Multimorbidity patterns and the risk of falls among older adults: a community-based study in China.

BACKGROUND: Due to the high prevalence of multimorbidity and realistic health service demands for fall prevention, there is growing interest in the association between multimorbidity and falls. Our study aimed to identify multimorbidity patterns among Chinese older adults and explore the association between multimorbidity patterns and falls. METHODS: Data from 4,579 Chinese community-dwelling older adults was included in this analysis. Information regarding falls and 10 chronic conditions was collected. An exploratory factor analysis was performed to determine multimorbidity patterns. Regression models were fitted to explore the associations of individual chronic disease or multimorbidity patterns with falls. RESULTS: Among 4,579 participants, 368 (8.0%) were defined as fallers, including 92 (2.0%) frequent fallers, and multimorbidity affected 2,503 (54.7%) participants. Older adults with multimorbidity were more likely to be fallers [odds ratio (OR) = 1.3, P = 0.02] and frequent fallers (OR = 1.7, P = 0.04). Three multimorbidity patterns were identified (i.e., cardiovascular-metabolic diseases, psycho-cognitive diseases and organic diseases), and the associations between psycho-cognitive diseases/organic diseases and prevalent falls or frequent falls were found to be significant. CONCLUSIONS: The psycho-cognitive disease pattern and organic disease pattern are significantly associated with falls. Therefore, more attention should be paid to patients with psycho-cognitive diseases and timely, targeted diagnostic and treatment services should be provided in fall prevention.

Development of Cost-Effective Fatty Liver Disease Prediction Models in a Chinese Population: Statistical and Machine Learning Approaches.

BACKGROUND: The increasing prevalence of nonalcoholic fatty liver disease (NAFLD) in China presents a significant public health concern. Traditional ultrasound, commonly used for fatty liver screening, often lacks the ability to accurately quantify steatosis, leading to insufficient follow-up for patients with moderate-to-severe steatosis. Transient elastography (TE) provides a more quantitative diagnosis of steatosis and fibrosis, closely aligning with biopsy results. Moreover, machine learning (ML) technology holds promise for developing more precise diagnostic models for NAFLD using a variety of laboratory indicators. OBJECTIVE: This study aims to develop a novel ML-based diagnostic model leveraging TE results for staging hepatic steatosis. The objective was to streamline the model's input features, creating a cost-effective and user-friendly tool to distinguish patients with NAFLD requiring follow-up. This innovative approach merges TE and ML to enhance diagnostic accuracy and efficiency in NAFLD assessment. METHODS: The study involved a comprehensive analysis of health examination records from Suzhou Municipal Hospital, spanning from March to May 2023. Patient data and questionnaire responses were meticulously inputted into Microsoft Excel 2019, followed by thorough data cleaning and model development using Python 3.7, with libraries scikit-learn and numpy to ensure data accuracy. A cohort comprising 978 residents with complete medical records and TE results was included for analysis. Various classification models, including logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), were constructed and evaluated based on the area under the receiver operating characteristic curve (AUROC). RESULTS: Among the 916 patients included in the study, 273 were diagnosed with moderate-to-severe NAFLD. The concordance rate between traditional ultrasound and TE for detecting moderate-to-severe NAFLD was 84.6% (231/273). The AUROC values for the RF, LightGBM, XGBoost, SVM, KNN, and LR models were 0.91, 0.86, 0.83, 0.88, 0.77, and 0.81, respectively. These models achieved accuracy rates of 84%, 81%, 78%, 81%, 76%, and 77%, respectively. Notably, the RF model exhibited the best performance. A simplified RF model was developed with an AUROC of 0.88, featuring 62% sensitivity and 90% specificity. This simplified model used 6 key features: waist circumference, BMI, fasting plasma glucose, uric acid, total bilirubin, and high-sensitivity C-reactive protein. This approach offers a cost-effective and user-friendly tool while streamlining feature acquisition for training purposes. CONCLUSIONS: The study introduces a groundbreaking, cost-effective ML algorithm that leverages health examination data for identifying moderate-to-severe NAFLD. This model has the potential to significantly impact public health by enabling targeted investigations and interventions for NAFLD. By integrating TE and ML technologies, the study showcases innovative approaches to advancing NAFLD diagnostics.

Synergistic effects on oxidative stress, apoptosis and necrosis resulting from combined toxicity of three commonly used pesticides on HepG2 cells.

The widespread use of pesticides performs a vital role in safeguarding crop yields and quality, providing the opportunity for multiple pesticides to co-exist, which poses a significant potential risk to human health. To assess the toxic effects caused by exposures to individual pesticides (chlorpyrifos, carbofuran and acetamiprid), binary combinations and ternary combinations, individual and combined exposure models were developed using HepG2 cells and the types of combined effects of pesticide mixtures were assessed using concentration addition (CA), independent action (IA) and combination index (CI) models, respectively, and the expression of biomarkers related to oxidative stress, apoptosis and cell necrosis was further examined. Our results showed that both individual pesticides and mixtures exerted toxic effects on HepG2 cells. The CI model indicated that the toxic effects of pesticide mixtures exhibited synergistic effects. The results of the lactate dehydrogenase (LDH) release and apoptosis assay revealed that the pesticide mixture increased the release of LDH and apoptosis levels. Moreover, our results also showed that individual pesticides and mixtures disrupted redox homeostasis and that pesticide mixtures produced more intense oxidative stress effects. In conclusion, we have illustrated the enhanced combined toxicity of pesticide mixtures by in-vitro experiments, which provides a theoretical basis and scientific basis for further toxicological studies.

Construction of ceRNA networks with different types of IDH1 mutation status in low-grade glioma patients.

Background: Isocitrate dehydrogenase 1 (IDH1) mutation status is related to the prognosis and immune microenvironment of glioma. Long non-coding ribonucleic acids (lncRNAs) interact with microRNAs (miRNAs), and play roles in the competitive endogenous RNA (ceRNA) network and tumor progression. Methods: Data on low-grade glioma (LGG) IDH1 mutation was acquired from The Cancer Genome Atlas (TCGA). An empirical analysis of differential gene expression was conducted to identify differentially expressed mRNAs (DEmRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed lncRNAs (DElncRNAs). Survival-associated genes were identified by a univariate Cox regression analysis. An enrichment analysis was conducted to explore the gene ontology and pathways of the DEmRNAs. Results: Eighty-eight DEIDH1mRNAs, 88 DEIDH1lncRNAs, and 6 DEIDH1miRNAs were identified to construct a ceRNA network of LGG patients. Validated by Chinese Glioma Genome Atlas and our LGG patients of gene expression and survival, and the colorectal neoplasia differentially expressed (CRNDE), HOXA transcript antisense RNA, myeloid-specific 1 (HOTAIRM1)/miRNA-206a/hepatocyte nuclear factor 4 (HNF4G) axis was determined. Conclusions: We established a ceRNA network by integrating the different IDH1 mutation statuses of LGG patients, and identified HNF4G, CRNDE, and HOTAIRM1 as genes related to the prognosis of and immune infiltration in LGG patients. Our findings suggest that these genes may be targets for LGG treatment, especially for patients with the wild-type IDH1 gene variants.

Prognostic Value of Combined Lactate Dehydrogenase, C-Reactive Protein, Cancer Antigen 153 and Cancer Antigen 125 in Metastatic Breast Cancer.

BACKGROUND: Breast cancer (BC), especially metastatic BC, is one of the most lethal diseases in women. CA 125 and CA 15-3 are commonly used indicators for diagnosis and prognosis of BC. Some serological indicators, such as lactate dehydrogenase (LDH) and C-reactive protein (CRP), can also be used to assess the prognosis and progression in BC. METHODS: Univariate Cox regression analysis and LASSO regression analysis were performed to identify prognostic factors and build prognostic models. We distributed the patients into 2 groups based on the median risk score, analyzed prognosis by Kaplan-Meier curve, and screened independent prognostic factors by multivariate Cox regression analysis. RESULT: We identified 4 indicators-LDH, CRP, CA 15-3, and CA 125-related to the prognosis in BC and established a prognostic model. The high LDH group showed worse overall survival (OS) than low LDH group (P = .017; hazard ratio (HR), 1.528; 95% confidence interval (CI), 1.055-2.215). The high CRP group showed worse OS than low CRP group (P = .004; HR, 1.666; 95% CI, 1.143-2.429). The high CA153 group showed worse OS than low CA 15-3 group (P=.011; HR, 1.563; 95% CI, 1.075-2.274). The high CA 125 group showed worse OS than low CA 125 group (P = .021; HR, 1.499; 95% CI, 1.031-2.181). The area under the curve for risk score was .824, Ki-67 was .628, age was .511, and grade was .545. Risk score was found to be an independent prognostic factor using multivariate Cox regression analysis. CONCLUSION: We successfully established an optimization model by combining 4 prognosis-related indicators to assess the prognosis in patients with metastatic BC.

The Depletion of Carbohydrate Metabolic Genes in the Gut Microbiome Contributes to the Transition From Central Obesity to Type 2 Diabetes.

Obesity, especially central obesity, is a strong risk factor for developing type 2 diabetes (T2D). However, the mechanism underlying the progression from central obesity to T2D remains unknown. Therefore, we analyzed the gut microbial profiles of central obese individuals with or without T2D from a Chinese population. Here we reported both the microbial compositional and gene functional alterations during the progression from central obesity to T2D. Several opportunistic pathogens were enriched in obese T2D patients. We also characterized thousands of genes involved in sugar and amino acid metabolism whose abundance were significantly depleted in obese T2D group. Moreover, the abundance of those genes was negatively associated with plasma glycemia level and percentage of individuals with impaired plasma glucose status. Therefore, our study indicates that the abundance of those depleted genes can be used as a potential biomarker to identify central obese individuals with high risks of developing T2D.

Identification of immune-related genes as prognostic factors in bladder cancer.

Bladder cancer is one of the most common cancers worldwide. The immune response and immune cell infiltration play crucial roles in tumour progression. Immunotherapy has delivered breakthrough achievements in the past decade in bladder cancer. Differentially expressed genes and immune-related genes (DEIRGs) were identified by using the edgeR package. Gene ontology annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for functional enrichment analysis of DEIRGs. Survival-associated IRGs were identified by univariate Cox regression analysis. A prognostic model was established by univariate COX regression analysis, and verified by a validation prognostic model based on the GEO database. Patients were divided into high-risk and low-risk groups based on the median risk score value for immune cell infiltration and clinicopathological analyses. A regulatory network of survival-associated IRGs and potential transcription factors was constructed to investigate the potential regulatory mechanisms of survival-associated IRGs. Nomogram and ROC curve to verify the accuracy of the model. Quantitative real-time PCR was performed to validate the expression of relevant key genes in the prognostic model. A total of 259 differentially expressed IRGs were identified in the present study. KEGG pathway analysis of IRGs showed that the "cytokine-cytokine receptor interaction" pathway was the most significantly enriched pathway. Thirteen survival-associated IRGs were selected to establish a prognostic index for bladder cancer. In both TCGA prognostic model and GEO validation model, patients with high riskscore had worse prognosis compared to low riskscore group. A high infiltration level of macrophages was observed in high-risk patients. OGN, ELN, ANXA6, ILK and TGFB3 were identified as hub survival-associated IRGs in the network. EBF1, WWTR1, GATA6, MYH11, and MEF2C were involved in the transcriptional regulation of these survival-associated hub IRGs. The present study identified several survival-associated IRGs of clinical significance and established a prognostic index for bladder cancer outcome evaluation for the first time.

Assessing the prognostic value of stemness-related genes in breast cancer patients.

Breast cancer (BC) is currently one of the deadliest tumors worldwide. Cancer stem cells (CSCs) are a small group of tumor cells with self-renewal and differentiation abilities and high treatment resistance. One of the reasons for treatment failures is the inability to completely eliminate tumor stem cells. By using the edgeR package, we identified stemness-related differentially expressed genes in GSE69280. Via Lasso-penalized Cox regression analysis and univariate Cox regression analysis, survival genes were screened out to construct a prognostic model. Via nomograms and ROC curves, we verified the accuracy of the prognostic model. We selected 4 genes (PSMB9, CXCL13, NPR3, and CDKN2C) to establish a prognostic model from TCGA data and a validation model from GSE24450 data. We found that the low-risk score group had better OS than the high-risk score group, whether using TCGA or GSE24450 data. A prognostic model including four stemness-related genes was constructed in our study to determine targets of breast cancer stem cells (BCSCs) and improve the treatment effect.

Prognostic value of immune related genes in lung adenocarcinoma.

Lung cancer has the highest incidence and mortality rates of all cancers in China. Immune-related genes and immune infiltrating lymphocytes are involved in tumor growth, and in the past decade, immunotherapy has become increasingly important in the treatment of lung cancer. Using the edgeR package, differentially expressed genes and immune-related genes (DEIRGs) were identified in patients with lung adenocarcinoma (LUAD). Functional enrichment analysis of DEIRGs was performed using Gene Ontology annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Survival-associated immune-related genes (IRGs) were selected using univariate Cox regression analysis and the prognostic model was assessed using multivariate Cox regression analysis. Overall, 273 DEIRGs were identified in LUAD, and KEGG pathway analysis of IRGs showed that 'cytokine-cytokine receptor interaction' was the most significantly enriched pathway. Furthermore, six survival associated IRGs were screened to establish a prognostic model; patients in the high risk score group had less favorable survival times, and the prognostic model was negatively associated with B cell infiltration. The present study established a prognostic model using analysis of survival-related immune-related genes, which were associated with B cell infiltration.

Comprehensive analysis of a long non-coding RNA-associated competing endogenous RNA network in glioma.

Long non-coding RNAs (lncRNAs) can act as competing endogenous RNAs (ceRNAs), interacting with microRNAs (miRNAs) and playing an important role in tumor progression. However, the role of lncRNA-mediated ceRNAs in glioma remains largely unknown. The present study aimed to identify novel lncRNAs and their associated function in glioma. RNA sequencing and corresponding clinical data from patients with glioma were obtained from The Cancer Genome Atlas. A total of 598 glioma tissues and 5 normal brain tissues were analyzed in the present study. The differentially expressed (DE) lncRNAs, mRNAs and miRNAs were identified using R packages and were used to construct a ceRNA network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to investigate the biological functions of the DEmRNAs. Kaplan-Meier curve analysis was performed to investigate the association between DElncRNA expression and patient outcome. A total of 752 DElncRNAs, 2,079 DEmRNAs and 113 DEmiRNAs were identified between glioma and normal tissues. A lncRNA-miRNA-mRNA ceRNA network consisting of 61 lncRNAs, 12 miRNAs and 92 mRNAs was constructed. Survival analysis indicated that 36 DElncRNAs, 72 DEmRNAs and 3 DEmiRNAs were associated with overall survival in patients with glioma. The present study identified novel lncRNAs associated with survival prognosis and may facilitate further investigation of lncRNA-mediated ceRNA regulatory mechanisms in glioma.

Combined PLT and NE to predict the prognosis of patients with locally advanced cervical cancer.

Cervical cancer is one of the most common tumors in women. Neutrophils (NEs) and platelets (PLTs) are components of cells in circulating blood. NEs are one of the components of white blood cells (WBCs), accounting for the vast majority of WBCs, recognized as one of the indicators of inflammation. PLTs are associated with thrombosis and inflammation. Both of them play an important role in tumor growth and metastasis. According to pre-radiotherapy PLT and NE media levels, we divided the patients into three groups: PLT and NE both high levels group, single high level group and both low group. By using COX regression models and nomogram, a prognostic model for patients was established. Both high levels of pre-radiotherapy PLT and NE group or high levels of post-radiotherapy PLT and NE group were correlated with worst overall survival (OS) compared with the other two groups. PLT and NE were correlated with outcomes of the patients with locally advanced cervical cancer.

Establishment of a Prognostic Model Using Immune-Related Genes in Patients With Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most prevalent neoplasms worldwide, particularly in China. Immune-related genes (IRGs) and immune infiltrating lymphocytes play specific roles in tumor growth. Considering how important immunotherapy has become for HCC treatment in the past decade, our objective was to establish a prognostic model by screening survival-related IRGs in patients with HCC. Using edgeR, we identified differentially expressed IRGs (DEIRGs), DEmiRNAs, and DElncRNAs. Functional enrichment analysis of DEIRGs was performed to investigate the biological functions of IRGs via gene ontology annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Protein-protein interaction and competing endogenous RNA networks were established using Cytoscape. Survival-associated IRGs were selected via univariate COX regression analysis, a The Cancer Genome Atlas (TCGA) prognostic model and GSE76427 validation model were developed using multivariate COX regression analysis test by AIC (Akaike Information Criterion). We identified 116 DEIRGs in patients with HCC; the "cytokine-cytokine receptor interaction" pathway was found to be the most enriched pathway. Via the prognostic model helped us classify patients into high- and low-risk score groups based on overall survival (OS); high risk score was associated with worse OS, and a positive correlation was observed between the prognostic model and immune cell infiltration. To summarize, we established a prognostic model using survival-related IRGs that provides sufficient information for prognosis prediction and immunotherapy of patients with HCC.

A nomogram for predicting overall survival in patients with uterine leiomyosarcoma: a SEER population-based study.

Aim: To establish and validate a nomogram for the estimation of overall survival of patients with uterine leiomyosarcoma (uLMS). Methods: Information on patients diagnosed as uLMS was retrospectively retrieved from the Surveillance, Epidemiology, and End Results database. The patients were randomly assigned into the training and the validation cohorts. Univariate and multivariate analyses were used to determine the independent prognostic factors for building a nomogram for predicting overall survival. The predictive accuracy was evaluated based on the concordance indices and the calibration plots. Results: A nomogram that combined age, marital status, tumor size, Surveillance, Epidemiology, and End Result stage, surgery and radiation was established. The internal and external concordance indices were 0.748 and 0.745, respectively. The calibration plots approached 45 degrees. Conclusion: The nomogram might be an effective tool for predicting the survival of patients with uLMS.

Comprehensive analysis of the lncRNA­associated competing endogenous RNA network in breast cancer.

Long noncoding RNAs (lncRNAs) have been confirmed to be potential prognostic markers in a variety of cancers and to interact with microRNAs (miRNAs) as competing endogenous RNAs (ceRNAs) to regulate target gene expression. However, the role of lncRNA­mediated ceRNAs in breast cancer (BC) remains unclear. In the present study, a ceRNA network was generated to explore their role in BC. The expression profiles of mRNAs, miRNAs and lncRNAs in 1,109 BC tissues and 113 normal breast tissues were obtained from The Cancer Genome Atlas database (TCGA). A total of 3,198 differentially expressed (DE) mRNAs, 150 differentially DEmiRNAs and 1,043 DElncRNAs were identified between BC and normal tissues. A lncRNA­miRNA­mRNA network associated with BC was successfully constructed based on the combined data obtained from RNA databases, and comprised 97 lncRNA nodes, 24 miRNA nodes and 74 mRNA nodes. The biological functions of the 74 DEmRNAs were further investigated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The results demonstrated that the DEmRNAs were significantly enriched in two GO biological process categories; the main biological process enriched term was 'positive regulation of GTPase activity'. By KEGG analysis, four key enriched pathways were obtained, including the 'MAPK signaling pathway', the 'Ras signaling pathway', 'prostate cancer', and the 'FoxO signaling pathway'. Kaplan­Meier survival analysis revealed that six DElncRNAs (INC AC112721.1, LINC00536, MIR7­3HG, ADAMTS9­AS1, AL356479.1 and LINC00466), nine DEmRNAs (KPNA2, RACGAP1, SHCBP1, ZNF367, NTRK2, ORS1, PTGS2, RASGRP1 and SFRP1) and two DEmiRNAs (hsa­miR­301b and hsa­miR­204) had significant effects on overall survival in BC. The present results demonstrated the aberrant expression of INC AC112721.1, AL356479.1, LINC00466 and MIR7­3HG in BC, indicating their potential prognostic role in patients with BC.

Prognostic values of lymphocyte and eosinophil counts in resectable cervical squamous cell carcinoma.

Aim: Cervical cancer is one of the leading causes of cancer mortality in women. Peripheral white blood cell parameters such as neutrophil (NE), eosinophil (EO), basophil (BA), as well as lymphocyte (LY) and monocyte (MO), are correlated with tumor outcomes. Methods: In total, 110 cervical squamous cell carcinoma patients were recruited in this study. The potential prognostic factors were evaluated by univariate and multivariate survival analysis. Results: Cox regression analysis model indicated that higher pretreatment EO level and increased post-/preradiotherapy EO ratio were independently associated with worse progression-free survival. Lower pretreatment LY or higher EO levels and increased post-/preradiotherapy EO ratio were independently associated with worse overall survival. Conclusion: LY and EO are correlated with outcomes of cervical squamous cell cancer.

Prognostic Values of Platelet-Associated Indicators in Resectable Cervical Cancer.

BACKGROUND: Cervical cancer is one of the leading causes of cancer mortality in women, which seriously threatens the health of women worldwide. Platelet (PLT)-related parameters, including PLT count, mean platelet volume (MPV), plateletcrit (PCT), and platelet distribution width (PDW), are correlated with tumor prognosis. METHODS: In total, 110 patients with cervical carcinoma were recruited in this study. The patients were divided into 2 groups according to the receiver operating characteristic analysis cutoff values of PLT, MPV, PCT, or PDW. The post-/preradiotherapy ratios were defined as the rate of preradiotherapy PLT-related parameters counts and the corresponding ones obtained after radiotherapy. RESULTS: Higher pretreatment PLT level was correlated with Higher Federation of Gynecology and Obstetrics (FIGO) stage (II). Higher pretreatment PLT level was correlated with worse progression-free survival (PFS) and overall survival (OS). Increased post-/preradiotherapy ratio of PLT was correlated with worse PFS and OS. Changes in PCT, MPV, or PDW levels had no effects on PFS or OS. Cox regression analysis model indicated that larger tumor size, higher pretreatment PLT level, and increased post-/preradiotherapy PLT ratio were independently associated with worse PFS; higher FIGO stage (II) and increased post-/preradiotherapy PLT ratio were independently associated with worse OS. CONCLUSION: Pretreatment PLT level and increased post-/preradiotherapy PLT ratio are correlated with outcomes of cervical cancer.

Prognostic Values of Systemic Inflammation Response (SIR) Parameters in Resectable Cervical Cancer.

BACKGROUND: Cervical carcinoma is the leading cause of cancer mortality in women. C-reactive protein (CRP), albumin (ALB), globulin (GLB), lactate dehydrogenase (LDH), and albumin-to-globulin ratio (AGR) are indicators of systemic inflammation response correlated with tumor outcomes. METHODS: This study recruited 110 patients with cervical cancer. The patients were divided into 2 groups according to pretreatment median values of CRP, ALB, GLB, LDH, and AGR. The post/preradiotherapy or post/pretreatment ratios were defined as rates of pretreatment CRP, ALB, GLB, LDH, and AGR values and the corresponding ones obtained after radiotherapy or whole treatment. RESULTS: Higher pretreatment CRP or LDH levels were correlated with worse progression-free survival (PFS) and overall survival (OS). Increased post/preradiotherapy CRP ratio was correlated with worse PFS and OS, increased post/preradiotherapy LDH ratio was correlated with worse PFS. Increased post/pretreatment CRP ratio was correlated with worse PFS and OS, not-increased post/pretreatment AGR ratio was correlated with worse OS. Cox regression analysis model indicated that, moderately or poorly of differentiation, higher pretreatment CRP or LDH levels were independently associated with worse PFS, higher pretreatment CRP or LDH levels and increased post/pretreatment CRP ratio were independently associated with worse OS. CONCLUSION: CRP, LDH, or AGR are correlated with outcomes of resectable cervical cancer.

Nomograms for estimating survival in patients with liver-only colorectal metastases: A retrospective study.

BACKGROUND: The aim of this study was to develop and validate nomograms for individual risk prediction in patients with liver-only colorectal metastases (CRLM). METHODS: Histologically confirmed CRLM diagnosed between 2010 and 2015 were analysed from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate analyses were used to obtain independent prognostic factors to build nomograms for predicting 1- and 3-year overall survival (OS) and cancer-specific survival (CSS). The predictive accuracy of the nomogram was determined by concordance index (C-index) and calibration plots. RESULTS: A total of 9615 patients with CRLM were included in the study. A nomogram predicting OS was constructed according to 9 independent clinicopathological factors. A nomogram predicting CSS was constructed based on the same 9 factors. The C-indexes of the nomograms were significantly better than the TNM staging system (7th edition) in both sets for predicting both OS and CSS. The calibration plots displayed an optimal agreement between the predictive results and the actual observed outcomes. CONCLUSIONS: The proposed nomograms can help clinicians calculate the probability in patients with CRLM.