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Background: 7-Methylguanosine(m7G) contributes greatly to its pathogenesis and progression in colorectal cancer. We proposed building a prognostic model of m7G-related LncRNAs. Our prognostic model was used to identify differences between hot and cold tumors.Methods: The study included 647 colorectal cancer patients (51 cancer-free patients and 647 cancer patients) from The Cancer Genome Atlas (TCGA). We identified m7G-related prognostic lncRNAs by employing the univariate Cox regression method. Assessments were conducted using univariate Cox regression, multivariate Cox regression, receiver operating characteristics (ROC), nomogram, calibration curves, and Kaplan-Meier analysis. All of these procedures were used with the aim of confirming the validity and stability of the model. Besides these two analyses, we also conducted half-maximal inhibitory concentration (IC50), immune analysis, principal component analysis (PCA), and gene set enrichment analysis (GSEA). The entire set of m7G-related (lncRNAs) with respect to cold and hot tumors has been divided into two clusters for further discussion of immunotherapy.Results: The risk model was constructed with 17 m7G-related lncRNAs. A good correlation was found between the calibration plots and the prognosis prediction in the model. By assessing IC50 in a significant way across risk groups, systemic treatment can be guided. By using clusters, it may be possible to distinguish hot and cold tumors effectively and to aid in specific therapeutic interventions. Cluster 1 was identified as having the highest response to immunotherapy drugs and thus was identified as the hot tumor.Conclusion: This study shows that 17 m7G-related lncRNA can be used in clinical settings to predict prognosis and use them to determine whether a tumor is cold or hot in colorectal cancer and improve the individualization of treatment.

Background The aim of this study was to develop comprehensive and effective nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) rates in patients with colorectal mucinous adenocarcinoma (CRMA). Methods A total of 4711 CRMA patients who underwent radical surgery between 2010 and 2018 from the Surveillance, Epidemiology, and End Results (SEER) database were collected and randomized into development (n=3299) and validation (n=1412) cohorts at a ratio of 7:3 for model development and validation. OS and CSS nomograms were developed using the prognostic factors from the development cohort after multivariable Cox regression analysis. The performance of the nomograms was evaluated using Harrell’s concordance index (C-index), calibration diagrams, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Results The study included 4711 patients. Multivariate Cox regression analysis demonstrated that age, tumor size, grade, pT stage, pN stage, M stage, carcinoembryonic antigen, perineural invasion, tumor deposits, regional nodes examined, and chemotherapy were correlated with OS and CSS. Marital status was independently related to OS. In the development and validation cohorts, the C-index of OS was 0.766 and 0.744, respectively, and the C-index of CSS was 0.826 and 0.809, respectively. Calibration curves and ROC curves showed predictive accuracy. DCA showed that the nomograms had excellent potency over the 8th edition of the TNM staging system with higher clinical net benefits. Significant differences in OS and CSS were observed among low-, medium-, and high-risk groups. Conclusions Nomograms were developed for the first time to predict personalized 1-, 3-, and 5-year OS and CSS in CRMA postoperative patients. External and internal validation confirmed the excellent discrimination and calibration ability of the nomograms. The nomograms can help clinicians design personalized treatment strategies and assist with clinical decisions.

BackgroundThe aim of this study was to develop comprehensive and effective nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) rates in patients with colorectal mucinous adenocarcinoma (CRMA). MethodsA total of 4711 CRMA patients who underwent radical surgery between 2010 and 2018 from the Surveillance, Epidemiology, and End Results (SEER) database were collected and randomized into development (n=3,299) and validation (n=1,412) cohorts at a ratio of 7:3 for model development and validation. OS and CSS nomograms were developed using the prognostic factors from the development cohort after multivariable Cox regression analysis. The performance of the nomograms was evaluated using Harrell’s concordance index (C-index), calibration diagrams, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA).ResultsThe study included 4,711 patients. Multivariate Cox regression analysis demonstrated that age, tumor size, grade, pT stage, pN stage, M stage, carcinoembryonic antigen, perineural invasion, tumor deposits, regional nodes examined, and chemotherapy were correlated with OS and CSS. Marital status was independently related to OS. In the development and validation cohorts, the C-index of OS was 0.766 and 0.744, respectively, and the C-index of CSS was 0.826 and 0.809, respectively. Calibration curves and ROC curves showed predictive accuracy. DCA showed that the nomograms had excellent potency over the 8th edition of TNM staging system with higher clinical net benefits. Significant differences in OS and CSS were observed among low-, medium-, and high-risk groups.ConclusionsNomograms were developed for the first time to predict personalized 1-, 3-, and 5-year OS and CSS in CRMA postoperative patients. External and internal validation confirmed the excellent discrimination and calibration ability of the nomograms. The nomograms can help clinicians design personalized treatment strategies and assist with clinical decisions.

Background: Gastric cancer (GC) is one of the most common cancers in the world. Patients with GC who experienced early relapse have poor prognosis. We aim to develop an early relapse-associated gene signature to optimize prognosis prediction in patients with replapsing GC. Methods: The GC cohorts including GSE62254 set (N=300) and GSE15459 set (N=192) were extracted from Gene Expression Omnibus (GEO) database. Propensity score matching (1:1) based on pathological stage was executed between patients with early relapse and long-term survival from GSE62254 set. Global transcriptome analysis was performed between the two groups to identify early relapse-associated gene. Based on the differentially expressed genes, we developed a classifier incorporating 5 genes that using LASSO Cox regression model. The signature’s prognostic value was internally validated in 210 GC patients and validated in GSE15459 set externally. The patients from GSE62254 set could be divided into high-risk or low-risk group.Results: In the train set, patients in high-risk group had poor prognosis as compared to those in low-risk group [hazard ratio (HR): 3.002, 95% confidence interval (CI): 2.132-4.226, P<0.001)]. Good reproducibility for the prognostic value of early relapse-associated gene signature was verified in the internal validation set (HR: 2.772, 95% CI: 1.836–4.184, P<0.001) and another external validation set (HR: 1.733, 95% CI: 1.149–2.614, P=0.009). Also, we developed a nomogram which integrated the five mRNA classifier, pathological stage and lymph node ratio (LNR) to evaluate prognosis based on GSE62254 set. Time-dependent receiver-operating characteristic at 1 year demonstrated that integrated signature had better prognostic accuracy [area under curve (AUC=0.849)] than the American Joint Commission on Cancer TNM staging system (AUC=0.773) and LNR (AUC=0.811) in GSE62254 set. Conclusions: This study suggest that an early relapse-associated gene signature that can robustly divide gastric cancer patients into two groups with distinctive prognosis. This classifier may contribute to select gastric cancer patients with poor prognosis who require more frequent follow-up and more aggressive therapeutic intervention.