A predictive model for colorectal cancer complicated with intestinal obstruction based on specific inflammation score.

PURPOSE: Inflammatory factors play an important role in the onset and progression of colorectal cancer (CRC). This study aimed to develop and validate a novel scoring system that utilizes specific inflammatory factor indicators to predict intestinal obstruction in CRC patients. METHODS: This study conducted a retrospective analysis of 1,470 CRC patients who underwent surgical resection between January 2013 and July 2018. These patients were randomly allocated to the training group (n = 1060) and the validation group (n = 410). Univariate and multivariate logistic regression analyses were performed to identify independent predictive factors for intestinal obstruction. The CRC peculiar inflammation score (CPIS), comprising lymphocyte-to-monocyte ratio (LMR), prognostic nutrition index (PNI), and alanine transaminase-to-lymphocyte ratio index (ALRI) scores, was significantly associated with the occurrence of intestinal obstruction. A nomogram combining CPIS with other clinical features was developed to predict this occurrence. Model accuracy was assessed by determining the area under the receiver operating characteristic (ROC) curve (AUC). RESULTS: The CPIS generated by multi-factor logistic regression was as follows: - 1.576 × LMR - 0.067 × PNI + 0.018 × ALRI. Using CPIS cutoff values of 50% (- 7.188) and 85% (- 6.144), three predictive groups were established. Patients with a high CPIS had a significantly higher risk of intestinal obstruction than those with a low CPIS (odds ratio [OR]: 10.0, confidence interval [CI]: 5.85-17.08, P < 0.001). The predictive nomogram demonstrated good calibration and discrimination abilities. The AUC of the ROC curve for the obstruction nomogram was 0.813 (95% CI: 0.777-0.850) in the training set and 0.806 (95% CI: 0.752-0.860) in the validation set. The calibration curve exhibited neither bias nor high credibility. Decision curve analysis indicated the utility of this predictive model. CONCLUSION: CRC-associated intestinal obstruction is closely linked to inflammatory markers in patients. CPIS is a CRC-specific inflammatory predictive score based on a combination of inflammatory-related indicators. A high CPIS serves as a strong indicator of intestinal obstruction. Its integration with other clinical factors and preoperative inflammatory-specific indicators significantly enhances the diagnosis and treatment of CRC patients with intestinal obstruction.

Developments and Trends of Nanotechnology Application in Sepsis: A Comprehensive Review Based on Knowledge Visualization Analysis.

Sepsis, a common life-threatening clinical condition, continues to have high morbidity and mortality rates, despite advancements in management. In response, significant research efforts have been directed toward developing effective strategies. Within this scope, nanotechnology has emerged as a particularly promising field, attracting significant interest for its potential to enhance disease diagnosis and treatment. While several reviews have highlighted the use of nanoparticles in sepsis, comprehensive studies that summarize and analyze the hotspots and research trends are lacking. To identify and further promote the development of nanotechnology in sepsis, a bibliometric analysis was conducted on the relevant literature, assessing research trends and hotspots in the application of nanomaterials for sepsis. Next, a comprehensive review of the subjectively recognized research hotspots in sepsis, including nanotechnology-enhanced biosensors and nanoscale imaging for sepsis diagnostics, and nanoplatforms designed for antimicrobial, immunomodulatory, and detoxification strategies in sepsis therapy, is elucidated, while the potential side effects and toxicity risks of these nanomaterials were discussed. Particular attention is given to biomimetic nanoparticles, which mimic the biological functions of source cells like erythrocytes, immune cells, and platelets to evade immune responses and effectively deliver therapeutic agents, demonstrating substantial translational potential. Finally, current challenges and future perspectives of nanotechnology applications in sepsis with a view to maximizing their great potential in the research of translational medicine are also discussed.

Influence of skeletal muscle and intermuscular fat on postoperative complications and long-term survival in rectal cancer patients.

BACKGROUND: The body composition of patients with rectal cancer potentially affects postoperative outcomes. This study explored the correlations between skeletal muscle and adipose tissue quantified by computed tomography (CT) with postoperative complications and long-term prognosis in patients with rectal cancer after surgical resection. METHODS: This retrospective cohort study included patients with rectal cancer who underwent surgical resection at the Wuhan Union Hospital between 2014 and 2018. CT images within 3 months prior to the surgery were used to quantify the indices of skeletal muscle and adipose tissue at the levels of the third lumbar vertebra (L3) and umbilicus. Optimal cut-off values for each index were defined separately for males and females. Associations between body composition and postoperative complications, overall survival (OS), and disease-free survival (DFS) were evaluated using logistic and Cox proportional hazards models. RESULTS: We included 415 patients (240 males and 175 females; mean age: 57.8 ± 10.5 years). At the L3 level, a high skeletal muscle density (SMD; hazard ratio [HR]: 0.357, 95% confidence interval [CI]: 0.191-0.665, P = 0.001; HR: 0.571, 95% CI: 0.329-0.993, P = 0.047) and a high skeletal muscle index (SMI; HR: 0.435, 95% CI 0.254-0.747, P = 0.003; HR: 0.568, 95% CI: 0.359-0.897, P = 0.015) were independent prognostic factors for better OS and DFS. At the umbilical level, a large intermuscular fat area (IMFA; HR: 1.904, 95% CI: 1.068-3.395, P = 0.029; HR: 2.064, 95% CI: 1.299-3.280, P = 0.002) was an independent predictive factor for worse OS and DFS, and a high SMI (HR: 0.261, 95% CI: 0.132-0.517, P < 0.001; HR: 0.595, 95% CI: 0.387-0.913, P = 0.018) was an independent prognostic factor for better OS and DFS. The models combining body composition and clinical indicators had good predictive abilities for OS. The receiver operating characteristic areas under the curve were 0.848 and 0.860 at the L3 and umbilical levels, respectively (both P < 0.05). CONCLUSIONS: No correlations existed between CT-quantified body composition parameters and postoperative complications. However, a high SMD and high SMI were significantly associated with longer OS and DFS at the L3 level, whereas a large IMFA and low SMI were associated with worse OS and DFS at the umbilical level. Combining CT-quantified body composition and clinical indicators could help physicians predict the prognosis of patients with rectal cancer after surgery.

Development and Interpretation of a Clinicopathological-Based Model for the Identification of Microsatellite Instability in Colorectal Cancer.

Chemotherapy is not recommended for patients with deficient mismatch repair (dMMR) in colorectal cancer (CRC); therefore, assessing the status of MMR is crucial for the selection of subsequent treatment. This study is aimed at building predictive models to accurately and rapidly identify dMMR. A retrospective analysis was performed at Wuhan Union Hospital between May 2017 and December 2019 based on the clinicopathological data of patients with CRC. The variables were subjected to collinearity, least absolute shrinkage and selection operator (LASSO) regression, and random forest (RF) feature screening analyses. Four sets of machine learning models (extreme gradient boosting (XGBoost), support vector machine (SVM), naive Bayes (NB), and RF) and a conventional logistic regression (LR) model were built for model training and testing. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive performance of the developed models. In total, 2279 patients were included in the study and were randomly divided into either the training or test group. Twelve clinicopathological features were incorporated into the development of the predictive models. The area under curve (AUC) values of the five predictive models were 0.8055 for XGBoost, 0.8174 for SVM, 0.7424 for NB, 8584 for RF, and 0.7835 for LR (Delong test, P value < 0.05). The results showed that the RF model exhibited the best recognition ability and outperformed the conventional LR method in identifying dMMR and proficient MMR (pMMR). Our predictive models based on routine clinicopathological data can significantly improve the diagnostic performance of dMMR and pMMR. The four machine learning models outperformed the conventional LR model.

The Prognostic Significance of RIMKLB and Related Immune Infiltrates in Colorectal Cancers.

RimK-like family member B (RIMKLB) is an enzyme that post-translationally modulates ribosomal protein S6, which can affect the development of immune cells. Some studies have suggested its role in tumor progression. However, the relationships among RIMKLB expression, survival outcomes, and tumor-infiltrating immune cells (TIICs) in colorectal cancer (CRC) are still unknown. Therefore, we analyzed RIMKLB expression levels in CRC and normal tissues and investigated the correlations between RIMKLB and TIICs as well as the impact of RIMKLB expression on clinical prognosis in CRC using multiple databases, including the Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA), PrognoScan, and UALCAN databases. Enrichment analysis was conducted with the cluster Profiler package in R software to explore the RIMKLB-related biological processes involved in CRC. The RIMKLB expression was significantly decreased in CRC compared to normal tissues, and correlated with histology, stage, lymphatic metastasis, and tumor status (p < 0.05). Patients with CRC with high expression of RIMKLB showed poorer overall survival (OS) (HR = 2.5,p = 0.00,042), and inferior disease-free survival (DFS) (HR = 1.9,p = 0.19) than those with low expression of RIMKLB. TIMER analysis indicated that RIMKLB transcription was closely related with several TIICs, including CD4+ and CD8+ T cells, B cells, tumor-associated macrophages (TAMs), monocytes, neutrophils, natural killer cells, dendritic cells, and subsets of T cells. Moreover, the expression of RIMKLB showed significant positive correlations with infiltrating levels of PD1 (r = 0.223, p = 1.31e-06; r = 0.249, p = 1.25e-03), PDL1 (r = 0.223, p = 6.03e-07; r = 0.41, p = 5.45e-08), and CTLA4 (r = 0.325, p = 9.68e-13; r = 0.41, p = 5.45e-08) in colon and rectum cancer, respectively. Enrichment analysis showed that the RIMKLB expression was positively related to extracellular matrix and immune inflammation-related pathways. In conclusion, RIMKLB expression is associated with survival outcomes and TIICs levels in patients with CRC, and therefore, might be a potential novel prognostic biomarker that reflects the immune infiltration status.

Development and Validation of a Predictive Scoring System for Colorectal Cancer Patients With Liver Metastasis: A Population-Based Study.

Liver metastasis in colorectal cancer (CRC) is common and has an unfavorable prognosis. This study aimed to establish a functional nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with colorectal cancer liver metastasis (CRCLM). A total of 9,736 patients with CRCLM from 2010 to 2016 were randomly assigned to training, internal validation, and external validation cohorts. Univariate and multivariate Cox analyses were performed to identify independent clinicopathologic predictive factors, and a nomogram was constructed to predict CSS and OS. Multivariate analysis demonstrated age, tumor location, differentiation, gender, TNM stage, chemotherapy, number of sampled lymph nodes, number of positive lymph nodes, tumor size, and metastatic surgery as independent predictors for CRCLM. A nomogram incorporating the 10 predictors was constructed. The nomogram showed favorable sensitivity at predicting 1-, 3-, and 5-year OS, with area under the receiver operating characteristic curve (AUROC) values of 0.816, 0.782, and 0.787 in the training cohort; 0.827, 0.769, and 0.774 in the internal validation cohort; and 0.819, 0.745, and 0.767 in the external validation cohort, respectively. For CSS, the values were 0.825, 0.771, and 0.772 in the training cohort; 0.828, 0.753, and 0.758 in the internal validation cohort; and 0.828, 0.737, and 0.772 in the external validation cohort, respectively. Calibration curves and ROC curves revealed that using our models to predict the OS and CSS would add more benefit than other single methods. In summary, the novel nomogram based on significant clinicopathological characteristics can be conveniently used to facilitate the postoperative individualized prediction of OS and CSS in CRCLM patients.

The value of haematological parameters and serum tumour markers for predicting KRAS mutations in 784 Chinese colorectal cancer patients: a retrospective analysis.

BACKGROUND: Identifying the mutation status of KRAS is important for optimizing treatment in patients with colorectal cancer (CRC). The aim of this study was to investigate the predictive value of haematological parameters and serum tumour markers (STMs) for KRAS gene mutations. METHODS: The clinical data of patients with colorectal cancer from January 2014 to December 2018 were retrospectively collected, and the associations between KRAS mutations and other indicators were analysed. Receiver operating characteristic (ROC) curve analysis was performed to quantify the predictive value of these factors. Univariate and multivariate logistic regression models were applied to identify predictors of KRAS mutations by calculating the odds ratios (ORs) and their corresponding 95% confidence intervals (CIs). RESULTS: KRAS mutations were identified in 276 patients (35.2%). ROC analysis revealed that age, CA12-5, AFP, SCC, CA72-4, CA15-3, FERR, CYFRA21-1, MCHC, and tumor location could not predict KRAS mutations (P = 0.154, 0.177, 0.277, 0.350, 0.864, 0.941, 0.066, 0.279, 0.293, and 0.053 respectively), although CEA, CA19-9, NSE and haematological parameter values showed significant predictive value (P = 0.001, < 0.001, 0.043 and P = 0.003, < 0.001, 0.001, 0.031, 0.030, 0.016, 0.015, 0.019, and 0.006, respectively) but without large areas under the curve. Multivariate logistic regression analysis showed that CA19-9 was significantly associated with KRAS mutations and was the only independent predictor of KRAS positivity (P = 0.016). CONCLUSIONS: Haematological parameters and STMs were related to KRAS mutation status, and CA19-9 was an independent predictive factor for KRAS gene mutations. The combination of these clinical factors can improve the ability to identify KRAS mutations in CRC patients.