Effects of Inflammation on the Immune Microenvironment in Gastric Cancer

Identification of a novel cellular senescence-related lncRNA signature for prognosis and immune response in osteosarcoma

Background

Cellular senescence, a novel hallmark of cancer, is associated with patient outcomes and tumor immunotherapy. However, at present, there is no systematic study on the use of cellular senescence-related long non-coding RNAs (CSR-lncRNAs) to predict survival in patients with osteosarcoma. In this study, we aimed to identify a CSR-lncRNAs signature and to evaluate its potential use as a survival prognostic marker and predictive tool for immune response of osteosarcoma.

Methods

We downloaded a cohort of patients with osteosarcoma from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We performed differential expression and co-expression analyses to identify CSR-lncRNAs. We performed univariate and multivariate Cox regression analyses along with the random forest algorithm to identify lncRNAs significantly correlated with senescence. Subsequently, we assessed the predictive models using survival curves, receiver operating characteristic curves, nomograms, C-index, and decision curve analysis. Based on this model, patients with osteosarcoma were divided into two groups according to their risk scores. Then, using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, we compared their clinical characteristics to uncover functional differences. We further conducted immune infiltration analyses using estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE), cell-type identification by estimating relative subsets of rna transcripts (CIBERSORT), and single-sample gene set enrichment analysis for the two groups. We also evaluated the expression of the target genes of immune checkpoint inhibitors (ICIs).

Results

We identified six lncRNAs that were significantly correlated with senescence and accordingly established a novel cellular senescence-related lncRNA prognostic signature incorporating these lncRNAs. The nomogram indicated that the risk model was an independent prognostic factor that could predict the survival of patients with osteosarcoma. This model demonstrated high accuracy upon validation. Further analysis revealed that patients with osteosarcoma in the low-risk group exhibited better clinical outcomes and enhanced immune infiltration.

Conclusions

The six-CSR-lncRNA prognostic signature effectively predicted survival outcomes and patients in the low-risk group might have improved immune infiltration.

Inflammation-Related Gene ADH1A Regulates the Polarization of Macrophage M1 and Influences the Malignant Progression of Gastric Cancer

Background

Gastric cancer (GC) is a malignant tumor originating from the gastric mucosa epithelium, and there is a low survival rate of GC patients after treatment, with a poor prognostic outcome. The inflammatory response within the tumor microenvironment plays an important role in GC progression.

Methods

We downloaded GC-related datasets and inflammation-related genes from GEO, TCGA and MSigDB databases, performed differential analysis, protein-protein interaction analysis, immunoinfiltration analysis and Lasso analysis to screen inflammation-related hub genes affecting GC progression, and carried out qRT-PCR for validation. In order to explore the role of ADH1A, we constructed overexpressed plasmids, treated GC cells with cGMP/PKG pathway agonist 8-Br-cGMP, and tested cell functions with CCK8, EdU, Transwell, scratch assay and other experiments. On this basis, GC cells were co-cultured with monocyte THP-1 to explore the effect of ADH1A on the polarization of macrophages.

Results

ADH1A was significantly decreased in GC cells, and its expression trend was consistent with the results of bioinformatics analysis. Therefore, we chose ADH1A for subsequent functional validation. Overexpression of ADH1A in GC cells revealed ADH1A's role in inhibiting the activity, proliferation, migration and invasion of GC cells, promoting apoptosis and secretion of IL-6, IFN-γ, CCL5 and CSF2, and facilitating the transformation of macrophages to a pro-inflammatory M1 phenotype. ssGSEA results demonstrated the potential involvement of ADH1A in the cGMP/PKG signaling pathway, and significant changes in the expression of proteins related to the cGMP/PKG signaling pathway. The use of the cGMP/PKG signaling pathway agonist 8-Br-cGMP in ADH1A-overexpressing GC cells substantiated ADH1A's capacity to inhibit the cGMP/PKG signaling pathway, thereby suppressing the malignant progression of GC and promoting the transformation of macrophages to a pro-inflammatory M1 phenotype.

Conclusion

ADH1A is able to influence the malignant progression of GC and the transformation of macrophages to the pro-inflammatory M1 phenotype through the cGMP/PKG signaling pathway.

Identification of Prognostic and Immune Characteristics of Two Lung Adenocarcinoma Subtypes Based on TRPV Channel Family Genes

Lung adenocarcinoma (LUAD) is one of the deadliest malignant tumors worldwide. Transient receptor potential vanilloid (TRPV) channels take pivotal parts in many cancers, but their impact on LUAD remains unexplored. In this study, LUAD samples were classified into two subtypes according to the expression characteristics of TRPV1-6 genes, with LUAD subtype cluster2 exhibiting significantly higher survival rates than cluster1. Subsequently, analysis of differentially expressed genes (DEGs) was performed between cluster1 and cluster2, revealing enrichment of DEGs in channel activity and Ca2+ signaling pathways. We established a protein-protein interaction network based on DEGs and constructed a LUAD prognostic model by using Cox regression analysis based on genes corresponding to 170 protein nodes. The prognostic model demonstrated good predictive ability for patient prognosis, with higher survival rates observed in the low-risk (LR) group. The risk score was validated as an independent prognostic indicator, according to Cox regression analysis. A clinically applicable nomogram was plotted. Immunological analysis indicated that the LR and high-risk (HR) groups had varied proportions of immune cell infiltration. The immunotherapy prediction indicated that LUAD patients in LR group had a greater likelihood to benefit from immune checkpoint blockade therapy. Furthermore, we hypothesized that the expression patterns of feature genes in the LUAD model were related to the sensitivity to lung cancer therapeutic drugs TAS-6417 and Erlotinib. To sum up, our LUAD prognostic model possessed clinical applicability for prognosis and immunotherapy response prediction.

The Role of LMP1 in Epstein-Barr Virus-associated Gastric Cancer

EBV promotes many cancers such as lymphoma, nasopharyngeal carcinoma, and gastric; Latent Membrane Protein 1 (LMP1) is considered to be a major oncogenic protein encoded by Epstein- Barr virus (EBV). LMP1 functions as a carcinogen in lymphoma and nasopharyngeal carcinoma, and LMP1 may also promote gastric cancer. The expression level of LMP1 in host cells is a key determinant in tumorigenesis and maintenance of virus specificity. By promoting cell immortalization and cell transformation, promoting cell proliferation, affecting immunity, and regulating cell apoptosis, LMP1 plays a crucial tumorigenic role in epithelial cancers. However, very little is currently known about LMP1 in Epstein-Barr virus-associated gastric cancer (EBVaGC); the main reason is that the expression level of LMP1 in EBVaGC is comparatively lower than other EBV-encoded proteins, such as The Latent Membrane Protein 2A (LMP2A), Epstein-Barr nuclear antigen 1 (EBNA1) and BamHI-A rightward frame 1 (BARF1), to date, there are few studies related to LMP1 in EBVaGC. Recent studies have demonstrated that LMP1 promotes EBVaGC by affecting The phosphatidylinositol 3-kinase- Akt (PI3K-Akt), Nuclear factor-kappa B (NF-κB), and other signaling pathways to regulate many downstream targets such as Forkhead box class O (FOXO), C-X-C-motif chemokine receptor (CXCR), COX-2 (Cyclooxygenase-2); moreover, the gene methylation induced by LMP1 in EBVaGC has become one of the characteristics that distinguish this gastric cancer (GC) from other types of gastric cancer and LMP1 also promotes the formation of the tumor microenvironment (TME) of EBVaGC in several ways. This review synthesizes previous relevant literature, aiming to highlight the latest findings on the mechanism of action of LMP1 in EBVaGC, summarize the function of LMP1 in EBVaGC, lay the theoretical foundation for subsequent new research on LMP1 in EBVaGC, and contribute to the development of novel LMP1-targeted drugs.

Prognostic significance of MALAT1 in clear cell renal cell carcinoma based on TCGA and GEO

Long noncoding RNAs metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) can regulate tumorigenesis and progression of various cancers. However, there is little known about the tumor biology and regulatory mechanism of MALAT1 in clear cell renal cell carcinoma (ccRCC). The objective of this study was to evaluate the prognostic value and potential functions of MALAT1 in ccRCC based on the cancer genome atlas. Through bioinformatics research, we analyzed the expression of MALAT1 in ccRCC, and the relationship with clinicopathological features, overall survival and infiltration of immune cells, and established the prognostic models. The results showed that MALAT1 was highly expressed in ccRCC tissues and predicted poor ccRCC patient outcome. The expression level of MALAT1 was significantly correlated with histologic grade, pathologic grade, T stage, M stage. ROC curve showed that MALAT1 had a good diagnostic accuracy, area under the curve of 0.752. The univariate and multivariate cox regression analysis showed that high MALAT1 expression was an independent prognostic factor for overall survival in the cancer genome atlas (hazard ratio = 2.271, 95% confidence interval: 1.435-3.593, P < .001). Gene set enrichment analysis revealed that MALAT1 expression was associated with the DNA methylation, epigenetic regulation of gene expression signaling pathway. In addition, the prognostic models were established to predict 1-, 3- and 5-year survival. This study showed that high expression of MALAT1 might be a potential diagnostic and prognostic biomarker.

Carbon Dots for the Treatment of Inflammatory Diseases: An Appraisal of In Vitro and In Vivo Studies

In recent decades, several studies demonstrating various applications of carbon dots (C-dots), including metal sensing, bioimaging, pH sensing, and antimicrobial activities, have been published. Recent developments have shifted this trend toward biomedical applications that target various biomarkers relevant to chronic diseases. However, relevant developments and research results regarding the anti-inflammatory properties of C-dots against inflammation-associated diseases have not been systematically reviewed. Hence, this review discusses the anti-inflammatory effects of C-dots in in vivo and in vitro models of LPS-induced inflammation, gout, cartilage tissue engineering, drug-induced inflammation, spinal cord injury, wound healing, liver diseases, stomach cancer, gastric ulcers, acute kidney and lung injury, psoriasis, fever or hypothermia, and bone tissue regeneration. The compiled studies demonstrate the promising potential of C-dots as anti-inflammatory agents for the development of new drugs.

Zinc Finger Proteins in the War on Gastric Cancer: Molecular Mechanism and Clinical Potential

According to the 2020 global cancer data released by the World Cancer Research Fund (WCRF) International, gastric cancer (GC) is the fifth most common cancer worldwide, with yearly increasing incidence and the second-highest fatality rate in malignancies. Despite the contemporary ambiguous molecular mechanisms in GC pathogenesis, numerous in-depth studies have demonstrated that zinc finger proteins (ZFPs) are essential for the development and progression of GC. ZFPs are a class of transcription factors with finger-like domains that bind to Zn²⁺ extensively and participate in gene replication, cell differentiation and tumor development. In this review, we briefly outline the roles, molecular mechanisms and the latest advances in ZFPs in GC, including eight principal aspects, such as cell proliferation, epithelial-mesenchymal transition (EMT), invasion and metastasis, inflammation and immune infiltration, apoptosis, cell cycle, DNA methylation, cancer stem cells (CSCs) and drug resistance. Intriguingly, the myeloid zinc finger 1 (MZF1) possesses reversely dual roles in GC by promoting tumor proliferation or impeding cancer progression via apoptosis. Therefore, a thorough understanding of the molecular mechanism of ZFPs on GC progression will pave the solid way for screening the potentially effective diagnostic indicators, prognostic biomarkers and therapeutic targets of GC.

Contributions of the receptor for advanced glycation end products axis activation in gastric cancer

Compelling shreds of evidence derived from both clinical and experimental research have demonstrated the crucial contribution of receptor for advanced glycation end products (RAGE) axis activation in the development of neoplasms, including gastric cancer (GC). This new actor in tumor biology plays an important role in the onset of a crucial and long-lasting inflammatory milieu, not only by supporting phenotypic changes favoring growth and dissemination of tumor cells, but also by functioning as a pattern-recognition receptor in the inflammatory response to Helicobacter pylori infection. In the present review, we aim to highlight how the overexpression and activation of the RAGE axis contributes to the proliferation and survival of GC cells as and their acquisition of more invasive phenotypes that promote dissemination and metastasis. Finally, the contribution of some single nucleotide polymorphisms in the RAGE gene as susceptibility or poor prognosis factors is also discussed.

Effects of immune inflammation in head and neck squamous cell carcinoma: Tumor microenvironment, drug resistance, and clinical outcomes

Background: Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor with a very high mortality rate, and a large number of studies have confirmed the correlation between inflammation and malignant tumors and the involvement of inflammation-related regulators in the progression of HNSCC. However, a prognostic model for HNSCC based on genes involved in inflammatory factors has not been established. Methods: First, we downloaded transcriptome data and clinical information from patients with head and neck squamous cell carcinoma from TCGA and GEO (GSE41613) for data analysis, model construction, and differential gene expression analysis, respectively. Genes associated with inflammatory factors were screened from published papers and intersected with differentially expressed genes to identify differentially expressed inflammatory factor-related genes. Subgroups were then typed according to differentially expressed inflammatory factor-related genes. Univariate, LASSO and multivariate Cox regression algorithms were subsequently applied to identify prognostic genes associated with inflammatory factors and to construct prognostic prediction models. The predictive performance of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic curve (ROC). Subsequently, we analyzed differences in immune composition between patients in the high and low risk groups by immune infiltration. The correlation between model genes and drug sensitivity (GSDC and CTRP) was also analyzed based on the GSCALite database. Finally, we examined the expression of prognostic genes in pathological tissues, verifying that these genes can be used to predict prognosis. Results: Using univariate, LASSO, and multivariate cox regression analyses, we developed a prognostic risk model for HNSCC based on 13 genes associated with inflammatory factors (ITGA5, OLR1, CCL5, CXCL8, IL1A, SLC7A2, SCN1B, RGS16, TNFRSF9, PDE4B, NPFFR2, OSM, ROS1). Overall survival (OS) of HNSCC patients in the low-risk group was significantly better than that in the high-risk group in both the training and validation sets. By clustering, we identified three molecular subtypes of HNSCC carcinoma (C1, C2, and C3), with C1 subtype having significantly better OS than C2 and C3 subtypes. ROC analysis suggests that our model has precise predictive power for patients with HNSCC. Enrichment analysis showed that the high-risk and low-risk groups showed strong immune function differences. CIBERSORT immune infiltration score showed that 25 related and differentially expressed inflammatory factor genes were all associated with immune function. As the risk score increases, specific immune function activation decreases in tumor tissue, which is associated with poor prognosis. We also screened for susceptibility between the high-risk and low-risk groups and showed that patients in the high-risk group were more sensitive to talazoparib-1259, camptothecin-1003, vincristine-1818, Azd5991-1720, Teniposide-1809, and Nutlin-3a (-) -1047.Finally, we examined the expression of OLR1, SCN1B, and PDE4B genes in HNSCC pathological tissues and validated that these genes could be used to predict the prognosis of HNSCC. Conclusion: In this experiment, we propose a prognostic model for HNSCC based on inflammation-related factors. It is a non-invasive genomic characterization prediction method that has shown satisfactory and effective performance in predicting patient survival outcomes and treatment response. More interdisciplinary areas combining medicine and electronics will be explored in the future.

Differential Effect of Vaginal Microbiota on Spontaneous Preterm Birth among Chinese Pregnant Women

Objective

The effect of vaginal microbiota on spontaneous preterm birth (sPTB) has not been fully addressed, and few studies have explored the associations between vaginal taxa and sPTB in the gestational diabetes mellitus (GDM) and non-GDM groups, respectively. Study Design. To minimize external interference, a total of 41 pregnant women with sPTB and 308 controls (pregnant women without sPTB) from same regain were enrolled in this case-cohort study. Controls were randomly selected at baseline. With the exception of GDM, other characteristics were not significantly different between the two groups. Vaginal swabs were collected at early second trimester. Using 16S amplicon sequencing, the main bioinformatics analysis was performed on the platform of QIIME 2. Vaginal microbiota traits of the sPTB group were compared with controls. Finally, the effects of binary taxa on sPTB in the GDM group and the non-GDM group were analyzed, respectively.

Results

The proportion of GDM in the sPTB (19.51%) was higher than the controls (7.47%, P = 0.018). The vaginal microbiota of pregnant women with sPTB exhibited higher alpha diversity metrics (observed features, P = 0.001; Faith's phylogenetic diversity, P = 0.013) and different beta diversity metrics (unweighted UniFrac, P = 0.006; Jaccard's distance, P = 0.004), compared with controls. The presence of Lactobacillus paragasseri/gasseri (aOR: 3.12, 95% CI: 1.24-7.84), Streptococcus (aOR: 3.58, 95% CI: 1.68-7.65), or Proteobacteria (aOR: 3.39, 95% CI: 1.55-7.39) was associated with an increased risk of sPTB in the non-GDM group (P < 0.05). However, the relative abundance of novel L. mulieris (a new species of the L. delbrueckii group) was associated with a decreased risk of sPTB (false discovery rate, 0.10) in all pregnant women.

Conclusion

GDM may modify the association of vaginal taxa with sPTB, suggesting that maternal GDM should be considered when using vaginal taxa to identify pregnant women at high risk of sPTB.

Identification and validation of a novel cellular senescence-related lncRNA prognostic signature for predicting immunotherapy response in stomach adenocarcinoma

Background: Cellular senescence is a novel hallmark of cancer associated with patient outcomes and tumor immunotherapy. However, the value of cellular senescence-related long non-coding RNAs (lncRNAs) in predicting prognosis and immunotherapy response for stomach adenocarcinoma (STAD) patients needs further investigation.

Methods: The transcriptome and corresponding clinical information of STAD and cellular senescence-related genes were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and CellAge databases. Differential expression analysis and coexpression analysis were performed to obtain cellular senescence-related lncRNAs. Univariate regression analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were conducted to establish the cellular senescence-related lncRNA prognostic signature (CSLPS). Next, the survival curve, ROC curve, and nomogram were developed to assess the capacity of predictive models. Moreover, principal component analysis (PCA), gene set enrichment analysis (GSEA), tumor microenvironment (TME), tumor mutation burden (TMB), microsatellite instability (MSI), and tumor immune dysfunction and exclusion (TIDE) score analysis were performed between high- and low-risk groups.

Results: A novel CSLPS involving fifteen lncRNAs (REPIN1-AS1, AL355574.1, AC104695.3, AL033527.2, AC083902.1, TYMSOS, LINC00460, AC005165.1, AL136115.1, AC007405.2, AL391152.1, SCAT1, AC129507.1, AL121748.1, and ADAMTS9-AS1) was developed. According to the nomogram, the risk model based on the CSLPS was an independent prognostic factor and could predict 1-, 3-, and 5-year overall survival for STAD patients. GSEA suggested that the high-risk group was mainly associated with Toll-like receptor, JAK/STAT, NOD-like receptor, and chemokine signaling pathways. Further analysis revealed that STAD patients in the low-risk group with better clinical outcomes had a higher TMB, higher proportion of high microsatellite instability (MSI-H), better immune infiltration, and lower TIDE scores.

Conclusion: A fifteen-CSlncRNA prognostic signature could predict survival outcomes, and patients in the low-risk group may be more sensitive to immunotherapy.

Identification and Validation of an m6A Modification of JAK-STAT Signaling Pathway–Related Prognostic Prediction Model in Gastric Cancer

Background: Gastric cancer (GC) is one of the malignant tumors worldwide. Janus (JAK)–signal transduction and activator of transcription (STAT) signaling pathway is involved in cellular biological process and immune function. However, the association between them is still not systematically described. Therefore, in this study, we aimed to identify key genes involved in JAK-STAT signaling pathway and GC, as well as the potential mechanism.

Methods: The Cancer Genome Atlas (TCGA) database was the source of RNA-sequencing data of GC patients. Gene Expression Omnibus (GEO) database was used as the validation set. The predictive value of the JAK-STAT signaling pathway-related prognostic prediction model was examined using least absolute shrinkage and selection operator (LASSO); survival, univariate, and multivariate Cox regression analyses; and receiver operating characteristic curve (ROC) analyses to examine the predictive value of the model. Quantitative real-time polymerase chain reaction (qRT-PCR) and chi-square test were used to verify the expression of genes in the model and assess the association between the genes and clinicopathological parameters of GC patients, respectively. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis, version 3.0 (GSEA), sequence-based RNA adenosine methylation site predictor (SRAMP) online websites, and RNA immunoprecipitation (RIP) experiments were used to predict the model-related potential pathways, m6A modifications, and the association between model genes and m6A.

Results: A four-gene prognostic model (GHR, PIM1, IFNA8, and IFNB1) was constructed, namely, riskScore. The Kaplan–Meier curves suggested that patients with high riskScore expression had a poorer prognosis than those with low riskScore expression (p = 0.006). Multivariate Cox regression analyses showed that the model could be an independent predictor (p < 0.001; HR = 3.342, 95%, CI = 1.834–6.088). The 5-year area under time-dependent ROC curve (AUC) reached 0.655. The training test set verified these results. Further analyses unveiled an enrichment of cancer-related pathways, m6A modifications, and the direct interaction between m6A and the four genes.

Conclusion: This four-gene prognostic model could be applied to predict the prognosis of GC patients and might be a promising therapeutic target in GC.

DPEP1 promotes drug resistance in colon cancer cells by forming a positive feedback loop with ASCL2

BACKGROUND

Drug resistance is an important factor affecting the efficacy of chemotherapy in patients with colon cancer. However, clinical markers for diagnosing drug resistance of tumor cells are not only a few in number, but also low in specificity, and the mechanism of action of tumor cell drug resistance remains unclear.

METHODS

Dipeptidase 1 (DPEP1) expression was analyzed using the cancer genome atlas (TCGA) and genotype-Tissue Expression pan-cancer data. Survival analysis was performed using the survival package in R software to assess the prognostic value of DPEP1 expression in colon cancer. Correlation and Venn analyses were adopted to identify key genes. Immunohistochemistry, western blot, qRT-PCR, Co-immunoprecipitation, and dual-luciferase reporter experiments were carried out to explore the underlying associations between DPEP1 and Achaete scute-like 2 (ASCL2). MTT assays were used to evaluate the role of DPEP1 and ASCL2 in colon cancer drug resistance.

RESULTS

DPEP1 was highly expressed in colon cancer tissues. DPEP1 expression correlated negatively with disease-specific survival but not with overall survival. Bioinformatics analysis and experiments showed that the expressions of DPEP1 and ASCL2 in colon cancer tissues were markedly positively correlated. Mechanistic research indicated that DPEP1 enhanced the stability of protein ASCL2 by inhibiting its ubiquitination-mediated degradation. In turn, ASCL2 functioned as a transcription factor to activate the transcriptional activity of the DPEP1 gene and boost its expression. Furthermore, DPEP1 also could enhance the expression of colon cancer stem cell markers (LGR5, CD133, and CD44), which strengthened the tolerance of colon cancer cells to chemotherapy drugs.

CONCLUSIONS

Our findings reveal that the DPEP1 enhances the stemness of tumor cells by forming a positive feedback loop with ASCL2 to improve resistance to chemotherapy drugs.

Screening of key methylation-driven genes CDO1 in breast cancer based on WGCNA

BACKGROUND

With the rapid development of genomics and molecular biology, not only have biochemical indicators been used as tumour markers, but many new molecular markers have emerged. Epigenetic abnormalities are a new type of molecular marker, and DNA methylation is an important part of epigenetics.

OBJECTIVE

This study used weighted gene coexpression network analysis (WGCNA) to analyse key methylation-driven genes in breast cancer.

METHODS

The RNA-seq transcriptome data, DNA methylation data, and clinical information data of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database, and the MethylMix R package was used to screen methylation-driven genes in breast cancer. The ClusterProfiler package and enrichplot package in R software were used to further analyse the function and signalling pathway of methylation-driven genes. Through univariate and multivariate Cox regression analyses, methylation-driver genes related to prognostic were obtained, a prognostic model was constructed and prognostic characteristics were analysed.

RESULTS

The 17 methylation-driven genes related to prognosis were obtained by the WGCNA method in breast cancer, and the prognostic significance of these methylation-driven genes was determined by transcriptome and methylation combined survival analysis. Analysis of functions and signalling pathways showed that these genes were mainly enriched in biological processes and signalling pathway. Through univariate and multivariate Cox regression analyses, a prognostic model of 5 methylation-driven genes was constructed.

CONCLUSIONS

The AUC of the receiver operating characteristic (ROC) curve of this model was 0.784, showing that the model had a good prediction effect. Based on WGCNA screening, it was found that only CDO1 was the key methylation-driven gene for prognosis in breast cancer, indicating that CDO1 may be an important indicator of the prognosis of breast cancer patients.