DNA methylation profiling to predict recurrence risk in stage Ι lung adenocarcinoma: Development and validation of a nomogram to clinical management

Construction and validation of prognostic signatures related to mitochondria and macrophage polarization in gastric cancer

Background

Increasing evidence reveals the involvement of mitochondria and macrophage polarisation in tumourigenesis and progression. This study aimed to establish mitochondria and macrophage polarisation-associated molecular signatures to predict prognosis in gastric cancer (GC) by single-cell and transcriptional data.

Methods

Initially, candidate genes associated with mitochondria and macrophage polarisation were identified by differential expression analysis and weighted gene co-expression network analysis. Subsequently, candidate genes were incorporated in univariateCox analysis and LASSO to acquire prognostic genes in GC, and risk model was created. Furthermore, independent prognostic indicators were screened by combining risk score with clinical characteristics, and a nomogram was created to forecast survival in GC patients. Further, in single-cell data analysis, cell clusters and cell subpopulations were yielded, followed by the completion of pseudo-time analysis. Furthermore, a more comprehensive immunological analysis was executed to uncover the relationship between GC and immunological characteristics. Ultimately, expression level of prognostic genes was validated through public datasets and qRT-PCR.

Results

A risk model including six prognostic genes (GPX3, GJA1, VCAN, RGS2, LOX, and CTHRC1) associated with mitochondria and macrophage polarisation was developed, which was efficient in forecasting the survival of GC patients. The GC patients were categorized into high-/low-risk subgroups in accordance with median risk score, with the high-risk subgroup having lower survival rates. Afterwards, a nomogram incorporating risk score and age was generated, and it had significant predictive value for predicting GC survival with higher predictive accuracy than risk model. Immunological analyses revealed showed higher levels of M2 macrophage infiltration in high-risk subgroup and the strongest positive correlation between risk score and M2 macrophages. Besides, further analyses demonstrated a better outcome for immunotherapy in low-risk patients. In single-cell and pseudo-time analyses, stromal cells were identified as key cells, and a relatively complete developmental trajectory existed for stromal C1 in three subclasses. Ultimately, expression analysis revealed that the expression trend of RGS2, GJA1, GPX3, and VCAN was consistent with the results of the TCGA-GC dataset.

Conclusion

Our findings demonstrated that a novel prognostic model constructed in accordance with six prognostic genes might facilitate the improvement of personalised prognosis and treatment of GC patients.

Comprehensive landscape of junctional genes and their association with overall survival of patients with lung adenocarcinoma

Objectives

Junctional proteins are involved in tumorigenesis. Therefore, this study aimed to investigate the association between junctional genes and the prognosis of patients with lung adenocarcinoma (LUAD).

Methods

Transcriptome, mutation, and clinical data were retrieved from The Cancer Genome Atlas (TCGA). "Limma" was used to screen differentially expressed genes. Moreover, Kaplan-Meier survival analysis was used to identify junctional genes associated with LUAD prognosis. The junctional gene-related risk score (JGRS) was generated based on multivariate Cox regression analysis. An overall survival (OS) prediction model combining the JGRS and clinicopathological properties was proposed using a nomogram and further validated in the Gene Expression Omnibus (GEO) LUAD cohort.

Results

To our knowledge, this study is the first to demonstrate the correlation between the mRNA levels of 14 junctional genes (CDH15, CDH17, CDH24, CLDN6, CLDN12, CLDN18, CTNND2, DSG2, ITGA2, ITGA8, ITGA11, ITGAL, ITGB4, and PKP3) and clinical outcomes of patients with LUAD. The JGRS was generated based on these 14 genes, and a higher JGRS was associated with older age, higher stage levels, and lower immune scores. Thus, a prognostic prediction nomogram was proposed based on the JGRS. Internal and external validation showed the good performance of the prediction model. Mechanistically, JGRS was associated with cell proliferation and immune regulatory pathways. Mutational analysis revealed that more somatic mutations occurred in the high-JGRS group than in the low-JGRS group.

Conclusion

The association between junctional genes and OS in patients with LUAD demonstrated by our "TCGA filtrating and GEO validating" model revealed a new function of junctional genes.

Identification of a Novel CD8+ T cell exhaustion-related gene signature for predicting survival in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a major health concern, necessitating a deeper understanding of its prognosis and underlying mechanisms. This study aimed to investigate the mechanism and prognostic value of CD8+ T Cell exhaustion (CD8+ TEX)-related genes in HCC and construct a survival prognosis prediction model for patients with HCC.

METHODS

CD8+ TEX-related genes associated with HCC prognosis were analysed and identified, and a prognostic prediction model was constructed using the 'least absolute shrinkage and selection operator' Cox regression model. Immunohistochemistry was used to verify the expression of the model genes in HCC tissues. A nomogram was constructed based on risk scores and clinical features, and its predictive efficacy was verified. The expression of STAM, ANXA5, and MAD2L2 in HCC cell lines was detected by western blotting; subsequently, these genes were knocked down in HCC cell lines by small interfering RNA, and their effects on the proliferation and migration of HCC cell lines were detected by colony formation assay, cck8, wound healing, and transwell assays.

RESULTS

Six genes related to CD8+ TEX were included in the risk-prediction model. The prognosis of patients with HCC in the low-risk group was significantly better than that of those in the high-risk group. Cox regression analysis revealed that the risk score was an independent risk factor for the prognosis of patients with HCC. The differentially expressed genes in patients with high-risk HCC were mainly enriched in the nucleotide-binding oligomerization domain-containing protein-like receptor, hypoxia-inducible factor-1, and tumour programmed cell death protein (PD)-1/PD-L1 immune checkpoint pathways. The CD8+ TEX-related genes STAM, ANXA5, and MAD2L2 were knocked down in HCC cell lines to significantly inhibit cell proliferation and migration. The prediction results of the nomogram based on the risk score showed a good fit and application value.

CONCLUSION

The prediction model based on CD8+ TEX-related genes can predict the prognosis of HCC and provide a theoretical basis for the early identification of patients with poor HCC prognosis.

A novel DNA methylation signature to improve survival prediction of progression-free survival for testicular germ cell tumors

This study aimed to develop a nomogram for predicting the progression-free survival (PFS) of testicular germ cell tumors (TGCT) patients based on DNA methylation signature and clinicopathological characteristics. The DNA methylation profiles, transcriptome data, and clinical information of TGCT patients were obtained from the Cancer Genome Atlas (TCGA) database. Univariate Cox, lasso Cox, and stepwise multivariate Cox regression were applied to identify a prognostic CpG sites-derived risk signature. Differential expression analysis, functional enrichment analysis, immunoinfiltration analysis, chemotherapy sensitivity analysis, and clinical feature correlation analysis were performed to elucidate the differences among risk groups. A prognostic nomogram integrating CpG sites-derived risk signature and clinicopathological features was further established and evaluated likewise. A risk score model based on 7 CpG sites was developed and found to exhibit significant differences among different survival, staging, radiotherapy, and chemotherapy subgroups. There were 1452 differentially expressed genes between the high- and low-risk groups, with 666 being higher expressed and 786 being lower expressed. Genes highly expressed were significantly enriched in immune-related biological processes and related to T-cell differentiation pathways; meanwhile, down-regulated genes were significantly enriched in extracellular matrix tissue organization-related biological processes and involved in multiple signaling pathways such as PI3K-AKT. As compared with the low-risk group, patients in the high-risk group had decreased lymphocyte infiltration (including T-cell and B-cell) and increased macrophage infiltration (M2 macrophages). They also showed decreased sensitivity to etoposide and bleomycin chemotherapy. Three clusters were obtained by consensus clustering analysis based on the 7 CpG sites and showed distinct prognostic features, and the risk scores in each cluster were significantly different. Multivariate Cox regression analysis found that the risk scores, age, chemotherapy, and staging were independent prognostic factors of PFS of TGCT, and the results were used to formulate a nomogram model that was validated to have a C-index of 0.812. Decision curve analysis showed that the nomogram model was superior to other strategies in the prediction of PFS of TGCT. In this study, we successfully established CpG sites-derived risk signature, which might serve as a useful tool in the prediction of PFS, immunoinfiltration, and chemotherapy sensitivity for TGCT patients.

Construction of DNA methylation-based nomogram for predicting biochemical-recurrence-free survival in prostate cancer

This study aimed to develop a DNA methylation-based nomogram for predicting biochemical recurrence in patients with prostate cancer. A DNA methylation signature was obtained via univariate, lasso, and stepwise multivariate Cox regression models. A 11-DNA methylation signature yielded a high evaluative performance for biochemical-recurrence-free survival. Cox regression analysis indicated that 11-DNA methylation signature and Gleason score served as independent risk factors. A nomogram was constructed based on the 11-DNA methylation signature and Gleason score, and C-index as well as the calibration plots demonstrated good performance and clinical application of the nomogram. A DNA methylation-associated nomogram serve as a prognosis stratification tool to predict the biochemical recurrence of prostate cancer patients after radical prostatectomy.

Characterization of glucose metabolism in breast cancer to guide clinical therapy

Background

Breast cancer (BRCA) ranks as a leading cause of cancer death in women worldwide. Glucose metabolism is a noticeable characteristic of the occurrence of malignant tumors. In this study, we aimed to construct a novel glycometabolism-related gene (GRG) signature to predict overall survival (OS), immune infiltration and therapeutic response in BRCA patients.

Materials and methods

The mRNA sequencing and corresponding clinical data of BRCA patients were obtained from public cohorts. Lasso regression was applied to establish a GRG signature. The immune infiltration was evaluated with the ESTIMATE and CIBERSORT algorithms. The drug sensitivity was estimated using the value of IC50, and further forecasted the therapeutic response of each patient. The candidate target was selected in Cytoscape. A nomogram was constructed via the R package of “rms”.

Results

We constructed a six-GRG signature based on CACNA1H, CHPF, IRS2, NT5E, SDC1 and ATP6AP1, and the high-risk patients were correlated with poorer OS (P = 2.515 × 10⁻⁷). M2 macrophage infiltration was considerably superior in high-risk patients, and CD8⁺ T cell infiltration was significantly higher in low-risk patients. Additionally, the high-risk group was more sensitive to Lapatinib. Fortunately, SDC1 was recognized as candidate target and patients had a better OS in the low-SDC1 group. A nomogram integrating the GRG signature was developed, and calibration curves were consistent between the actual and predicted OS.

Conclusions

We identified a novel GRG signature complementing the present understanding of the targeted therapy and immune biomarker in breast cancer. The GRGs may provide fresh insights for individualized management of BRCA patients.

A nomogram based on A-to-I RNA editing predicting overall survival of patients with lung squamous carcinoma

Background

Adenosine-to-inosine RNA editing (ATIRE) is characterized as non-mutational epigenetic reprogramming hallmark of cancer, while little is known about its predictive role in cancer survival.

Methods

To explore survival-related ATIRE events in lung squamous cell carcinoma (LUSC), ATIRE profile, gene expression data, and corresponding clinical information of LUSC patients were downloaded from the TCGA database. Patients were randomly divided into a training (n = 134) and validation cohort (n = 94). Cox proportional hazards regression followed by least absolute shrinkage and selection operator algorithm were performed to identify survival-related ATIRE sites and to generate ATIRE risk score. Then a nomogram was constructed to predict overall survival (OS) of LUSC patients. The correlation of ATIRE level and host gene expression and ATIREs’ effect on transcriptome expression were analyzed.

Results

Seven ATIRE sites that were TMEM120B chr12:122215052A > I, HMOX2 chr16:4533713A > I, CALCOCO2 chr17:46941503A > I, LONP2 chr16:48388244A > I, ZNF440 chr19:11945758A > I, CLCC1 chr1:109474650A > I, and CHMP3 chr2:86754288A > I were identified to generate the risk score, of which high levers were significantly associated with worse OS and progression-free survival in both the training and validation sets. High risk-score was also associated with advanced T stages and worse clinical stages. The nomogram performed well in predicting OS probability of LUSC. Moreover, the editing of ATIRE sites exerted a significant association with expression of host genes and affected several cancer-related pathways.

Conclusions

This is the first comprehensive study to analyze the role of ATIRE events in predicting LUSC survival. The AITRE-based model might serve as a novel tool for LUSC survival prediction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09773-0.

A Pyroptosis-Related Gene Panel for Predicting the Prognosis and Immune Microenvironment of Cervical Cancer

Cervical cancer (CC) is one of the most common malignant tumors of the female reproductive system. And the immune system disorder in patients results in an increasing incidence rate and mortality rate. Pyroptosis is an immune system-related programmed cell death pathway that produces systemic inflammation by releasing pro-inflammatory intracellular components. However, the diagnostic significance of pyroptosis-related genes (PRGs) in CC is still unclear. Therefore, we identified 52 PRGs from the TCGA database and screened three Differentially Expressed Pyroptosis-Related Genes (DEPRGs) in the prognosis of cervical cancer: CHMP4C, GZMB, TNF. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate COX regression analysis were then used to construct a gene panel based on the three prognostic DEPRGs. The patients were divided into high-and low-risk groups based on the median risk score of the panel. According to the Kaplan-Meier curve, there was a substantial difference in survival rates between the two groups, with the high-risk group’s survival rate being significantly lower than the low-risk group’s. The PCA and t-SNE analyses revealed that the panel was able to differentiate patients into high-and low-risk groups. The area under the ROC curve (AUC) shows that the prognostic panel has high sensitivity and specificity. The risk score could then be employed as an independent prognostic factor using univariate and multivariate COX regression analyses paired with clinical data. The analyses of GO and KEGG functional enrichment of differentially expressed genes (DEGs) in the high-and low-risk groups revealed that these genes were primarily engaged in immune response and inflammatory cell chemotaxis. To illustrate immune cell infiltration in CC patients further, we used ssGSEA to compare immune-related cells and immune pathway activation between the high-and low-risk groups. The link between three prognostic DEPRGs and immune-related cells was still being discussed after evaluating immune cell infiltration in the TCGA cohort with “CIBERSORT.” In addition, the GEPIA database and qRT-PCR analysis were used to verify the expression levels of prognostic DEPRGs. In conclusion, PRGs are critical in tumor immunity and can be utilized to predict the prognosis of CC.

LncRNA GACAT3: A Promising Biomarker and Therapeutic Target in Human Cancers

Long non-coding RNAs (lncRNAs) are a class of functional RNA molecules that do not encode proteins and are composed of more than 200 nucleotides. LncRNAs play important roles in epigenetic and gene expression regulation. The oncogenic lncRNA GACAT3 was recently discovered to be dysregulated in many tumors. Aberrant expression of GACAT3 contributes to clinical characteristics and regulates multiple oncogenic processes. The association of GACAT3 with a variety of tumors makes it a promising biomarker for diagnosis, prognosis, and targeted therapy. In this review, we integrate the current understanding of the pathological features, biological functions, and molecular mechanisms of GACAT3 in cancer. Additionally, we provide insight into the utility of GACAT3 as an effective diagnostic and prognostic marker for specific tumors, which offers novel opportunities for targeted therapeutic intervention.

Establishment and verification of a nomogram prediction model of hypertension risk in Xinjiang Kazakhs

Hypertension is the main risk factor for cardiovascular and renal diseases. It is of great importance to develop effective risk prediction models to identify high-risk groups of hypertension. This study is to establish and verify a nomogram model for predicting the risk of hypertension among Kazakh herders in Xinjiang, China.This is a prospective cohort study. Totally, 5327 Kazakh herders from the Nanshan pastoral area of Xinjiang were enrolled. They were randomly divided into the modeling set of 3729 cases (70%) and the validation set of 1598 cases (30%). In the modeling set, univariate analysis, least absolute shrinkage and selection operator regression and multivariate Logistic regression were used to analyze the influencing factors of hypertension, and a nomogram prediction model was constructed. We then validated the model in the validation set, and evaluated the accuracy of the model using receiver operating characteristic and calibration curve.Based on univariate analysis, least absolute shrinkage and selection operator regression and multivariate logistic regression analysis, we identified 14 independent predictors of hypertension in the modeling set, including age, smoking, alcohol consumption, baseline body mass index, baseline diastolic blood pressure, baseline systolic blood pressure, daily salt intake, yak-butter intake, daily oil intake, fruit and vegetable intake, low-density lipoprotein, cholesterol, abdominal circumference, and family history. The area under the receiver operating characteristic curve of the modeling set and the verification set was 0.803 and 0.809, respectively. Moreover, the calibration curve showed a higher agreement between the nomogram prediction and the actual observation of hypertension.The risk prediction nomogram model has good predictive ability and could be used as an effective tool for the risk prediction of hypertension among Kazakh herders in Xinjiang.

Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients

Background

Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV.

Methods

The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets.

Results

A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed.

Conclusion

Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.

Nomogram to Predict Cancer Specific Survival in Patients with Pathological Stage IA Non-small Cell Lung Cancer

We identified the prognostic factors of resected stage IA non-small cell lung cancer (NSCLC) and developed a nomogram, with purpose of defining the high-risk population who may need closer follow-up or more intensive care. Eligible stage IA NSCLC cases from the Surveillance, Epidemiology, and End Results (SEER) database and the Sun Yat-sen University Cancer Center (SYSUCC) were included. Stage IB NSCLCs were also included for evaluating the risk stratification efficacy. Cancer specific survival (CSS) was compared between groups. Statistically significant factors from multivariate analysis were entered into the nomogram. The performance of the nomogram was evaluated by concordance index (C-index) and calibration plots. A total of 23,112 NSCLC cases (SEER stage IA training cohort, N=7,777; SEER stage IA validation cohort, N=7,776; SEER stage IB cohort, N=7,559) from the SEER database were included. 1,304 NSCLC cases (SYSUCC stage IA validation cohort, N=684; SYSUCC stage IB cohort, N=620) from the SYSUCC were also included. Younger age, female, lobectomy, well differentiated, smaller size and more examined lymph nodes were identified as favorable prognostic factors. A nomogram was established. The C-index was 0.68 (95%CI, 0.67-0.69), 0.66 (95% CI, 0.64-0.68) and 0.66 (95% CI, 0.61-0.71) for the SEER training cohort, SEER validation cohort and SYSUCC validation cohort. A risk classification system was constructed to stratify stage IA NSCLC into low-risk subgroup and high-risk subgroup. The CSS curves of these two subgroups showed statistically significant distinctions. This nomogram delivered a prognostic prediction for stage IA NSCLC and may aid individual clinical practice.

BC-TFdb: a database of transcription factor drivers in breast cancer

Transcription factors (TFs) are DNA-binding proteins, which regulate many essential biological functions. In several cancer types, TF function is altered by various direct mechanisms, including gene amplification or deletion, point mutations, chromosomal translocations, expression alterations, as well as indirectly by non-coding DNA mutations influencing the binding of the TF. TFs are also actively involved in breast cancer (BC) initiation and progression. Herein, we have developed an open-access database, BC-TFdb (Breast Cancer Transcription Factors database), of curated, non-redundant TF involved in BC. The database provides BC driver TFs related information including genomic sequences, proteomic sequences, structural data, pathway information, mutations information, DNA binding residues, survival and therapeutic resources. The database will be a useful platform for researchers to obtain BC-related TF-specific information. High-quality datasets are downloadable for users to evaluate and develop computational methods for drug designing against BC. Database URL: https://www.dqweilab-sjtu.com/index.php.

circ-ANXA7 facilitates lung adenocarcinoma progression via miR-331/LAD1 axis

Background

Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer, with a poor prognosis. The roles of circular RNAs (circRNAs) in tumors have been initially clarified. In this study, we probed into the functions and underlying molecular mechanisms of circ-ANXA7 in LUAD.

Methods

According to circRNA microarray analysis based on 40 pairs of LUAD tissues and non-tumor tissues, a novel circ-ANXA7 was up-regulated in LUAD, which was verified in LUAD tissues and cells by RT-qPCR. Correlation between its expression and clinical features of LUAD was analyzed. When transfected with sh-circ-ANXA7, proliferation, invasion, and migration of LUAD cells were determined by a series of functional assays. Furthermore, tumor growth was investigated in nude mice injected with sh-circ-ANXA7. Dual luciferase report and gain and loss assays were used to confirm the relationships between circ-ANXA7 and miR-331, miR-331 and LAD1.

Results

circ-ANXA7 was up-regulated in LUAD tissues and cells. Its high expression promoted proliferation, migration, and invasion of LUAD cells as well as tumor growth. High circ-ANXA7 expression usually predicted a poorer prognosis for LUAD patients. Furthermore, circ-ANXA7 could accelerate proliferation and invasion of LUAD cells by targeting miR-331. miR-331 directly bound to the 3′-UTR of LAD1. LAD1 induced proliferation and invasion of LUAD cells, which was reversed after co-transfection with circ-ANXA7 knockdown. LAD1 expression could be an independent prognostic marker for LUAD by univariate and multivariate analysis.

Conclusions

Our research identified a novel circ-ANXA7 for LUAD, which could facilitate proliferation, migration, and invasion of LUAD cells by miR-331/ LAD1 axis. circ-ANXA7 could become a promising prognosis and treatment target for LUAD.

Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival

To identify a glycolysis-related gene signature for the evaluation of prognosis in patients with breast cancer, we analyzed the data of a training set from TCGA database and four validation cohorts from the GEO and ICGC databases which included 1,632 patients with breast cancer. We conducted GSEA, univariate Cox regression, LASSO, and multiple Cox regression analysis. Finally, an 11-gene signature related to glycolysis for predicting survival in patients with breast cancer was developed. And Kaplan–Meier analysis and ROC analyses suggested that the signature showed a good prognostic ability for BC in the TCGA, ICGC, and GEO datasets. The analyses of univariate Cox regression and multivariate Cox regression revealed that it’s an important prognostic factor independent of multiple clinical features. Moreover, a prognostic nomogram, combining the gene signature and clinical characteristics of patients, was constructed. These findings provide insights into the identification of breast cancer patients with a poor prognosis.

DNA methylation profiling to predict recurrence risk in stage Ι lung adenocarcinoma: Development and validation of a nomogram to clinical management

Increasing evidence suggested DNA methylation may serve as potential prognostic biomarkers; however, few related DNA methylation signatures have been established for prediction of lung cancer prognosis. We aimed at developing DNA methylation signature to improve prognosis prediction of stage I lung adenocarcinoma (LUAD). A total of 268 stage I LUAD patients from the Cancer Genome Atlas (TCGA) database were included. These patients were separated into training and internal validation datasets. GSE39279 was used as an external validation set. A 13‐DNA methylation signature was identified to be crucially relevant to the relapse‐free survival (RFS) of patients with stage I LUAD by the univariate Cox proportional hazard analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox proportional hazard analysis in the training dataset. The Kaplan‐Meier analysis indicated that the 13‐DNA methylation signature could significantly distinguish the high‐ and low‐risk patients in entire TCGA dataset, internal validation and external validation datasets. The receiver operating characteristic (ROC) analysis further verified that the 13‐DNA methylation signature had a better value to predict the RFS of stage I LUAD patients in internal validation, external validation and entire TCGA datasets. In addition, a nomogram combining methylomic risk scores with other clinicopathological factors was performed and the result suggested the good predictive value of the nomogram. In conclusion, we successfully built a DNA methylation‐associated nomogram, enabling prediction of the RFS of patients with stage I LUAD.