Development and validation of a risk prediction model and nomogram for colon adenocarcinoma based on methylation-driven genes.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhu L
PROVIDER: S-EPMC8266312 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Background/Aims: The incidence of gastric cancer (GC) ranks fifth among common tumors and GC is the third leading cause of cancer-related death worldwide. The aim of this study was to develop and validate a nomogram for predicting the overall survival (OS) of patients with GC. Methods: DNA methylation (DNAm)-driven genes were identified by integrating DNAm and gene expression profiling analyses from The Cancer Genome Atlas (TCGA) GC cohort. Then, a risk score model was built based on Kaplan-Meier (K-M), least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses. After analyzing the clinical parameters, a nomogram was constructed and assessed. Another cohort (GSE62254) was used for external validation. Results: Thirteen differentially expressed DNAm-driven genes were narrowed down to a six-gene signature (PODN, NPY, MICU3, TUBB6 and RHOJ were hypermethylated, and MYO1A was hypomethylated), which was associated with OS (P < 0.05) after survival and LASSO regression analyses. These differentially expressed genes (DEGs) with altered DNAm statuses were included in the prognostic risk score model. The univariate Cox regression analysis indicated that risk score, age, and number of positive lymph nodes were significantly associated with survival time in GC patients. The multivariate Cox regression analysis also indicated that these variables were significant prognostic factors for GC. A nomogram including these variables was constructed, and its performance in predicting the 1-, 3- and 5-year survival outcomes of GC patients was estimated through time-dependent receiver operating characteristic (ROC) curves. In addition, the clinical benefit of this model was revealed by decision curve analysis (DCA). Pathway enrichment analysis suggested that these DNAm-driven genes might impact tumor progression by affecting signaling pathways such as the "ECM RECEPTOR INTERACTION" and "DNA REPLICATION" pathways. Conclusions: The altered status of the DNAm-driven gene signature (PODN, MYO1A, NPY, MICU3, TUBB6 and RHOJ) was significantly associated with the OS of GC patients. A nomogram incorporating risk score, age and number of positive lymph nodes can be conveniently used to facilitate the individualized prediction of OS in patients with GC.
| S-EPMC7053317 | biostudies-literature
Project description:Background: DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC). Methods: DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package MethylMix to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features. Results: We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC. Conclusion: The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.
| S-EPMC8458765 | biostudies-literature
Project description:BackgroundAccurate diagnosis of early lung cancer from small pulmonary nodules (SPN) is challenging in clinical setting. We aimed to develop a radiomic nomogram to differentiate lung adenocarcinoma from benign SPN.MethodsThis retrospective study included a total of 210 pathologically confirmed SPN (≤ 10 mm) from 197 patients, which were randomly divided into a training dataset (n = 147; malignant nodules, n = 94) and a validation dataset (n = 63; malignant nodules, n = 39). Radiomic features were extracted from the cancerous volumes of interest on contrast-enhanced CT images. The least absolute shrinkage and selection operator (LASSO) regression was used for data dimension reduction, feature selection, and radiomic signature building. Using multivariable logistic regression analysis, a radiomic nomogram was developed incorporating the radiomic signature and the conventional CT signs observed by radiologists. Discrimination and calibration of the radiomic nomogram were evaluated.ResultsThe radiomic signature consisting of five radiomic features achieved an AUC of 0.853 (95% confidence interval [CI]: 0.735-0.970), accuracy of 81.0%, sensitivity of 82.9%, and specificity of 77.3%. The two conventional CT signs achieved an AUC of 0.833 (95% CI: 0.707-0.958), accuracy of 65.1%, sensitivity of 53.7%, and specificity of 86.4%. The radiomic nomogram incorporating the radiomic signature and conventional CT signs showed an improved AUC of 0.857 (95% CI: 0.723-0.991), accuracy of 84.1%, sensitivity of 85.4%, and specificity of 81.8%. The radiomic nomogram had good calibration power.ConclusionThe radiomic nomogram might has the potential to be used as a non-invasive tool for individual prediction of SPN preoperatively. It might facilitate decision-making and improve the management of SPN in the clinical setting.
| S-EPMC7278188 | biostudies-literature
Project description:PurposeThe exploration and interpretation of DNA methylation-driven genes might contribute to molecular classification, prognostic prediction and therapeutic choice. In this study, we built a prognostic risk model via integrating analysis of the transcriptome and methylation profile for patients with gastric cancer (GC).MethodsThe mRNA expression profiles, DNA methylation profiles and corresponding clinicopathological information of 415 GC patients were downloaded from The Cancer Genome Atlas (TCGA). Differential expression and correlation analysis were performed to identify DNA methylation-driven genes. The candidate genes were selected by univariate Cox regression analyses followed by the least absolute shrinkage and selection operator (LASSO) regression. A prognostic risk nomogram model was then built together with clinicopathological parameters.Results5 DNA methylation-driven genes (CXCL3, F5, GNAI1, GAMT and GHR) were identified by integrated analyses and selected to construct the prognostic risk model with clinicopathological parameters. High expression and low DNA hypermethylation of F5, GNAI1, GAMT and GHR, as well as low expression and high DNA hypomethylation of CXCL3 were significantly associated with poor prognosis rates, respectively. The high-risk group showed a significantly shorter prognosis than the low-risk group in the TCGA dataset (HR = 0.212, 95% CI = 0.139-0.322, P = 2e-15). The final nomogram model showed high predictive efficiency and consistency in the training and validation group.ConclusionWe construct and validate a prognostic nomogram model for GC based on five DNA methylation-driven genes with high performance and stability. This nomogram model might be a powerful tool for prognosis evaluation in the clinic and also provided novel insights into the epigenetics in GC.
| S-EPMC7732649 | biostudies-literature
Project description:BackgroundDNA methylation is an important part of epigenetic modification, and its abnormality is closely related to esophageal adenocarcinoma (EAC). This study was aimed at using bioinformatics analysis to identify methylation-driven genes (MDGs) in EAC patients and establish a risk model as a biological indicator of EAC prognosis.MethodDownloaded EAC DNA methylation, transcriptome, and related clinical data from TCGA database. MethylMix was used to identify MDGs. R package clusterProfiler and the ConsensusPathDB online database were used to analyze the rich functions and pathways of these MDGs. The prognostic risk model was established by univariate Cox regression, Lasso regression, and multivariate Cox regression analysis. Finally each MDG in the model were carried out through the survival R package.ResultsA total of 273 MDGs were identified, which were enriched in transcriptional regulation and embryonic organ morphogenesis. Cox regression analysis established a risk model consisting of GPBAR1, OLFM4, FOXI2, and CASP10. In addition, further survival analysis revealed that OLFM4 and its two related sites were significantly related to the EAC patients' survival.ConclusionIn summary, this study used bioinformatics methods to identify EAC MDGs and established a reliable risk prognosis model. It provided potential biomarkers for the early treatment and prognosis evaluation of EAC.
| S-EPMC8213479 | biostudies-literature
Project description:Objective Colon adenocarcinoma (COAD) is one of the most prevalent cancers worldwide. However, the pyroptosis-related lncRNAs of COAD have not been deeply examined and validated. Here, we constructed and validated a risk model on pyroptosis-related lncRNAs in COAD. Methods The RNA sequencing transcriptome and clinical data of COAD patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed pyroptosis-related mRNAs and mRNA-lncRNA coexpression network were identified. After univariate and multifactorial cox analyses of prognosis-related lncRNAs, a risk model was constructed. Next, we analyzed the differences in immune infiltration, immune checkpoint blockade-, immune checkpoint-, and N6-methyladenosine-related gene expressions between the high- and low-risk groups. RT-qPCR was used to validate the expression of lncRNAs. Result A risk model was constructed based on 9 pyroptosis-related lncRNAs and separated COAD patients into the high- and low-risk groups. Immune infiltration analysis and immune checkpoint blockade-, immune checkpoint-, and N6-methyladenosine-related genes showed significant differences between the two subgroups. RT-qPCR showed that the 9 pyroptosis-related lncRNAs could be used as prognostic indicators. Conclusion A novel risk model based on pyroptosis-related lncRNAs was constructed and demonstrated that these lncRNAs might be used as independent prognostic biomarkers. This will also assist shed light on the COAD prognosis and therapy.
| S-EPMC9509522 | biostudies-literature
Project description:ObjectiveThis research aims to construct and authenticate a comprehensive predictive model for all-cause mortality, based on a multifaceted array of risk factors.MethodsThe derivation cohort for this study was the Chinese Longitudinal Healthy Longevity Survey (CLHLS), while the Healthy Ageing and Biomarkers Cohort Study (HABCS) and the China Health and Retirement Longitudinal Study (CHARLS) were used as validation cohorts. Risk factors were filtered using lasso regression, and predictive factors were determined using net reclassification improvement. Cox proportional hazards models were employed to establish the mortality risk prediction equations, and the model's fit was evaluated using a discrimination concordance index (C-index). To evaluate the internal consistency of discrimination and calibration, a 10x10 cross-validation technique was employed. Calibration plots were generated to compare predicted probabilities with observed probabilities. The prediction ability of the equations was demonstrated using nomogram.ResultsThe CLHLS (mean age 88.08, n = 37074) recorded 28158 deaths (179683 person-years) throughout the course of an 8-20 year follow-up period. Additionally, there were 1384 deaths in the HABCS (mean age 86.74, n = 2552), and 1221 deaths in the CHARLS (mean age 72.48, n = 4794). The final all-cause mortality model incorporated demographic characteristics like age, sex, and current marital status, as well as functional status indicators including cognitive function and activities of daily living. Additionally, lifestyle factors like past smoking condition and leisure activities including housework, television viewing or radio listening, and gardening work were included. The C-index for the derivation cohort was 0.728 (95% CI: 0.724-0.732), while the external validation results for the CHARS and HABCS cohorts were 0.761 (95% CI: 0.749-0.773) and 0.713 (95% CI: 0.697-0.729), respectively.ConclusionThis study introduces a reliable, validated, and acceptable mortality risk predictor for older adults in China. These predictive factors have potential applications in public health policy and clinical practice.
| S-EPMC10825771 | biostudies-literature
Project description:Background Mitophagy is used by eukaryotic cells to eliminate damaged mitochondria. The deregulation of this process can lead to an accumulation of dysfunctional mitochondria and is implicated in carcinogenesis and tumorigenesis. Despite increasing evidence that mitophagy is involved in the development of colon cancer, the role of mitophagy-related genes (MRGs) in colon adenocarcinoma (COAD) prognosis and treatment remains largely unknown. Methods Differential analysis was used to identify differentially expressed mitophagy-related genes associated with COAD and conduct key module screening. Cox regression and least absolute shrinkage selection operator, and other analyses were used to characterize prognosis-related genes and verify the feasibility of the model. The model was tested using GEO data and a nomogram was constructed for future clinical application. The level of immune cell infiltration and immunotherapy were compared between the two groups, and sensitivity to treatment with many commonly used chemotherapeutic agents was assessed in individuals with different risk factors. Finally, qualitative reverse transcription polymerase chain reaction and western blotting were performed to assess the expression of prognosis-related MRGs. Results A total of 461 differentially expressed genes were mined in COAD. Four prognostic genes, PPARGC1A, SLC6A1, EPHB2, and PPP1R17, were identified to construct a mitophagy-related gene signature. The feasibility of prognostic models was assessed using Kaplan-Meier analysis, time-dependent receiver operating characteristics, risk scores, Cox regression analysis, and principal component analysis. At 1, 3, and 5 years, the area under the receiver operating characteristic curves were 0.628, 0.678, and 0.755, respectively, for TCGA cohort, and 0.609, 0.634, and 0.640, respectively, for the GEO cohort. Drug sensitivity analysis found that camptothecin, paclitaxel, bleomycin, and doxorubicin were significantly different between low- and high-risk patients. The qPCR and western blotting results of clinical samples further confirmed the public database results. Conclusions This study successfully constructed a mitophagy-related gene signature with significant predictive value for COAD, informing new possibilities for the treatment of this disease.
| S-EPMC10079075 | biostudies-literature
Project description:BackgroundClinical staging is essential for clinical decisions but remains imprecise. We purposed to construct a novel survival prediction model for improving clinical staging system (cTNM) for patients with esophageal adenocarcioma (EAC).MethodsA total of 4180 patients diagnosed with EAC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and included as the training cohort. Significant prognostic variables were identified for nomogram model development using multivariable Cox regression. The model was validated internally by bootstrap resampling, and then subjected to external validation with a separate cohort of 886 patients from 2 institutions in China. The prognostic performance was measured by concordance index (C-index), Akaike information criterion (AIC) and calibration plots. Different risk groups were stratified by the nomogram scores.ResultsA total of six variables were determined related with survival and entered into the nomogram construction. The calibration curves showed satisfied agreement between nomogram-predicted survival and actual observed survival for 1-, 3-, and 5-year overall survival. By calculating the AIC and C-index values, our nomogram presented superior discriminative and risk-stratifying ability than current TNM staging system. Significant distinctions in survival curves were observed between different risk subgroups stratified by nomogram scores.ConclusionThe established and validated nomogram presented better risk-stratifying ability than current clinical staging system, and could provide a convenient and reliable tool for individual survival prediction and treatment strategy making.
| S-EPMC8445330 | biostudies-literature
Project description:Background: Stomach adenocarcinoma (STAD) is a significant global health problem. It is urgent to identify reliable predictors and establish a potential prognostic model. Methods: RNA-sequencing expression data of patients with STAD were downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) database. Gene expression profiling and survival analysis were performed to investigate differentially expressed genes (DEGs) with significant clinical prognosis value. Overall survival (OS) analysis and univariable and multivariable Cox regression analyses were performed to establish the prognostic model. Protein-protein interaction (PPI) network, functional enrichment analysis, and differential expression investigation were also performed to further explore the potential mechanism of the prognostic genes in STAD. Finally, nomogram establishment was undertaken by performing multivariate Cox regression analysis, and calibration plots were generated to validate the nomogram. Results: A total of 229 overlapping DEGs were identified. Following Kaplan-Meier survival analysis and univariate and multivariate Cox regression analysis, 11 genes significantly associated with prognosis were screened and five of these genes, including COL10A1, MFAP2, CTHRC1, P4HA3, and FAP, were used to establish the risk model. The results showed that patients with high-risk scores have a poor prognosis, compared with those with low-risk scores (p = 0.0025 for the training dataset and p = 0.045 for the validation dataset). Subsequently, a nomogram (including TNM stage, age, gender, histologic grade, and risk score) was created. In addition, differential expression and immunohistochemistry stain of the five core genes in STAD and normal tissues were verified. Conclusion: We develop a prognostic-related model based on five core genes, which may serve as an independent risk factor for survival prediction in patients with STAD.
| S-EPMC8727349 | biostudies-literature