cancer"},{"id":21464059,"name":"Biological sciences/Molecular biology/Non coding rnas"},{"id":21464060,"name":"Biological sciences/Molecular biology/Non coding rnas/Long non coding rnas"},{"id":21464061,"name":"Health sciences/Oncology/Cancer/Gastrointestinal cancer/Gastric cancer"},{"id":21464062,"name":"Health sciences/Oncology/Cancer/Cancer microenvironment"},{"id":21464063,"name":"Health sciences/Oncology/Cancer/Cancer therapy"},{"id":21464064,"name":"Health sciences/Oncology/Cancer/Tumour biomarkers"},{"id":21464065,"name":"Health sciences/Oncology/Cancer/Tumour immunology"},{"id":21464066,"name":"Health sciences/Oncology/Cancer/Cancer models"}],"tags":[],"updatedAt":"2023-07-25T20:14:24+00:00","versionOfRecord":[],"versionCreatedAt":"2023-05-16 15:04:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2843204","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2843204","identity":"rs-2843204","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}