Lymph Node Metastasis In Colorectal Cancer Research Articles

Preoperative prediction of lymph node metastasis in colorectal cancer using 18F-FDG PET/CT peritumoral radiomics analysis.

Radiomics has been used in the diagnosis of tumor lymph node metastasis (LNM). However, to date, most studies have been based on intratumoral radiomics. Few studies have focused on the use of 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT) peritumoral radiomics for the diagnosis of LNM in colorectal cancer (CRC). Determining the value of radiomics features extracted from 18F-FDG PET/CT images of the peritumoral region in predicting LNM in patients with CRC. The clinical data and preoperative 18F-FDG PET/CT images of 244 CRC patients were retrospectively analyzed. Intratumoral and peritumoral radiomics features were screened using the mutual information method, and least absolute shrinkage and selection operator regression. Based on the selected radiomics features, a radiomics score (Rad-score) was calculated, and independent risk factors obtained from univariate and multivariate logistic regression analyses were used to construct clinical and combined (Radiomics + Clinical) models. The performance of these models was evaluated using the DeLong test, while their clinical utility was assessed by decision curve analysis. Finally, a nomogram was constructed to visualize the predictive model. The most optimal set of features retained by the feature filtering process were all peritumoral radiomic features. Carcinoembryonic antigen levels, PET/CT-reported lymph node status and Rad-score were found to be independent risk factors for LNM. All three LNM risk assessment models exhibited good predictive performance, with the combined model showing the best classification results, with areas under the curve of 0.85 and 0.76 in the training and validation groups, respectively. The DeLong test revealed that the performance of the combined model was superior to that of the clinical and radiomics models in both the training and validation groups, although this difference was only statistically significant in the training group. DCA indicated that the combined model displayed better clinical utility. 18F-FDG PET/CT peritumoral radiomics is uniquely suited to predict the presence of LNM in patients with CRC. In particular, the predictive efficacy of LNM for precision therapy and individualized patient management can be improved by using a combination of clinical risk factors.

Radiological Cutoff Values for Diagnosis of Lymph Node Metastasis in Colorectal Cancer With Multilevel Analysis.

A cutoff value for lymph node diameter in colorectal cancer lymph node metastases has not been established. This prospective study aimed to investigate the direct association between swollen lymph nodes identified on preoperative computed tomography (CT) and pathological findings and proposed a cutoff value. We enrolled patients scheduled to undergo curative surgery with lymph node dissection for colorectal adenocarcinoma who underwent preoperative contrast-enhanced CT and had swollen lymph nodes ≥7 mm in diameter. Two gastrointestinal surgeons intraoperatively identified the target lymph nodes to assess the association between lymph node diameter and pathological findings. The diagnostic performance for lymph node metastasis was determined using multi-level logistic modelling. A total of 109 patients were enrolled, and 225 swollen lymph nodes were pathologically evaluated. Using a cutoff value of ≥9 mm for the short diameter, the positive and negative predictive values, sensitivity, and specificity were 100.0% (99.6%-100.0%), 99.9% (99.1%-100.0%), 62.0% (45.6%-76.0%), and 84.9% (67.0%-94.0%), respectively. The cutoff value for improving the positive predictive value for the preoperative lymph node metastasis diagnosis in colorectal cancer patients should be at least 9 mm in diameter.

Association between Heavy Metals and Trace Elements in Cancerous and Non-cancerous Tissues with the Risk of Colorectal Cancer Progression in Northwest China.

Alterations in heavy metals and trace element levels may be associated with various cancers. However, the role of this interaction in colorectal cancer (CRC) progression is unclear. In recent years, Principal Component Analysis (PCA) and Bayesian Kernel Machine Regression (BKMR) models have provided new ideas for analyzing the effects of metal mixtures on CRC progression. Herein, we assessed the differences in the levels of arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), selenium (Se), and zinc (Zn) in tumors and adjacent healthy tissues, to investigate the relationship between heavy metals/trace elements and CRC progression. Surgical samples of CRC and noncancerous tissues were collected, and trace metal levels were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Logistic regression, PCA, and BKMR models were used to investigate the relationship between heavy metals and trace elements and the degree of tumor differentiation and lymph node metastasis in CRC. Cancer tissues showed lower As, Cd, Co, and Cr concentrations, and higher Se concentrations than healthy tissues (P < 0.05). In addition, CRC patients with poorly differentiated tumors and/or positive lymph node metastases had lower levels of Cd, Zn, Cu, and Se (P < 0.05). Logistic regression showed that single metal concentration was negatively correlated with CRC progression. PCA and BKMR models also showed that the metal mixture concentration was negatively correlated with CRC progression, with Cd contributing the most. Overall, changes in heavy metal and trace element levels may be related to the development of CRC; however, further mechanistic studies are required.

Differential microbiota network according to colorectal cancer lymph node metastasis stages

Differential microbiota network according to colorectal cancer lymph node metastasis stages

Targeting FAPα-positive lymph node metastatic tumor cells suppresses colorectal cancer metastasis

Lymphatic metastasis is the main metastatic route for colorectal cancer, which increases the risk of cancer recurrence and distant metastasis. The properties of the lymph node metastatic colorectal cancer (LNM-CRC) cells are poorly understood, and effective therapies are still lacking. Here, we found that hypoxia-induced fibroblast activation protein alpha (FAPα) expression in LNM-CRC cells. Gain- or loss-function experiments demonstrated that FAPα enhanced tumor cell migration, invasion, epithelial–mesenchymal transition, stemness, and lymphangiogenesis via activation of the STAT3 pathway. In addition, FAPα in tumor cells induced extracellular matrix remodeling and established an immunosuppressive environment via recruiting regulatory T cells, to promote colorectal cancer lymph node metastasis (CRCLNM). Z-GP-DAVLBH, a FAPα-activated prodrug, inhibited CRCLNM by targeting FAPα-positive LNM-CRC cells. Our study highlights the role of FAPα in tumor cells in CRCLNM and provides a potential therapeutic target and promising strategy for CRCLNM.

An Analysis of the Gene Expression Associated with Lymph Node Metastasis in Colorectal Cancer.

This study aimed to explore the genes regulating lymph node metastasis in colorectal cancer (CRC) and to clarify their relationship with tumor immune cell infiltration and patient prognoses. The data sets of CRC patients were collected through the Cancer Gene Atlas database; the differentially expressed genes (DEGs) associated with CRC lymph node metastasis were screened; a protein-protein interaction (PPI) network was constructed; the top 20 hub genes were selected; the Gene Ontology functions and the Kyoto Encyclopedia of Genes and Genomes pathways were enriched and analyzed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to further screen the characteristic genes associated with CRC lymph node metastasis in 20 hub genes, exploring the correlation between the characteristic genes and immune cell infiltration, conducting a univariate COX analysis on the characteristic genes, obtaining survival-related genes, constructing a risk score formula, conducting a Kaplan-Meier analysis based on the risk score formula, and performing a multivariate COX regression analysis on the clinical factors and risk scores. A total of 62 DEGs associated with CRC lymph node metastasis were obtained. Among the 20 hub genes identified via PPI, only calcium-activated chloride channel regulator 1 (CLCA1) expression was down-regulated in lymph node metastasis, and the rest were up-regulated. A total of nine characteristic genes associated with CRC lymph node metastasis (KIF1A, TMEM59L, CLCA1, COL9A3, GDF5, TUBB2B, STMN2, FOXN1, and SCN5A) were screened using the LASSO regression method. The nine characteristic genes were significantly related to different kinds of immune cell infiltration, from which three survival-related genes (TMEM59L, CLCA1, and TUBB2B) were screened. A multi-factor COX regression showed that the risk scores obtained from TMEM59L, CLCA1, and TUBB2B were independent prognostic factors. Immunohistochemical validation was performed in tissue samples from patients with rectal and colon cancer. TMEM59L, CLCA1, and TUBB2B were independent prognostic factors associated with lymphatic metastasis of CRC.

High serum mannose in colorectal cancer: a novel biomarker of lymph node metastasis and poor prognosis.

Lymph node status is an important prognostic indicator and it significantly influences treatment decisions for colorectal cancer (CRC). The objective of this study was to evaluate the ability of serum monosaccharides in predicting lymph node metastasis (LNM) and prognosis. High performance anion exchange chromatography coupled with pulsed amperometric detector (HPAEC-PAD) was used to quantify serum monosaccharides from 252 CRC patients. Receiver operating characteristic (ROC) curves were used to evaluate predictive performance of parameters. Predictors of LNM were evaluated by univariate and multivariate analyses. The prognostic role of the factors was evaluated by survival analysis. The levels of serum mannose (Man) and galactose (Gal) were significantly increased in patients with LNM (p <0.0001, p =0.0017, respectively). The area under the curves (AUCs) of Man was 0.8140, which was higher than carcinoembryonic antigen (CEA) (AUC =0.6523). Univariate and multivariate analyses demonstrated histologic grade (G3) (odds ratio [OR] =2.60, p =0.043), histologic grade (mucin-producing subtype) (odds ratio [OR] =3.38, p =0.032), lymphovascular invasion (LVI) (OR =2.42, p <0.01), CEA (>5ng/ml) (OR =1.85, p =0.042) and high Man (OR =2.65, p =0.006) to be independent risk factors of LNM. The survival analysis showed that the high serum Man was independent risk factor for poor prognosis in CRC patients (HR=1.75, p =0.004). The Man is superior to CEA in prediction of LNM for CRC patients. Man is expected to be a predictor for LNM in CRC. High serum Man is associated with poor prognosis of CRC patients.

Use of iTRAQ-based quantitative proteomic identification of CHGA and UCHL1 correlated with lymph node metastasis in colorectal carcinoma.

Metastatic dissemination of colorectal cancer (CRC), the third most common cancer type, is responsible for CRC deaths. Understanding the transition of lymph node metastasis (LNM) from Stage II to Stage III is beneficial in the prognosis and intervention of CRC. In this study, a quantitative proteomic survey was conducted to investigate the LNM-associated proteins and evaluate the clinicopathological characteristics of these target proteins in CRC. By using the LC-MS/MS iTRAQ technology, we analysed the proteomic changes between LMN II and LMN III. Fresh tumours from the CRC specimens consisting of 12 node-negative (Stage II) and 12 node-positive (Stage III) cases were analysed by LC-MS/MS iTRAQ proteome analysis. Subsequently, tissue microarray with immunohistochemistry staining was conducted to access the clinicopathological characteristics of these proteins in 116 paraffin-embedded CRC samples, each for non-LNM and LNM CRC. To study the effects of the differentially expressed proteins on the potential mechanism, Boyden chamber assay, flow cytometry and shRNA-based assessments were conducted to examine the role of the epithelial-mesenchymal transition (EMT) and the invasiveness of CRC cells and others in vivo xenograft mouse model experiments. Forty-eight proteins were found differentially expressed between non-LNM and LNM CRC tissues. Protein abundances of chromogranin-A (CHGA) and ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1) were observed in node-positive CRC (p < 0.05). Knockdown of CHGA and UCHL1 significantly regulate cancer behaviours of HCT-116, including inhibition of cell migration, invasiveness, cell cycle G1/S arrest and reactive oxygen species (ROS) generation. Mechanistically, the CHGA and UCHL1 inactivation displayed decreased levels of UCH-L1, chromogranin A, β-catenin, cyclin E, twist-1/2, vimentin, MMP-9, N-cadherin and PCNA through the activation of the Rho-GTPase/AKT/NFκB pathways. Histone modification of H3K4 trimethylation of CHGA and UCHL1 promoter were increased to activate their transcription through the signalling transduction such as Rho-GTPase, AKT and NFκB pathways. Our results indicated that UCHL1 and chromogranin A are novel regulators in CRC lymph node metastasis to potentially provide new insights into the mechanism of CRC progression and serve as biomarkers for CRC diagnosis at the metastatic stage.

Colorectal cancer lymph node metastasis prediction with weakly supervised transformer-based multi-instance learning

Lymph node metastasis examined by the resected lymph nodes is considered one of the most important prognostic factors for colorectal cancer (CRC). However, it requires careful and comprehensive inspection by expert pathologists. To relieve the pathologists’ burden and speed up the diagnostic process, in this paper, we develop a deep learning system with the binary positive/negative labels of the lymph nodes to solve the CRC lymph node classification task. The multi-instance learning (MIL) framework is adopted in our method to handle the whole slide images (WSIs) of gigapixels in size at once and get rid of the labor-intensive and time-consuming detailed annotations. First, a transformer-based MIL model, DT-DSMIL, is proposed in this paper based on the deformable transformer backbone and the dual-stream MIL (DSMIL) framework. The local-level image features are extracted and aggregated with the deformable transformer, and the global-level image features are obtained with the DSMIL aggregator. The final classification decision is made based on both the local and the global-level features. After the effectiveness of our proposed DT-DSMIL model is demonstrated by comparing its performance with its predecessors, a diagnostic system is developed to detect, crop, and finally identify the single lymph nodes within the slides based on the DT-DSMIL and the Faster R-CNN model. The developed diagnostic model is trained and tested on a clinically collected CRC lymph node metastasis dataset composed of 843 slides (864 metastasis lymph nodes and 1415 non-metastatic lymph nodes), achieving the accuracy of 95.3% and the area under the receiver operating characteristic curve (AUC) of 0.9762 (95% confidence interval [CI]: 0.9607–0.9891) for the single lymph node classification. As for the lymph nodes with micro-metastasis and macro-metastasis, our diagnostic system achieves the AUC of 0.9816 (95% CI: 0.9659–0.9935) and 0.9902 (95% CI: 0.9787–0.9983), respectively. Moreover, the system shows reliable diagnostic region localizing performance: the model can always identify the most likely metastases, no matter the model’s predictions or manual labels, showing great potential in avoiding false negatives and discovering incorrectly labeled slides in actual clinical use.Graphical

Prediction model of colorectal cancer (CRC) lymph node metastasis based on intestinal bacteria.

Lymph node metastasis is the main metastatic mode of CRC. Lymph node metastasis affects patient prognosis. To screen differential intestinal bacteria for CRC lymph node metastasis and construct a prediction model. First, fecal samples of 119 CRC patients with lymph node metastasis and 110 CRC patients without lymph node metastasis were included for the detection of intestinal bacterial 16S rRNA. Then, bioinformatics analysis of the sequencing data was performed. Community structure and composition analysis, difference analysis, and intragroup and intergroup correlation analysis were conducted between the two groups. Finally, six machine learning models were used to construct a prediction model for CRC lymph node metastasis. The community richness and the community diversity at the genus level of the two groups were basically consistent. A total of 12 differential bacteria (Agathobacter, Catenibacterium, norank_f__Oscillospiraceae, Lachnospiraceae_FCS020_group, Lachnospiraceae_UCG-004, etc.) were screened at the genus level. Differential bacteria, such as Agathobacter, Catenibacterium, norank_f__Oscillospiraceae, and Lachnospiraceae_FCS020_group, were more associated with no lymph node metastasis in CRC. In the discovery set, the RF model had the highest prediction accuracy (AUC = 1.00, 98.89% correct, specificity = 55.21%, sensitivity = 55.95%). In the test set, SVM model had the highest prediction accuracy (AUC = 0.73, 72.92% correct, specificity = 69.23%, sensitivity = 88.89%). Lachnospiraceae_FCS020_group was the most important variable in the RF model. Lachnospiraceae_UCG - 004 was the most important variable in the SVM model. CRC lymph node metastasis is closely related to intestinal bacteria. The prediction model based on intestinal bacteria can provide a new evaluation method for CRC lymph node metastasis.

MAN2A1 predicts prognosis and progression through cancer-related pathways in colorectal cancer.

Colorectal cancer (CRC) is a common malignant tumor leading to poor prognosis and high mortality. Mannosidase alpha class 2A member 1 (MAN2A1) turns to oncogene through fusing Fer tyrosine kinase (FER) and associates with multiple cancer occurrence. In order to determine whether MAN2A1 can promote tumorigenesis and metastasis in CRC, we conducted a series of studies. We obtained gene expression and clinical data of CRC from The Cancer Genome Atlas (TCGA) databases. RNA raw counts data was merged by Python. Batch processing of univariate Cox regression analysis was performed to preliminary identify the genes associated with prognosis. Differentially expressed genes (DEGs) between lymph node metastasis (LNM) patients and non-LNM patients were identified via edgR in Sangerbox tool. Protein-protein interactive (PPI) network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. Kaplan-Meier (KM) survival of CRC patients was analyzed by Sangerbox tool. Clinicopathologic characteristics of CRC patients were analyzed by SPSS statistics software. Differences in RNA expression levels of genes were validated in our cohort by real-time polymerase chain reaction (RT-PCR). Analyses of Signaling pathways and gene ontology were explored by gene set enrichment analysis (GSEA). We first obtained 4,455 genes associated with the prognosis of CRC, and 998 of these genes were also DEGs in CRC between metastatic CRC tissues and in situ tissues. Therein, MAN2A1 expression was downregulated in LNM CRC compared with CRC in situ, also downregulated in CRC compared with adjacent normal tissues, and high gene expression levels of MAN2A1 was associated with better survival. Our study suggested that MAN2A1 could be a potential biomarker significantly related to prognosis and LNM of CRC patients.

Role of one-step nucleic acid amplification in colorectal cancer lymph node metastases detection.

Current histopathological staging procedures in colorectal cancer (CRC) depend on midline division of the lymph nodes (LNs) with one section of hematoxylin and eosin staining. Cancer cells outside this transection line may be missed, which could lead to understaging of Union for International Cancer Control Stage II high-risk patients. The one-step nucleic acid amplification (OSNA) assay has emerged as a rapid molecular diagnostic tool for LN metastases detection. It is a molecular technique that can analyze the entire LN tissue using a reverse-transcriptase loop-mediated isothermal amplification reaction to detect tumor-specific cytokeratin 19 mRNA. Our findings suggest that the OSNA assay has a high diagnostic accuracy in detecting metastatic LNs in CRC and a high negative predictive value. OSNA is a standardized, observer-independent technique, which may lead to more accurate staging. It has been suggested that in stage II CRC, the upstaging can reach 25% and these patients can access postoperative adjuvant chemotherapy. Moreover, intraoperative OSNA sentinel node evaluation may allow early CRC to be treated with organ-preserving surgery, while in more advanced-stage disease, a tailored lymphadenectomy can be performed considering the presence of aberrant lymphatic drainage and skip metastases.

Expression Levels and Clinical Significance of Serum miR-497, CEA, CA24-2, and HBsAg in Patients with Colorectal Cancer.

The objective of the current study was to look at the levels of blood micro ribonucleic acid- (miR-) 497, carcinoembryonic antigen (CEA), carbohydrate antigen (CA) 24-2, and hepatitis B surface antigen (HBsAg) in patients with colorectal cancer (CRC), as well as the clinical importance of these markers in CRC patients. The serum levels of miR-497, CEA, CA24-2, and HBsAg were compared between 60 patients with CRC (observation group) and another 60 patients with colorectal polyps (control group). The 4 indicators in patients with lymph node metastasis and liver metastasis were compared. The diagnostic effects of 4 detection methods and the combined detection were analyzed, and the influence of 4 indicators on the 5-year cumulative survival rate of patients was discussed. The results showed that the serum levels of miR-497 and HBsAg were lower, and the levels of CEA and CA24-2 were higher in the observation group (P < 0.05). The combined detection had the best diagnostic effect, and CEA alone had the best prediction effect. The serum level of miR-497 was significantly lower in patients with lymphatic metastasis, with the significantly higher levels of CEA and CA24-2 (P < 0.05). The HBsAg level of patients with liver metastases was greatly lower than that of patients without liver metastases (P < 0.05). The 5-year cumulative survival rate of patients with high levels of CEA and CA24-2 was significantly lower than that of patients with low level of CEA. The 5-year cumulative survival rate was lower in patients with low level of HBsAg, but the difference was small. The 5-year cumulative survival rate of patients with elevated serum miR-497 was observably lower. In conclusion, combined detection could diagnose CRC more accurately. Serum miR-497, CEA, and CA24-2 were important in the diagnosis of lymph node metastasis of CRC. HBsAg did a better job of predicting liver metastases in CRC patients. High level of CEA significantly reduced the cumulative survival rate of CRC patients and could predict the long-term survival rate of patients. Serum levels of miR-497, CEA, CA24-2, and HBsAg played a positive role in the diagnosis and evaluation of CRC and could identify lymph node and liver metastases, having a high clinical guidance value.

2D-DIGE-MS Proteomics Approaches for Identification of Gelsolin and Peroxiredoxin 4 with Lymph Node Metastasis in Colorectal Cancer.

Simple SummarySchematic presentation of the histone modification of gelsolin (GSN) and peroxiredoxin-4 (PRDX4) promoters by endogenous EGFR/RhoA/PKC/ERK signaling pathways in DLD-1 cells. Identifying lymph node metastasis (LNM)-associated proteins by two-dimensional difference gel electrophoresis MS/MS in colorectal cells (CRC) indicates the reliability of candidate markers of GSN and PRDX4 and therapeutic targets for CRC.Background/Aims: A combination of fluorescence two-dimensional difference gel electrophoresis (2D-DIGE) and matrix-assisted laser desorption/ionization time of flight mass spectrometry approach was used to search for potential markers for prognosis and intervention of colorectal cancer (CRC) at different stages of lymph node metastasis (LMN). This quantitative proteomic survey aimed to investigate the LNM-associated proteins and evaluate the clinicopathological characteristics of these target proteins in CRC from stage I to stage IV. Methods: Sixteen CRC cases were categorized into paired non-LNM and LNM groups, and two-dimensional difference gel electrophoresis and MS proteome analysis were performed. Differential protein expression between non-LNM and LNM CRC was further validated in a tissue microarray, including 40 paraffin-embedded samples by immunohistochemistry staining. Moreover, a Boyden chamber assay, flow cytometry, and shRNA were used to examine the epithelial–mesenchymal transition and mechanism invasiveness of the differentially expressed proteins in DLD-1 cells and in vivo xenograft mouse model. Results: Eighteen differentially expressed proteins were found between non-LNM and LNM CRC tissues. Among them, protein levels of Gelsolin (GSN) and peroxiredoxin 4 (PRDX4) were abundant in node-positive CRC. Downregulation of GSN and PRDX4 markedly suppressed migration and invasiveness and also induced cell cycle G1/S arrest in DLD-1. Mechanistically, the EGFR/RhoA/PKCα/ERK pathways are critical for transcriptional activation of histone modification of H3 lysine 4 trimethylation (H3K4me3) of GSN and PRDX4 promoters, resulting in upregulation of GSN, PRDX4, Twist-1/2, cyclinD1, proliferating cell-nuclear antigen, β-catenin, N-cadherin, and matrix metalloprotein-9. Conclusions: GSN and PRDX4 are novel regulators in CRC lymph node metastasis to potentially provide new insights into the mechanism of CRC progression and serve as a biomarker for CRC diagnosis at the metastatic stage.

A model of multiple tumor marker for lymph node metastasis assessment in colorectal cancer: a retrospective study.

BackgroundAssessment of colorectal cancer (CRC) lymph node metastasis (LNM) is critical to the decision of surgery, prognosis, and therapy strategy. In this study, we aimed to develop and validate a multiple tumor marker nomogram for predicting LNM in CRC patients.MethodsA total of 674 patients who met the inclusion criteria were collected and randomly divided into primary cohort and internal test cohort at a ratio of 7:3. An external test cohort enrolled 178 CRC patients from the West China Hospital. Clinicopathologic variables were obtained from electronic medical records. The least absolute shrinkage and selection operator (LASSO) and interquartile range analysis were carried out for variable dimensionality reduction and feature selection. Multivariate logistic regression analysis was conducted to develop predictive models of LNM. The performance of the established models was evaluated by the receiver operating characteristic (ROC) curve, calibration belt, and clinical usefulness.ResultsBased on minimum criteria, 18 potential features were reduced to six predictors by LASSO and interquartile range in the primary cohort. The model demonstrated good discrimination and ROC curve (AUC = 0.721 in the internal test cohort, AUC = 0.758 in the external test cohort) in LNM assessment. Good calibration was shown for the probability of CRC LNM in the internal and external test cohorts. Decision curve analysis illustrated that multi-tumor markers nomogram was clinically useful.ConclusionsThe study proposed a reliable nomogram that could be efficiently and conveniently utilized to facilitate the assessment of individually-tailored LNM in patients with CRC, complementing imaging and biopsy tests.

Deep learning radiomics model related with genomics phenotypes for lymph node metastasis prediction in colorectal cancer

Background and purposeThe preoperative lymph node (LN) status is important for the treatment of colorectal cancer (CRC). Here, we established and validated a deep learning (DPL) model for predicting lymph node metastasis (LNM) in CRC. Materials and methodsA total of 423 CRC patients were divided into cohort 1 (training set, n = 238, testing set, n = 101) and cohort 2 (validation set, n = 84). Among them, 84 patients’ tumour tissues were collected for RNA sequencing. The DPL features were extracted from enhanced venous-phase computed tomography of CRC using an autoencoder. A DPL model was constructed with the least absolute shrinkage and selection operator algorithm. Carcinoembryonic antigen and carbohydrate antigen 19-9 were incorporated into the DPL model to construct a combined model. The model performance was assessed by receiver operating characteristic curves, calibration curves and decision curves. The correlations between DPL features, which have been selected, and genes were analysed by Spearman' correlation, and the genes correlated with DPL features were used to transcriptomic analysis. ResultsThe DPL model, integrated with 20 DPL features, showed a good discrimination performance in predicting the LNM, with areas under the curves (AUCs) of 0.79, 0.73 and 0.70 in the training set, testing set and validation set, respectively. The combined model had a better performance, with AUCs of 0.81, 0.77 and 0.73 in the three sets, respectively. Decision curve analysis confirmed the clinical application value of the DPL model and combined model. Furthermore, catabolic processes and immune-related pathways were identified and related with the selected DPL features. ConclusionThis study presented a DPL model and a combined model for LNM prediction. We explored the potential genomic phenotypes related with DPL features. In addition, the model could potentially be utilized to facilitate the individualized prediction of LNM in CRC.

Loss of ZNF677 expression is a predictive biomarker for lymph node metastasis in Middle Eastern Colorectal Cancer

Zinc-finger proteins are transcription factors with a “finger-like” domain that are widely involved in many biological processes. The zinc-finger protein 677 (ZNF677) belongs to the zinc-finger protein family. Previous reports have highlighted the tumor suppressive role of ZNF677 in thyroid and lung cancer. However, its role in colorectal cancer (CRC) has not been explored. ZNF677 protein expression was analyzed by immunohistochemistry in a large cohort of 1158 CRC patients. ZNF677 loss of expression was more frequent in CRC tissues (45.3%, 525/1158), when compared to that of normal tissue (5.1%, 11/214) (p < 0.0001) and was associated with mucinous histology (p = 0.0311), advanced pathological stage (p < 0.0001) and lymph node (LN) metastasis (p = 0.0374). Further analysis showed ZNF677 loss to be significantly enriched in LN metastatic CRC compared to overall cohort (p = 0.0258). More importantly, multivariate logistic regression analysis showed that ZNF677 loss is an independent predictor of LN metastasis in CRC (Odds ratio = 1.41; 95% confidence interval 1.05–1.87; p = 0.0203).The gain- and loss-of-function studies in CRC cell lines demonstrated that loss of ZNF677 protein expression prominently increased cell proliferation, progression of epithelial-mesenchymal transition and conferred chemoresistance, whereas its overexpression reversed the effect. In conclusion, loss of ZNF677 protein expression is common in Middle Eastern CRC and contributes to the prediction of biological aggressiveness of CRC. Therefore, ZNF677 could not only serve as a marker in predicting clinical prognosis in patient with CRC but also as a potential biomarker for personalized targeted therapy.

Clinicopathologic characteristics and survival of patients with double primary malignancies: breast and colorectal cancer.

PurposeThis study aimed to investigate the clinicopathologic features and survival in patients with both breast cancer (BrC) and colorectal cancer (CRC).MethodsBetween 1996 and 2019, patients who were diagnosed with both BrC and CRC were evaluated retrospectively. Patients with distant metastasis, palliative resection, and previous cancer histories except for BrCs or CRCs were excluded. Altogether, 105 patients were divided into the B=C group (n=21), B-first group (n=40), and C-first group (n=44) according to the definition of synchronous and metachronous cancers. The clinicopathologic features and overall survival were evaluated.ResultsTNM stages and histologic types were comparable among the 3 groups (P=0.434). The interval of diagnosis was 67.1±40.4 and 59.3±47.2 months in the B- and C-first groups, respectively. The incidence of adjuvant chemotherapy in the B-first group was 57.5%, which was higher than the B=C and C-first groups (P<0.001). The estrogen receptor, progesterone receptor, Ki-67, and HER-2 molecular markers were not significantly different among the groups. The overall survival of the B-first group showed lower survival rates than the C-first group (P=0.039). In the logistic regression, HER-2 status (hazard ratio [HR], 11.9; P=0.032) and lymph node metastasis of CRC (HR, 5.8; P=0.036) were prognostic factors affecting overall survival.ConclusionB-first group had poorer survival outcomes than the C-first group in patients with the metachronous BrC and CRC. HER2 positivity and CRC lymph node metastasis may be prognostic factors that affect overall survival in these patients. The findings support that a colorectal cancer screening program should be included during BrC surveillance.

Deep learning can predict lymph node status directly from histology in colorectal cancer

BackgroundLymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC). ObjectivesThe objective of the study is to investigate whether image features extracted by a deep learning model from routine histological slides and/or clinical data can be used to predict CRC lymph node metastasis (LNM). MethodsUsing histological whole slide images (WSIs) of primary tumours of 2431 patients in the DACHS cohort, we trained a convolutional neural network to predict LNM. In parallel, we used clinical data derived from the same cases in logistic regression analyses. Subsequently, the slide-based artificial intelligence predictor (SBAIP) score was included in the regression. WSIs and data from 582 patients of the TCGA cohort were used as the external test set. ResultsOn the internal test set, the SBAIP achieved an area under receiver operating characteristic (AUROC) of 71.0%, the clinical classifier achieved an AUROC of 67.0% and a combination of the two classifiers yielded an improvement to 74.1%. Whereas the clinical classifier's performance remained stable on the TCGA set, performance of the SBAIP dropped to an AUROC of 61.2%. Performance of the clinical classifier depended strongly on the T stage. ConclusionDeep learning–based image analysis may help predict LNM of patients with CRC using routine histological slides. Combination with clinical data such as T stage might be useful. Strategies to increase performance of the SBAIP on external images should be investigated.

FUBP1 promotes colorectal cancer stemness and metastasis via DVL1-mediated activation of Wnt/β-catenin signaling.

Distant metastasis is, unfortunately, the leading cause of death in colorectal cancer (CRC). Approximately 50% of CRC patients develop liver metastases, while 10–30% of patients develop pulmonary metastases. The occurrence of metastasis is considered to be almost exclusively driven by cancer stem cells (CSCs) formation. However, the key molecules that confer the transformation to stem cells in CRC, and subsequent metastasis, remain unclear. Far upstream element‐binding protein 1 (FUBP1), a transcriptional regulator of c‐Myc, was screened in CSCs of CRC by mass spectrometry and was examined by immunohistochemistry in a cohort of CRC tissues. FUBP1 was upregulated in 85% of KRAS‐mutant and 25% of wild‐type CRC patients. Further, whether in KRAS‐mutant or wild‐type patients, elevated FUBP1 was positively correlated with CRC lymph node metastasis and clinical stage, and negatively associated with overall survival. Overexpression of FUBP1 significantly enhanced CRC cell migration, invasion, tumor sphere formation, and CD133 and ALDH1 expression in vitro, and tumorigenicity in vivo. Mechanistically, FUBP1 promoted the initiation of CSCs by activating Wnt/β‐catenin signaling via directly binding to the promoter of DVL1, a potent activator of β‐catenin. Knockdown of DVL1 significantly inhibited the transformation to stem cells in, as well as the tumorigenicity of, CRC. Activation of Wnt/β‐catenin signaling by DVL1 increased pluripotent transcription factors, including c‐Myc, NANOG, and SOX2. Moreover, FUBP1 was upregulated at the post‐transcriptional level. Elevated FUBP1 levels in KRAS wild‐type CRC patients is due to the decrease in Smurf2, which promotes ubiquitin‐mediated degradation of FUBP1. In contrast, FUBP1 was upregulated in KRAS‐mutant patients through both inhibition of caspase 3‐dependent cleavage and decreased Smurf2. Our results demonstrate, for the first time, that FUBP1 is an oncogene, initiating the development of CSCs, as well as a new powerful endogenous Wnt‐signaling agonist that could provide an important prognostic factor and therapeutic target for metastasis in both KRAS‐mutant and wild‐type CRC.