Significance of lymph node metastasis in the survival of stage IV colorectal cancer by hematogenous metastasis

A radiomics model based on T2WI and clinical indexes for prediction of lateral lymph node metastasis in rectal cancer

OBJECTIVE

The aim of this study was to explore the clinical value of a radiomics prediction model based on T2-weighted imaging (T2WI) and clinical indexes in predicting lateral lymph node (LLN) metastasis in rectal cancer patients.

METHODS

This was a retrospective analysis of 106 rectal cancer patients who had undergone LLN dissection. The clinical risk factors for LLN metastasis were selected by multivariable logistic regression analysis of the clinical indicators of the patients. The LLN radiomics features were extracted from the pelvic T2WI of the patients. The least absolute shrinkage and selection operator algorithm and backward stepwise regression method were adopted for feature selection. Three LLN metastasis prediction models were established through logistic regression analysis based on the clinical risk factors and radiomics features. Model performance was assessed in terms of discriminability and decision curve analysis in the training, verification and test sets.

RESULTS

The model based on the combined T2WI radiomics features and clinical risk factors demonstrated the highest accuracy, surpassing the models based solely on either T2WI radiomics features or clinical risk factors. Specifically, the model achieved an AUC value of 0.836 in the test set. Decision curve analysis revealed that this model had the greatest clinical utility for the vast majority of the threshold probability range from 0.4 to 1.0.

CONCLUSION

Combining T2WI radiomics features with clinical risk factors holds promise for the noninvasive assessment of the biological characteristics of the LLNs in rectal cancer, potentially aiding in therapeutic decision-making and optimizing patient outcomes.

Diagnostic value of a radiomics model based on CT and MRI for prediction of lateral lymph node metastasis of rectal cancer

This study aimed to develop a radiomics model for predicting lateral lymph node (LLN) metastasis in rectal cancer patients using MR-T2WI and CT images, and assess its clinical value. This prospective study included rectal cancer patients with complete MR-T2WI and portal enhanced CT images who underwent LLN dissection at Tianjin Union Medical Center between June 2017 and November 2022. Primary lesions and LLN were segmented using 3D slicer. Radiomics features were extracted from the region of interest using pyradiomics in Python. Least absolute shrinkage and selection operator algorithm and backward stepwise regression were employed for feature selection. Three LLN metastasis radiomics prediction models were established via multivariable logistic regression analysis. The performance of the model was evaluated using receiver operating characteristic curve analysis, and the area under the curve (AUC), sensitivity, specificity were calculated for the training, validation, and test sets. A nomogram was constructed for visualization, and decision curve analysis (DCA) was performed to evaluate clinical value. We included 94 eligible patients in the analysis. For each patient, we extracted a total of 1344 radiomics features. The CT combined with MR-T2WI model had the highest AUC for all sets compared to CT and MR-T2WI models. AUC values for the CT combined with MR-T2WI model in the training, validation, and test sets were 0.957, 0.901, and 0.936, respectively. DCA revealed high prediction value for the combined MR-T2WI and CT model. A radiomics model based on CT and MR-T2WI data effectively predicted LLN metastasis in rectal cancer patients preoperatively.

B7-H4 expression is upregulated by PKCδ activation and contributes to PKCδ-induced cell motility in colorectal cancer

Introduction

B7-H4 is overexpressed in colorectal cancer (CRC) and plays an important role in tumor growth and immunosuppression. However, the exact mechanism that regulates B7-H4 expression remains largely unknown. Here, we investigated whether protein kinase C δ (PKCδ) regulates the expression of B7-H4 in CRC.

Methods

By using immunohistochemical (IHC) and immunofluorescence (IF) staining, we analyzed the expression of B7-H4 and phospho-PKCδ (p-PKCδ) in 225 colorectal tumor samples and determined the clinical significance of the expression patterns. In vitro experiments were performed with the CRC cell lines HCT116 and SW620 to detect the effect of PKCδ activation on B7-H4 expression, and xenograft-bearing mice were treated with rottlerin to monitor the expression of B7-H4 and tumor metastasis.

Results

The B7-H4 expression level was significantly correlated with the p-PKCδ level (r = 0.378, P < 0.001) in tumor tissues. Coexpression of p-PKCδ and B7-H4 was significantly associated with moderate/poor differentiation (P = 0.024), lymph node metastasis (P = 0.001) and advanced Dukes’ stage (P = 0.002). Western blot analysis showed that Phorbol-12-Myristate-13-Acetate (TPA) increased B7-H4 expression in a concentration-dependent manner and that rottlerin abrogated the TPA-induced increase in B7-H4 expression. The protein levels of B7-H4 and p-STAT3 were significantly reduced by a PKCδ-specific siRNA. Moreover, the STAT3 inhibitor cryptotanshinone significantly decreased the B7-H4 protein level in CRC cells. Knockdown of B7-H4 or PKCδ suppressed cell migration and motility. Rottlerin also inhibited B7-H4 expression and tumor metastasis in vivo.

Conclusion

The B7-H4 expression level is significantly correlated with the p-PKCδ level and tumor metastasis in CRC samples. B7-H4 expression is upregulated by STAT3 activation via PKCδ and plays roles in PKCδ-induced cancer cell motility and metastasis, suggesting that the PKCδ/STAT3/B7-H4 axis may be a potential therapeutic target for CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02567-1.

Follistatin-Like 3 Correlates With Lymph Node Metastasis and Serves as a Biomarker of Extracellular Matrix Remodeling in Colorectal Cancer

Background

As a heterogeneous disease, colorectal cancer (CRC) presents a great challenge to individualized treatment due to its lymph node metastasis (LNM). Existing studies have shown that immune and stromal components in extracellular matrix (ECM) act as important part in tumorigenicity and progression, while their roles in LNM have not been fully elucidated. Here, crucial ECM-related genes responsible for LNM in CRC were selected by multi-omics analysis.

Methods

Firstly, we characterized the immune infiltration landscape of CRC samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases by using ssGSEA algorithm. The CRC patients were divided into several immune subgroups by hierarchical clustering analyses. Then, differential genes were identified among immune subgroups and CRC vs. normal tissues in TCGA and GEO GSE39582 cohorts, respectively. Next, weighted correlation network analysis (WGCNA) was employed to construct a co-expression network to find LNM-related modules and hub genes. Subsequently, we evaluated the clinical value of hub gene in prognostic prediction and chemotherapy/immunotherapy. Besides, the protein level of key gene was verified in an external cohort from our center. Finally, we explored the underlying mechanism of FSTL3-mediated LNM by Gene function annotation and correlation analysis.

Results

Two immune subgroups, namely Immunity_High and Immunity_Low, were defined among the two CRC cohorts using ssGSEA algorithm, respectively. Based on the two immune subgroups, 2,635 overlapping differentially expressed genes were obtained from two cohorts, which were sequentially subjected to WGCNA and univariate Cox regression analysis. Ultimately, FSTL3 was selected as the key gene. Here, we first confirmed that overexpression of FSTL3 correlated with LNM and worse prognosis in CRC and was verified at the protein level in the external validation cohort. Moreover, FSTL3 expression showed strongly positive correlation with immune and stromal components in ECM. We furthermore found that FSTL3 may accelerate LNM through the formation of inhibitory immune microenvironment via promoting macrophage and fibroblast polarization and T cell exhaustion. Interestingly, high FSTL3 expression is linked to chemoresistance, but immunotherapy-sensitive.

Conclusion

FSTL3 is identified as a biomarker for ECM remodeling and worse clinical outcomes for the first time in CRC and is also a potential immunotherapeutic target to block LNM for CRC.

Preoperative Predictors of Lymph Node Metastasis in Colon Cancer

Background

Lymph node metastasis (LNM) is a well-established prognostic factor for colon cancer. Preoperative LNM evaluation is relevant for planning colon cancer treatment. The aim of this study was to construct and evaluate a nomogram for predicting LNM in primary colon cancer according to pathological features.

Patients and Methods

Six-hundred patients with clinicopathologically confirmed colon cancer (481 cases in the training set and 119 cases in the validation set) were enrolled in the Affiliated Cancer Hospital of Guangxi Medical University from January 2010 to December 2019. The expression of molecular markers (p53 and β-catenin) was determined by immunohistochemistry. Multivariate logistic regression was used to screen out independent risk factors, and a nomogram was established. The accuracy and discriminability of the nomogram were evaluated by consistency index and calibration curve.

Results

Univariate logistic analysis revealed that LNM in colon cancer is significantly correlated (P <0.05) with tumor size, grading, stage, preoperative carcinoembryonic antigen (CEA) level, and peripheral nerve infiltration (PNI). Multivariate logistic regression analysis confirmed that CEA, grading, and PNI were independent prognostic factors of LNM (P <0.05). The nomogram for predicting LNM risk showed acceptable consistency and calibration capability in the training and validation sets.

Conclusions

Preoperative CEA level, grading, and PNI were independent risk factor for LNM. Based on the present parameters, the constructed prediction model of LNM has potential application value.

Preoperative prediction of regional lymph node metastasis of colorectal cancer based on 18F-FDG PET/CT and machine learning

PURPOSE

To establish and validate a regional lymph node (LN) metastasis prediction model of colorectal cancer (CRC) based on ¹⁸F-FDG PET/CT and radiomic features using machine-learning methods.

METHODS

A total of 199 colorectal cancer patients underwent pre-therapy diagnostic ¹⁸F-FDG PET/CT scans and CRC radical surgery. The Chang-Gung Image Texture Analysis toolbox (CGITA) was used to extract 70 PET radiomic features reflecting ¹⁸F-FDG uptake heterogeneity of tumors. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomic features and develop a radiomic signature score (Rad-score). The training set was used to establish five machine-learning prediction models and the test set was used to test the efficacy of the models. The effectiveness of the models was compared by ROC analysis.

RESULTS

The CRC patients were divided into a training set (n = 144) and a test set (n = 55). Two radiomic features were selected to build the Rad-score. Five machine-learning algorithms including logistic regression, support vector machine (SVM), random forest, neural network and eXtreme gradient boosting (XGBoost) were used to established models. Among the five machine-learning models, logistic regression (AUC 0.866, 95% CI 0.808-0.925) and XGBoost (AUC 0.903, 95% CI 0.855-0.951) models performed the best. In the training set, the AUC of these two models were significantly higher than that of the LN metastasis status reported by ¹⁸F-FDG PET/CT for differentiating positive and negative regional LN metastases in CRC (all p < 0.05). Good efficacy of the above two models was also achieved in the test set. We created a nomogram based on the logistic regression model that visualized the results and provided an easy-to-use method for predicting regional LN metastasis in patients with CRC.

CONCLUSION

In this study, five machine-learning models for preoperative prediction of regional LN metastasis of CRC based on ¹⁸F-FDG PET/CT and PET-based radiomic features were successfully developed and validated. Among them, the logistic regression and XGBoost models performed the best, with higher efficacy than ¹⁸F-FDG PET/CT in both the training and test sets.

Molecular Alterations in Metastatic Ovarian Cancer From Gastrointestinal Cancer

Reports indicate that most metastatic ovarian cancer (MOC) originates from gastrointestinal cancer (GIC). Notably, GICs metastasize to the ovary frequently via 3 main routes including hematogenous spread, lymphogenous spread, and transcoelomic spread. Nonetheless, the mechanism of the progression remains unknown, and only a handful of literature exists on the molecular alteration implicated in MOC from GIC. This work collected existing evidence and literature on the vital molecules of the metastatic pathway and systematically analyzed them geared toward exploring the mechanism of the metastatic pathway of MOC. Further, this review described dominating molecular alteration in the metastatic process from cancer cells detaching away from lesions to arrive at the ovary, including factors for regulating signaling pathways in epithelial-interstitial transformation, invading, and surviving in the circulatory system or abdominal cavity. We interrogated the basis of the ovary as a distant metastatic site. This article provides new insights into the metastatic pathway and generates novel therapeutic targets for effective treatment and satisfactory outcomes in GIC patients.

MCCC2 overexpression predicts poorer prognosis and promotes cell proliferation in colorectal cancer

PURPOSES

Recently, Methylcrotonoyl-CoA carboxylase 2 (MCCC2) is reported to be involved in tumor formation and progression. However, MCCC2 has nerve been reported in colorectal cancer. In this study, we aimed to investigate the role of MCCC2 in colorectal cancer.

METHODS

118 colorectal cancer and matched adjacent normal tissues were enrolled in this study. The expression level of MCCC2 was measured by quantificational real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). The clinical significance of MCCC2 and its influence on cell proliferation was further analyzed.

RESULTS

Results shown that the mRNA levels of MCCC2 in colorectal cancer tissues were significantly increased compared with those in normal tissues (P < .0001). MCCC2 high-expression was observed in 56.8% colorectal cancer tissues, which was significantly higher than those in normal controls (9.3%, P < .0001). MCCC2 high-expression correlated with tumor size, T stage, lymph node metastasis, distant metastasis, clinical stage and differentiation in colorectal cancer (P < .05). Moreover, MCCC2 high-expression predicted poorer prognosis and could be as an independent prognostic factor. In addition, MCCC2 knockdown significantly inhibited cell proliferation compared with these controls, while MCCC2 overexpression could reverse the effect.

CONCLUSION

These data indicate MCCC2 overexpression promotes cell proliferation and predicts poorer prognosis in colorectal cancer.

Clinical value of detecting IQGAP3, B7-H4 and cyclooxygenase-2 in the diagnosis and prognostic evaluation of colorectal cancer

Background

The IQ-motif-containing GTPase-activating protein (IQGAP) family comprises three members, IQGAP1, IQGAP2 and IQGAP3. IQGAP3 is the latest addition to the family. This study mainly investigated the novel marker IQGAP3 at serum and tumor tissue levels compared with the markers B7-H4 and cyclooxygenase-2 (COX-2) in patients with colorectal cancer (CRC) and in healthy individuals, aiming to evaluate the diagnostic and prognostic value of IQGAP3 for CRC.

Materials and methods

Serum samples were collected prior to any therapy in 118 CRC patients and as part of a routine examination in 85 healthy individuals. Serum IQGAP3, B7-H4 and COX-2 levels were measured using commercially available ELISA kits. Immunohistochemistry was performed to detect the IQGAP3, B7-H4 and COX-2 in tumor tissues and normal para-carcinoma tissues. The receiver operating characteristics (ROC) curve and the area under the curve (AUC) were used to evaluate and compare the diagnostic value of different serum tumor markers. Univariate and multivariate analyses were performed to identify the prognostic risk factors for CRC.

Results

IQGAP3, B7-H4 and COX-2 showed low or high expression in tumor tissues while no expression in normal para-carcinoma tissues. Serum levels of IQGAP3 in CRC group were significantly higher than those in healthy control group (P < 0.001). The IQGAP3 AUC was 0.799, while the B7-H4 AUC was 0.795 and the COX-2 AUC was 0.796. IQGAP3 seemed to be superior to B7-H4 and COX-2 in detecting CRC, with the highest sensitivity among the three markers. Multivariate analysis showed that T stage, N stage, differentiation degree, TNM stage and both serum and tissue IQGAP3, B7-H4 and COX-2 levels were significant prognostic factors for CRC.

Conclusions

IQGAP3 has a better diagnostic efficacy than B7-H4 and COX-2 in detecting CRC and it has value in predicting the prognosis of patients with CRC.