Nomogram for predicting overall survival time of patients with stage IV colorectal cancer

Raddeanin A Inhibits Colorectal Cancer Growth and Ameliorates Oxaliplatin Resistance Through the WNT/β-Catenin Signaling Pathway

Background: Chemotherapy based on oxaliplatin (OXA) is the first-line treatment for advanced colorectal cancer (CRC), and acquired resistance to OXA is the main reason for clinical treatment failure in CRC. Methods: To search for compounds that can reverse OXA resistance, we screened a small molecule inhibitor drug library and identified a drug, Raddeanin A (RA), that enhanced the anticancer effect of OXA. Using human CRC cell lines, CRC organoid models, and in vivo subcutaneous tumorigenic studies, we determined that RA inhibits the proliferation of CRC cells by promoting apoptosis and inducing cell cycle arrest. Results: We constructed OXA-resistant CRC cell lines and demonstrated that RA enhances the sensitivity of these cells to OXA. Further experiments showed that the mechanism by which RA enhanced the anticancer effects of OXA in CRC was by inhibiting the activation of the WNT/β-catenin signaling pathway. Conclusions: Because RA has been shown to be biocompatible in animal models, there is a possibility that RA could be developed as a sensitizer for resistant cancer cells or as a novel lead compound to enhance the therapeutic efficacy of OXA in resistant CRCs.

Establishment and Validation of a Prognostic Nomogram for Predicting Postoperative Overall Survival in Advanced Stage III-IV Colorectal Cancer Patients

BACKGROUND

Most colorectal cancer (CRC) patients are at an advanced stage when they are first diagnosed. Risk factors for predicting overall survival (OS) in advanced stage CRC patients are crucial, and constructing a prognostic nomogram model is a scientific method for survival analysis.

METHODS

A total of 2956 advanced stage CRC patients were randomised into training and validation groups at a 7:3 ratio. Univariate and multivariate Cox proportional hazards regression analyses were used to screen risk factors for OS and subsequently construct a prognostic nomogram model for predicting 1-, 3-, 5-, 8- and 10-year OS of advanced stage CRC patients. The performance of the model was demonstrated by the area under the curve (AUC) values, calibration curves and decision curve analysis (DCA). Kaplan-Meier curves were used to plot the survival probabilities for different strata of each risk factor.

RESULTS

There was no statistically significant difference (p > 0.05) in the 32 clinical variables between patients in the training and validation groups. Univariate and multivariate Cox proportional hazards regression analyses demonstrated that age, location, TNM, chemotherapy, liver metastasis, lung metastasis, MSH6, CEA, CA199, CA125 and CA724 were risk factors for OS. We estimated the AUC values for the nomogram model to predict 1-, 3-, 5-, 8- and 10-year OS, which in the training group were 0.826 (95% CI: 0.807-0.845), 0.836 (0.819-0.853), 0.839 (0.820-0.859), 0.835 (0.809-0.862) and 0.825 (0.779-0.870) respectively; in the validation group, the corresponding AUC values were 0.819 (0.786-0.852), 0.831 (0.804-0.858), 0.830 (0.799-0.861), 0.815 (0.774-0.857) and 0.802 (0.723-0.882) respectively. Finally, the 1-, 3-, 5-, 8- and 10-year OS rates for advanced stage CRC patients were 73.4 (71.8-75.0), 49.5 (47.8-51.4), 43.3 (41.5-45.2), 40.1 (38.1-41.9) and 38.6 (36.6-40.8) respectively.

CONCLUSION

We constructed and validated an original nomogram for predicting the postoperative OS of advanced stage CRC patients, which can help facilitates physicians to accurately assess the individual survival of postoperative patients and identify high-risk patients.

Survival outcome and prognostic factors for early-onset and late-onset metastatic colorectal cancer: a population based study from SEER database

Colorectal cancer is the third most common cancer worldwide and there has been a concerning increase in the incidence rate of colorectal cancer among individuals under the age of 50. This study compared the survival outcome between early-onset and late-onset metastatic colorectal cancer to find the differences and identify their prognostic factors. We obtained patient data from SEER database. Survival outcome was estimated using Kaplan-Meier survival curves and compared using the log-rank test. Univariate and multivariate analyses were conducted utilizing COX models to identify their independent prognostic factors. A total of 10,036 early-onset metastatic colorectal (EOCRC) cancer patients and 56,225 late-onset metastatic colorectal cancer (LOCRC) patients between 2010 and 2019 were included in this study. EOCRC has more survival benefits than LOCRC. Tumor primary location (p < 0.001), the location of metastasis (p < 0.001) and treatment modalities (p < 0.001) affect the survival outcomes between these two groups of patients. Female patients had better survival outcomes in EOCRC group (p < 0.001), but no difference was found in LOCRC group (p = 0.57). In conclusion, our study demonstrated that EOCRC patients have longer survival time than LOCRC patients. The sex differences in survival of metastatic colorectal cancer patients are associated with patients' age. These findings contribute to a better understanding of the differences between metastatic EOCRC and LOCRC, and can help inform the development of more precise treatment guidelines to improve prognosis.

Construction of a Prognostic Model Based on Methylation-Related Genes in Patients with Colon Adenocarcinoma

Purpose

Colon adenocarcinoma (COAD) is the second leading cause of death in the world, and the new incidence rate ranks third among all cancers. Abnormal DNA methylation is related to the occurrence and development of tumors. In this study, we aimed to identify genes associated with abnormal methylation in COAD.

Methods

COAD transcriptome data, methylation data and clinical information were downloaded from the TCGA database and GEO database. The differentially expressed genes (DEGs) and methylated genes (DMGs) were analyzed and identified in COAD. PCA analysis was applied to divide COAD into subtypes, and the survival and immune cell infiltration of each subtype were evaluated. Cox and LASSO analyses were performed to construct COAD risk model. GSEA was used to evaluate the enrichment pathways. The Kaplan-Meier was used to analyze the difference in survival. ROC curve was plotted to evaluate the accuracy of the model, and GSE17536 was used to verify the accuracy of the risk model. The risk model is combined with the clinicopathological characteristics of COAD patients to perform multivariate Cox regression analysis to obtain independent risk factors and draw nomograms.

Results

In total, 4564 DEGs and 1093 DMGs were screened, among which 298 were found to be overlapping genes. For 220 of these overlapping genes, the methylation was significantly negatively correlated to expression levels. An optimal signature from 4 methylated biomarkers was identified to construct the prognostic model.

Conclusion

Our study identified 4 methylated biomarkers in the COAD. Then, we constructed the risk model to provide a theoretical basis and reference value for the research and treatment of COAD.