Distant Metastasis Pattern and Prognostic Prediction Model of Colorectal Cancer Patients Based on Big Data Mining

Development and validation of nomograms for predicting overall survival and cancer-specific survival in unresected colorectal cancer patients undergoing chemotherapy

This study aims to develop nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) in colorectal cancer (CRC) patients who did not receive primary site surgery but underwent chemotherapy. We analyzed data from 3,050 patients treated with chemotherapy without primary site surgery from 2010 to 2015, sourced from the Surveillance, Epidemiology, and End Results (SEER) database. The data were randomly divided into training and validation sets. Initial variable selection was performed using the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis was used to identify independent prognostic factors. Two nomograms were subsequently constructed based on these factors. Survival analysis was conducted using Kaplan-Meier plots and the log-rank test. We identified nine significant predictors of OS and CSS: age, marital status, primary site, grade, histology, T stage, M stage, tumor size, and CEA levels. The models for OS and CSS exhibited excellent predictability, with time-dependent area under the receiver operating characteristic curves (AUCs) exceeding 0.7. Calibration curves confirmed the accuracy of these predictions in the training and validation sets. Additionally, decision curve analysis (DCA) indicated that our models provide greater clinical benefit than traditional TNM staging. Notably, survival outcomes varied significantly across risk categories, affirming the models' effective discrimination. For CRC patients who did not receive primary site surgery but underwent chemotherapy, this validated nomogram enables precision prognostication fundamentally shifting the paradigm from population-level TNM estimates to individualized risk-adaptive management.

Biomarkers of lymph node metastasis in colorectal cancer: update

Colorectal cancer (CRC) ranks as the second leading cause of cancer-related deaths globally, trailing only behind lung cancer, and stands as the third most prevalent malignant tumor, following lung and breast cancers. The primary cause of mortality in colorectal cancer (CRC) stems from distant metastasis. Among the various routes of metastasis in CRC, lymph node metastasis predominates, serving as a pivotal factor in both prognostication and treatment decisions for patients. This intricate cascade of events involves multifaceted molecular mechanisms, highlighting the complexity underlying lymph node metastasis in CRC. The cytokines or proteins involved in lymph node metastasis may represent the most promising lymph node metastasis markers for clinical use. In this review, we aim to consolidate the current understanding of the mechanisms and pathophysiology underlying lymph node metastasis in colorectal cancer (CRC), drawing upon insights from the most recent literatures. We also provide an overview of the latest advancements in comprehending the molecular underpinnings of lymph node metastasis in CRC, along with the potential of innovative targeted therapies. These advancements hold promise for enhancing the prognosis of CRC patients by addressing the challenges posed by lymph node metastasis.

Impact of Preoperative Neutrophil to Prealbumin Ratio Index (NPRI) on Short-Term Complications and Long-Term Prognosis in Patients Undergoing Laparoscopic Radical Surgery for Colorectal Cancer

Objective

This study aims to evaluate the impact and predictive value of the preoperative NPRI on short-term complications and long-term prognosis in patients undergoing laparoscopic radical surgery for colorectal cCancer (CRC).

Methods

A total of 302 eligible CRC patients were included, assessing five inflammation-and nutrition-related markers and various clinical features for their predictive impact on postoperative outcomes. Emphasis was on the novel indicator NPRI to elucidate its prognostic and predictive value for perioperative risks.

Results

Multivariate logistic regression analysis identified a history of abdominal surgery, prolonged surgical duration, CEA levels ≥5 ng/mL, and NPRI ≥ 3.94 × 10-2 as independent risk factors for postoperative complications in CRC patients. The Clavien--Dindo complication grading system highlighted the close association between preoperative NPRI and both common and severe complications. Multivariate analysis also identified a history of abdominal surgery, tumor diameter ≥5 cm, poorly differentiated or undifferentiated tumors, and NPRI ≥ 2.87 × 10-2 as independent risk factors for shortened overall survival (OS). Additionally, a history of abdominal surgery, tumor maximum diameter ≥5 cm, tumor differentiation as poor/undifferentiated, NPRI ≥ 2.87 × 10-2, and TNM Stage III were determined as independent risk factors for shortened disease-free survival (DFS). Survival curve results showed significantly higher 5-year OS and DFS in the low NPRI group compared to the high NPRI group. The incorporation of NPRI into nomograms for OS and DFS, validated through calibration and decision curve analyses, attested to the excellent accuracy and practicality of these models.

Conclusion

Preoperative NPRI independently predicts short-term complications and long-term prognosis in patients undergoing laparoscopic colorectal cancer surgery, enhancing predictive accuracy when incorporated into nomograms for patient survival.

Disease characteristics and prognostic factors of colorectal cancer patients with bone metastasis: A real-world data from Turkey

BACKGROUND

Bone metastasis is rarely seen in colorectal cancer (CRC) patients, and there is insufficient data available regarding such cases. The study aimed to identify the prognostic factors and characteristics associated with overall survival in patients with bone metastatic CRC.

METHOD

Data from bone metastatic CRC patients referred to a high-volume tertiary cancer center in Turkey, between January 2018 and April 2021, were retrospectively collected. The records of 150 consecutive patients treated for bone metastases due to CRC were reviewed. Overall survival curves were generated by the Kaplan-Meier method and analyzed using the log-rank test.

RESULTS

Median age was 55 years (19-86 years). Bone metastases were more common in men and those with metachronous metastases. The axial skeleton was the most commonly involved site, and patients were frequently presented with single bone metastasis. Peritoneal metastases were significantly correlated with extra-axial metastases (P = 0.002), and radiotherapy was applied to axial metastases significantly, more frequently (P = 0.02). Lung metastasis was also more prevalent in K-RAS mutated patients (P = 0.008). The median survival time from diagnosis of bone metastasis was 8.3 months (95% confidence interval (CI), 5.5-10.6), and the three-year survival rate was 76.9% (95% CI, 69.8-84.0). Multivariate analysis revealed that brain metastases, right-sided colon tumor, high serum ALP, and Ca 19-9 levels were independent poor prognostic factors (P = 0.01, 0.02, <0.001, and 0.04, respectively).

CONCLUSIONS

The location of CRC correlates significantly with the site of bone metastasis; the prognosis of CRC patients with bone metastasis is very poor, and the significant poor prognostic factors are brain metastases, right-sidedness, high serum ALP, and Ca 19-9 levels. More attention should be paid to bone metastasis in CRC patients.

A competing risk nomogram to predict cancer-specific mortality of patients with late-onset colorectal cancer

OBJECTIVE

This study aimed to compare the clinical characteristics and survival differences between early-onset colorectal cancer (EOCRC) patients and late-onset colorectal cancer (LOCRC) patients, identify the risk factors for cancer-specific mortality (CSM) in LOCRC patients and construct a mortality risk assessment nomogram.

METHODS

CRC patients diagnosed pathologically between 2010 and 2019 in the SEER database were included and divided into the early-onset group and the late-onset group, and the late-onset group was divided into the training and validation sets. The Fine-Gray competing risk model was applied to analyze the prognostic factors of LOCRC patients and establish a competing risk nomogram for CSM.

RESULTS

There are differences in the distribution of multiple clinical features between the early-onset group and the late-onset group. Age, tumor size, histological type, pathological grading, T stage, N stage, M stage, SEER stage, primary tumor surgery, metastatic lesion surgery, radiotherapy, chemotherapy, neural invasion, and carcinoembryonic antigen (CEA) were independent influencing factors of the CSM rate in LOCRC patients. The C-index of the prognosis model outweighed 0.8, and the calibration curves fitted the reference line well.

CONCLUSION

The CSM competing risk nomogram for LOCRC patients in this study had acceptable predictive performance that could be applied to the clinic.

Lipid Nanoparticles-Based Therapy in Liver Metastasis Management: From Tumor Cell-Directed Strategy to Liver Microenvironment-Directed Strategy

Metastasis to the liver, as one of the most frequent metastatic patterns, was associated with poor prognosis. Major drawbacks of conventional therapies in liver metastasis were the lack of metastatic-targeting ability, predominant systemic toxicities and incapability of tumor microenvironment modulations. Lipid nanoparticles-based strategies like galactosylated, lyso-thermosensitive or active-targeting chemotherapeutics liposomes have been explored in liver metastasis management. This review aimed to summarize the state-of-art lipid nanoparticles-based therapies in liver metastasis management. Clinical and translational studies on the lipid nanoparticles in treating liver metastasis were searched up to April, 2023 from online databases. This review focused not only on the updates in drug-encapsulated lipid nanoparticles directly targeting metastatic cancer cells in treating liver metastasis, but more importantly on research frontiers in drug-loading lipid nanoparticles targeting nonparenchymal liver tumor microenvironment components in treating liver metastasis, which showed promise for future clinical oncological practice.

Experimental Murine Models for Colorectal Cancer Research

Colorectal cancer (CRC) is the third most prevalent malignancy worldwide and in both sexes. Numerous animal models for CRC have been established to study its biology, namely carcinogen-induced models (CIMs) and genetically engineered mouse models (GEMMs). CIMs are valuable for assessing colitis-related carcinogenesis and studying chemoprevention. On the other hand, CRC GEMMs have proven to be useful for evaluating the tumor microenvironment and systemic immune responses, which have contributed to the discovery of novel therapeutic approaches. Although metastatic disease can be induced by orthotopic injection of CRC cell lines, the resulting models are not representative of the full genetic diversity of the disease due to the limited number of cell lines suitable for this purpose. On the other hand, patient-derived xenografts (PDX) are the most reliable for preclinical drug development due to their ability to retain pathological and molecular characteristics. In this review, the authors discuss the various murine CRC models with a focus on their clinical relevance, benefits, and drawbacks. From all models discussed, murine CRC models will continue to be an important tool in advancing our understanding and treatment of this disease, but additional research is required to find a model that can correctly reflect the pathophysiology of CRC.

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

The liver is the most prevalent location of distant metastasis for pancreatic cancer (PC), which is highly aggressive. Pancreatic cancer with liver metastases (PCLM) patients have a poor prognosis. Furthermore, there is a lack of effective predictive tools for anticipating the diagnostic and prognostic techniques that are needed for the PCLM patients in current clinical work. Therefore, we aimed to construct two nomogram predictive models incorporating common clinical indicators to anticipate the risk factors and prognosis for PCLM patients. Clinicopathological information on pancreatic cancer that referred to patients who had been diagnosed between the years of 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses and a Cox regression analysis were utilized to recognize the independent risk variables and independent predictive factors for the PCLM patients, respectively. Using the independent risk as well as prognostic factors derived from the multivariate regression analysis, we constructed two novel nomogram models for predicting the risk and prognosis of PCLM patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, the consistency index (C-index), and the calibration curve were then utilized to establish the accuracy of the nomograms’ predictions and their discriminability between groups. Using a decision curve analysis (DCA), the clinical values of the two predictors were examined. Finally, we utilized Kaplan–Meier curves to examine the effects of different factors on the prognostic overall survival (OS). As many as 1898 PCLM patients were screened. The patient’s sex, primary site, histopathological type, grade, T stage, N stage, bone metastases, lung metastases, tumor size, surgical resection, radiotherapy, and chemotherapy were all found to be independent risks variables for PCLM in a multivariate logistic regression analysis. Using a multivariate Cox regression analysis, we discovered that age, histopathological type, grade, bone metastasis, lung metastasis, tumor size, and surgery were all independent prognostic variables for PCLM. According to these factors, two nomogram models were developed to anticipate the prognostic OS as well as the risk variables for the progression of PCLM in PCLM patients, and a web-based version of the prediction model was constructed. The diagnostic nomogram model had a C-index of 0.884 (95% CI: 0.876–0.892); the prognostic model had a C-index of 0.686 (95% CI: 0.648–0.722) in the training cohort and a C-index of 0.705 (95% CI: 0.647–0.758) in the validation cohort. Subsequent AUC, calibration curve, and DCA analyses revealed that the risk and predictive model of PCLM had high accuracy as well as efficacy for clinical application. The nomograms constructed can effectively predict risk and prognosis factors in PCLM patients, which facilitates personalized clinical decision-making for patients.

Effective oxygen metabolism-based prognostic signature for colorectal cancer

Backgroud

Oxygen metabolism is an important factor affecting the development of tumors, but its roles and clinical value in Colorectal cancer are not clear. We developed an oxygen metabolism (OM) based prognostic risk model for colorectal cancer and explored the role of OM genes in cancer.

Methods

Gene expression and clinical data obtained from The Cancer Genome Atlas, Clinical Proteomic Tumor Analysis Consortium databases were consider as discovery and validation cohort, respectively. The prognostic model based on differently expressed OM genes between tumor and GTEx normal colorectal tissues were constructed in discovery cohort and validated in validation cohort. The Cox proportional hazards analysis was used to test clinical independent. Upstream and downstream regulatory relationships and interaction molecules are used to clarify the roles of prognostic OM genes in colorectal cancer.

Results

A total of 72 common differently expressed OM genes were detected in the discovery and validation set. A five-OM gene prognostic model including LRT2, ATP6V0E2, ODC1, SEL1L3 and VDR was established and validated. Risk score determined by the model was an independent prognostic according to routine clinical factors. Besides, the role of prognostic OM genes involves transcriptional regulation of MYC and STAT3, and downstream cell stress and inflammatory response pathways.

Conclusions

We developed a five-OM gene prognostic model and study the unique roles of oxygen metabolism in of colorectal cancer.

Construction and systematic evaluation of a machine learning-based cuproptosis-related lncRNA score signature to predict the response to immunotherapy in hepatocellular carcinoma

Introduction

Hepatocellular carcinoma (HCC) is a common malignant cancer with a poor prognosis. Cuproptosis and associated lncRNAs are connected with cancer progression. However, the information on the prognostic value of cuproptosis-related lncRNAs is still limited in HCC.

Methods

We isolated the transcriptome and clinical information of HCC from TCGA and ICGC databases. Ten cuproptosis-related genes were obtained and related lncRNAs were correlated by Pearson's correlation. By performing lasso regression, we created a cuproptosis-related lncRNA prognostic model based on the cuproptosis-related lncRNA score (CLS). Comprehensive analyses were performed, including the fields of function, immunity, mutation and clinical application, by various R packages.

Results

Ten cuproptosis-related genes were selected, and 13 correlated prognostic lncRNAs were collected for model construction. CLS was positively or negatively correlated with cancer-related pathways. In addition, cell cycle and immune related pathways were enriched. By performing tumor microenvironment (TME) analysis, we determined that T-cells were activated. High CLS had more tumor characteristics and may lead to higher invasiveness and treatment resistance. Three genes (TP53, CSMD1 and RB1) were found in high CLS samples with more mutational frequency. More amplification and deletion were detected in high CLS samples. In clinical application, a CLS-based nomogram was constructed. 5-Fluorouracil, gemcitabine and doxorubicin had better sensitivity in patients with high CLS. However, patients with low CLS had better immunotherapeutic sensitivity.

Conclusion

We created a prognostic CLS signature by machine learning, and we comprehensively analyzed the signature in the fields of function, immunity, mutation and clinical application.

New Personal Model for Forecasting the Outcome of Patients with Histological Grade III-IV Colorectal Cancer Based on Regional Lymph Nodes

Background

Metastases at regional lymph nodes could easily occur in patients with high-histological-grade colorectal cancer (CRC). However, few models were built on the basis of lymph nodes to predict the outcome of patients with histological grades III-IV CRC.

Methods

Data in the Surveillance, Epidemiology, and End Results databases were used. Univariate and multivariate analyses were performed. A personalized prediction model was built in accordance with the results of the analyses. A nomogram was tested in two datasets and assessed using a calibration curve, a consistency index (C-index), and an area under the curve (AUC).

Results

A total of 14,039 cases were obtained from the database. They were separated into two groups (9828 cases for constructing the model and 4211 cases for validation). Logistic and Cox regression analyses were then conducted. Factors such as log odds of positive lymph nodes (LODDS) were utilized. Then, a personalized prediction model was established. The C-index in the construction and validation groups was 0.770. The 1-, 3-, and 5-year AUCs were 0793, 0.828, and 0.830 in the construction group, respectively, and 0.796, 0.833, and 0.832 in the validation group, respectively. The calibration curves showed well consistency in the 1-, 3- and 5-year OS between prediction and reality in both groups.

Conclusion

The nomogram built based on LODDS exhibited considerable reliability and accuracy.

Predictive models based on machine learning for bone metastasis in patients with diagnosed colorectal cancer

Background

This study aimed to develop an artificial intelligence predictive model for predicting the probability of developing BM in CRC patients.

Methods

From SEER database, 50,566 CRC patients were identified between January 2015 and December 2019 without missing data. SVM and LR models were trained and tested on the dataset. Accuracy, area under the curve (AUC), and IDI were used to evaluate and compare the models.

Results

For bone metastases in the entire cohort, SVM model with poly as kernel function presents the best performance, whose accuracy is 0.908, recall is 0.838, and AUC is 0.926, outperforming LR model. The top three most important factors affecting the model's prediction of BM include extraosseous metastases (EM), CEA, and size.

Conclusion

Our study developed an SVM model with poly as kernel function for predicting BM in CRC patients. SVM model could improve personalized clinical decision-making, help rationalize the bone metastasis screening process, and reduce the burden on healthcare systems and patients.