Nomogram to Predict the Overall Survival of Colorectal Cancer Patients: A Multicenter National Study

ADAR1 Promotes Invasion and Migration and Inhibits Ferroptosis via the FAK/AKT Pathway in Colorectal Cancer

The role of adenosine deaminase acting on RNA1 (ADAR1) in colorectal cancer (CRC) is poorly understood. This study investigated the roles and underlying molecular mechanisms of ADAR1 and its isoforms, explored the correlations between ADAR1 expression and the immune microenvironment and anticancer drug sensitivity, and examined the potential synergy of using ADAR1 expression and clinical parameters to determine the prognosis of CRC patients. CRC samples showed significant upregulation of ADAR1, and high ADAR1 expression was correlated with poor prognosis. Silencing ADAR1 inhibited the proliferation, invasion, and migration of CRC cells and induced ferroptosis by suppressing FAK/AKT activation, and the results of rescue assays were consistent with these mechanisms. Both ADAR1-p110 and ADAR1-p150 were demonstrated to regulate the FAK/AKT pathway, with ADAR1-p110 playing a particularly substantial role. In evaluating the prognosis of CRC patients, combining ADAR1 expression with clinical parameters produced a substantial synergistic effect. The in vivo tumorigenesis of CRC was significantly inhibited by silencing ADAR1. Furthermore, ADAR1 expression was positively correlated with tumor mutational burden (TMB) and microsatellite status (p < 0.05), indicating that ADAR1 plays a complex role in CRC immunotherapy. In conclusion, ADAR1 plays oncogenic roles in CRC both in vitro and in vivo, potentially by inhibiting ferroptosis via downregulation of the FAK/AKT pathway. Thus, ADAR1 serves as a potential prognostic biomarker and a promising target for CRC therapy.

Development and validation of a dynamic nomogram for individualized prediction of survival in patients with colon cancer

Current tools for predicting survival outcomes in colon cancer patients predominantly rely on clinical and pathologic characteristics. However, accumulating evidence demonstrates a significant correlation between nutritional status and patient outcomes. This study aimed to establish a new dynamic nomogram for individualized prediction of postoperative overall survival (OS). The clinicopathological and nutritional data of colon cancer patients undergoing radical resection were retrospectively collected and randomly divided into the primary and validation cohorts. Risk factors on OS rates were investigated by Cox analyses and, the nomogram was constructed using significant predictors. Among 1,024 patients, 341 deaths were observed after median follow-up of 54 months. Five independent prognostic factors, including nutritional status assessments, were incorporated into the nomogram. The C-index regarding 1-, 3-, and 5-year OS were 0.830, 0.859, and 0.757 in the primary cohort and 0.843, 0.870, and 0.773 in the validation cohort, respectively. Calibration curves for the probability of OS exhibited an optimal agreement. Decision curve analyses revealed the greater application value of the nomogram than the TNM staging system. Based on the nomogram, patients could be stratified into three scenarios with significant prognostic classification (P < 0.0001). In conclusion, we developed and validated an easy-to-use dynamic nomogram for predicting postoperative OS in colon cancer patients.

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.

A nomogram for predicting colorectal cancer liver metastasis using circulating tumor cells from the first drainage vein

PURPOSE

To use circulating tumor cells (CTC) from the first drainage vein (FDV) of the primary lesion and other clinically relevant parameters to construct a nomogram for predicting liver metastasis in colorectal cancer (CRC) patients, and to provide a theoretical basis for clinical diagnosis and treatment.

METHODS

Information from 343 CRC patients was collected and a database was established. Multivariate logistic analysis was used to identify independent factors for colorectal cancer liver metastasis(mCRC) and nomograms were constructed. Receiver operating characteristic curves(ROC), calibration plots, and decision curve analysis (DCA) were used to assess discrimination, agreement with actual risk, and the clinical utility of the prediction model, respectively.

RESULT

CTC levels in FDV were significantly higher in patients with liver metastasis than in those without liver metastasis. Logistic multivariate analysis showed that vascular invasion, T stage, carcinoembryonic antigen (CEA), CA19-9, and CTC could be used as predictors to construct nomograms. The nomograms showed good discriminatory ability in predicting mCRC, with area under the curve (AUC) values of 0.871 [95 % CI: 0.817-0.924) and 0.891 (95 % CI: 0.817-0.964) for the training and validation sets, respectively.] The calibration curves of both the training and validation sets showed that the model was effective in predicting the probability of mCRC. DCA was used to evaluate this predictive model and showed good net clinical benefit.

CONCLUSION

We developed and validated a nomogram model based on the combination of CTC in the FDV with other clinical parameters to better predict the occurrence of mCRC.

Post-diagnosis adiposity and colorectal cancer prognosis: A Global Cancer Update Programme (CUP Global) systematic literature review and meta-analysis

The adiposity influence on colorectal cancer prognosis remains poorly characterised. We performed a systematic review and meta-analysis on post-diagnosis adiposity measures (body mass index [BMI], waist circumference, waist-to-hip ratio, weight) or their changes and colorectal cancer outcomes. PubMed and Embase were searched through 28 February 2022. Random-effects meta-analyses were conducted when at least three studies had sufficient information. The quality of evidence was interpreted and graded by the Global Cancer Update Programme (CUP Global) independent Expert Committee on Cancer Survivorship and Expert Panel. We reviewed 124 observational studies (85 publications). Meta-analyses were possible for BMI and all-cause mortality, colorectal cancer-specific mortality, and cancer recurrence/disease-free survival. Non-linear meta-analysis indicated a reverse J-shaped association between BMI and colorectal cancer outcomes (nadir at BMI 28 kg/m2). The highest risk, relative to the nadir, was observed at both ends of the BMI distribution (18 and 38 kg/m2), namely 60% and 23% higher risk for all-cause mortality; 95% and 26% for colorectal cancer-specific mortality; and 37% and 24% for cancer recurrence/disease-free survival, respectively. The higher risk with low BMI was attenuated in secondary analyses of RCTs (compared to cohort studies), among studies with longer follow-up, and in women suggesting potential methodological limitations and/or altered physiological state. Descriptively synthesised studies on other adiposity-outcome associations of interest were limited in number and methodological quality. All the associations were graded as limited (likelihood of causality: no conclusion) due to potential methodological limitations (reverse causation, confounding, selection bias). Additional well-designed observational studies and interventional trials are needed to provide further clarification.

Preoperative albumin-bilirubin score predicts short-term outcomes and long-term prognosis in colorectal cancer patients undergoing radical surgery

BACKGROUND

The albumin-bilirubin (ALBI) score is a serum biochemical indicator of liver function and has been proven to have prognostic value in a variety of cancers. In colorectal cancer (CRC), a high ALBI score tends to be associated with poorer survival.

AIM

To investigate the correlation between the preoperative ALBI score and outcomes in CRC patients who underwent radical surgery.

METHODS

Patients who underwent radical CRC surgery between January 2011 and January 2020 at a single clinical center were included. The ALBI score was calculated by the formula (log10 bilirubin × 0.66) + (albumin × -0.085), and the cutoff value for grouping patients was -2.8. The short-term outcomes, overall survival (OS), and disease-free survival (DFS) were calculated.

RESULTS

A total of 4025 CRC patients who underwent radical surgery were enrolled in this study, and there were 1908 patients in the low ALBI group and 2117 patients in the high ALBI group. Cox regression analysis revealed that age, tumor size, tumor stage, ALBI score, and overall complications were independent risk factors for OS; age, tumor stage, ALBI score, and overall complications were identified as independent risk factors for DFS.

CONCLUSION

A high preoperative ALBI score is correlated with adverse short-term outcomes, and the ALBI score is an independent risk factor for OS and DFS in patients with CRC undergoing radical surgery.

Machine learning-based classifiers to predict metastasis in colorectal cancer patients

BACKGROUND

The increasing prevalence of colorectal cancer (CRC) in Iran over the past three decades has made it a key public health burden. This study aimed to predict metastasis in CRC patients using machine learning (ML) approaches in terms of demographic and clinical factors.

METHODS

This study focuses on 1,127 CRC patients who underwent appropriate treatments at Taleghani Hospital, a tertiary care facility. The patients were divided into training and test datasets in an 80:20 ratio. Various ML methods, including Naive Bayes (NB), random rorest (RF), support vector machine (SVM), neural network (NN), decision tree (DT), and logistic regression (LR), were used for predicting metastasis in CRC patients. Model performance was evaluated using 5-fold cross-validation, reporting sensitivity, specificity, the area under the curve (AUC), and other indexes.

RESULTS

Among the 1,127 patients, 183 (16%) had experienced metastasis. In the predictionof metastasis, both the NN and RF algorithms had the highest AUC, while SVM ranked third in both the original and balanced datasets. The NN and RF algorithms achieved the highest AUC (100%), sensitivity (100% and 100%, respectively), and accuracy (99.2% and 99.3%, respectively) on the balanced dataset, followed by the SVM with an AUC of 98.8%, a sensitivity of 97.5%, and an accuracy of 97%. Moreover, lower false negative rate (FNR), false positive rate (FPR), and higher negative predictive value (NPV) can be confirmed by these two methods. The results also showed that all methods exhibited good performance in the test datasets, and the balanced dataset improved the performance of most ML methods. The most important variables for predicting metastasis were the tumor stage, the number of involved lymph nodes, and the treatment type. In a separate analysis of patients with tumor stages I-III, it was identified that tumor grade, tumor size, and tumor stage are the most important features.

CONCLUSION

This study indicated that NN and RF were the best among ML-based approaches for predicting metastasis in CRC patients. Both the tumor stage and the number of involved lymph nodes were considered the most important features.

Development and validation of prognostic nomogram for patients with metastatic gastric adenocarcinoma based on the SEER database

The aim of this study was to investigate the prognostic factors affecting overall survival in patients with metastatic gastric adenocarcinoma and to establish a nomogram prediction model for comprehensive clinical application. Data from 2370 patients with metastatic gastric adenocarcinoma between 2010 and 2017 were retrieved from the surveillance, epidemiology, and end results database. They were randomly divided into a training set (70%) and a validation set (30%), univariate and multivariate Cox proportional hazards regressions were used to screen important variables that may affect overall survival and to establish the nomogram. The nomogram model was evaluated using a receiver operating characteristic curve, calibration plot, and decision curve analysis. Internal validation was performed to test the accuracy and validity of the nomogram. Univariate and multivariate Cox regression analyses revealed that, age, primary site, grade, and American joint committee on cancer. T, bone metastasis, liver metastasis, lung metastasis, tumor Size, and chemotherapy were identified as independent prognostic factors for overall survival and were included in the prognostic model to construct a nomogram. The prognostic nomogram showed good overall survival risk stratification ability for the area under the curve, calibration plots, and decision curve analysis in both the training and validation sets. Kaplan-Meier curves further showed that patients in the low-risk group had better overall survival. This study synthesizes the clinical, pathological, therapeutic characteristics of patients with metastatic gastric adenocarcinoma, establishes a clinically effective prognostic model, and that can help clinicians to better evaluate the patient's condition and provide accurate treatment.

Computed Tomography Images under the Nomogram Mathematical Prediction Model in the Treatment of Cerebral Infarction Complicated with Nonvalvular Atrial Fibrillation and the Impacts of Virus Infection

The aim of this work was to explore the effect of the nomogram mathematical model on the treatment of cerebral infarction complicated with nonvalvular atrial fibrillation (NVAF) and viral infection. The data were scanned by a circular trajectory fan beam isometric scanning mode system (scanning system), and the speckle noise of computed tomography (CT) images was smoothed by Lee filtering. 52 patients with postoperative recurrent viral infection (RVI group) and 248 patients without postoperative recurrent viral infection (NRVI group) were selected for retrospective analysis. The mathematical model curve was then analyzed through calibration plots and decision curves to predict the accuracy of the mathematical model. The results showed that the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy based on the training set were 0.7868, 0.7634, 0.6982, and 0.7146, respectively. The AUC, sensitivity, specificity, and accuracy based on the validation set were 0.7623, 0.7734, 0.6882, and 0.6948, respectively. There was no significant difference in the AUC between the two groups (P > 0.05), indicating that the nomogram mathematical prediction model had high repeatability. In conclusion, CT images based on the nomogram mathematical prediction model had good predictive ability in the treatment of cerebral infarction complicated with NVAF.

Development of web-based dynamic nomogram to predict survival in patients with gastric cancer: a population-based study

Gastric cancer (GC) is the fifth most frequent malignancy worldwide and the third leading cause of cancer-associated mortality. The study's goal was to construct a predictive model and nomograms to predict the survival of GC patients. This historical cohort study assessed 733 patients who underwent treatments for GC. The univariate and multivariable Cox proportional hazard (CPH) survival analyses were applied to identify the factors related to overall survival (OS). A dynamic nomogram was developed as a graphical representation of the CPH regression model. The internal validation of the nomogram was evaluated by Harrell's concordance index (C-index) and time-dependent AUC. The results of the multivariable Cox model revealed that the age of patients, body mass index (BMI), grade of tumor, and depth of tumor elevate the mortality hazard of gastric cancer patients (P < 0.05). The built nomogram had a discriminatory performance, with a C-index of 0.64 (CI 0.61, 0.67). We constructed and validated an original predictive nomogram for OS in patients with GC. Furthermore, nomograms may help predict the individual risk of OS in patients treated for GC.

Predicting metachronous liver metastasis in patients with colorectal cancer: development and assessment of a new nomogram

BACKGROUND

We aimed to develop and validate a nomogram model, which could predict metachronous liver metastasis in colorectal cancer within two years after diagnosis.

METHODS

A retrospective study was performed on colorectal cancer patients who were admitted to Beijing Shijitan Hospital from January 1, 2016 to June 30, 2019. The least absolute shrinkage and selection operator (LASSO) regression model was used to optimize feature selection for susceptibility to metachronous liver metastasis in colorectal cancer. Multivariable logistic regression analysis was applied to establish a predictive model through incorporating features selected in the LASSO regression model. C-index, receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA) were employed to assess discrimination, distinctiveness, consistency with actual occurrence risk, and clinical utility of candidate predictive model. Internal validation was assessed with bootstrapping method.

RESULTS

Predictors contained in candidate prediction nomogram included age, CEA, vascular invasion, T stage, N stage, family history of cancer, and KRAS mutation. This model displayed good discrimination with a C-index of 0.787 (95% confidence interval: 0.728-0.846) and good calibration, whereas area under the ROC curve (AUC) of 0.786. Internal validation obtained C-index of 0.786, and AUC of validation cohort is 0.784. Based on DCA, with threshold probability range from 1 to 60%; this predictive model might identify colorectal cancer metachronous liver metastasis to achieve a net clinical benefit.

CONCLUSION

We have developed and validated a prognostic nomogram with good discriminative and high accuracy to predict metachronous liver metastasis in CRC patients.

The High Ratio of the Plasma miR-96/miR-99b Correlated With Poor Prognosis in Patients With Metastatic Colorectal Cancer

Object: This study aims to clarify the expression of plasma miRNA in CRC patients, and to clarify the potential use of these miRNAs in diagnosis and prognosis, and to establish a prognostic model to initially explore its clinical value.

Methods: We detected the expression of 6 miRNAs in normal colon epithelial cell lines and colorectal cancer cell lines by qRT-PCR and they were validated in the tissues of three subtypes: 20 healthy subjects, 41 pCRC and 49 mCRC patients. COX regression and ROC analyses use to evaluate the diagnostic and prognostic efficacy of candidate miRNAs. Subsequently, we initially established a nomogram prognostic model. MiRNA is also used to construct miRNA-mRNA interaction network and PPI network modules.

Results: Five miRNAs showed significant differential expression in pCRC, mCRC patients and normal groups. ROC analysis showed that CEA, miR-96, miR-99b and miR-96/miR-99b are distinguishable from pCRC and mCRC patients, with AUC ranging from 0.65 to 0.91; among them, the ratio of miR-96/miR-99b is stronger than any diagnostic indicators, such as CEA and CA125. Multivariate survival analysis identified miR-96, miR-99b, N stage, M stage and clinical stage as independent prognostic indicators of mCRC. The nomogram based on these 5 characteristics has satisfactory prognostic values.

Conclusion: Our data indicate that plasma miR-96/miR-99b can be used as a promising biomarker for early detection of mCRC patients; our nomogram has a promising evaluation value.