The anatomy of the TNM for colon cancer

Identification and Validation of an Endoplasmic Reticulum Stress-Related lncRNA Signature for Colon Adenocarcinoma Patients

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Clinical and ultrasound features correlated with a heavy axillary nodal tumor burden in colon cancer

Aim: This study aimed to investigate the correlation between the pathologic and ultrasound (US) characteristics of colon cancer and the heavy axillary nodal burden. Methods: In total, 631 patients diagnosed with invasive colon cancer were recruited with ethical ratification. Results: The unitary pathologic features correlated with heavy axillary lymph nodal burden included the age of patient (p = 0.035), tumor size (p = 0.001), lymph node metastasis (p = 0.001), lymphovascular invasion (p = 0.020) and pathology type (p = 0.012). The independent US characteristics correlated with heavy axillary nodal burden included posterior acoustic enhancement (p = 0.006). Heavy axillary nodal burden was correlated with tumor size, lymph node metastasis, lymphovascular invasion and pathology type. Conclusion: Tumor size, lymph node metastasis and posterior acoustic can be used to predict the axillary lymph node tumor burden.

A New Oxaliplatin Resistance-Related Gene Signature With Strong Predicting Ability in Colon Cancer Identified by Comprehensive Profiling

Numerous colon cancer cases are resistant to chemotherapy based on oxaliplatin and suffer from relapse. A number of survival- and prognosis-related biomarkers have been identified based on database mining for patients who develop drug resistance, but the single individual gene biomarker cannot attain high specificity and sensitivity in prognosis prediction. This work was conducted aiming to establish a new gene signature using oxaliplatin resistance-related genes to predict the prognosis for colon cancer. To this end, we downloaded gene expression profile data of cell lines that are resistant and not resistant to oxaliplatin from the Gene Expression Omnibus (GEO) database. Altogether, 495 oxaliplatin resistance-related genes were searched by weighted gene co-expression network analysis (WGCNA) and differential expression analysis. As suggested by functional analysis, the above genes were mostly enriched into cell adhesion and immune processes. Besides, a signature was built based on four oxaliplatin resistance-related genes selected from the training set to predict the overall survival (OS) by stepwise regression and least absolute shrinkage and selection operator (LASSO) Cox analysis. Relative to the low risk score group, the high risk score group had dismal OS (P < 0.0001). Moreover, the area under the curve (AUC) value regarding the 5-year OS was 0.72, indicating that the risk score was accurate in the prediction of OS for colon cancer patients (AUC >0.7). Additionally, multivariate Cox regression suggested that the signature constructed based on four oxaliplatin resistance-related genes predicted the prognosis for colon cancer cases [hazard ratio (HR), 2.77; 95% CI, 2.03–3.78; P < 0.001]. Finally, external test sets were utilized to further validate the stability and accuracy of oxaliplatin resistance-related gene signature for prognosis of colon cancer patients. To sum up, this study establishes a signature based on four oxaliplatin resistance-related genes for predicting the survival of colon cancer patients, which sheds more light on the mechanisms of oxaliplatin resistance and helps identify colon cancer cases with a dismal prognostic outcome.

Identification of gene modules and hub genes in colon adenocarcinoma associated with pathological stage based on WGCNA analysis

Colorectal cancer (CRC) is one of the leading causes of cancer-related mortality in the world, in which colon adenocarcinoma (COAD) is the most common histological subtype of CRC. In this study, our aim is to identify gene modules and representative candidate biomarkers for clinical prognosis of patients with COAD, and help to predict prognosis and reveal the mechanisms of cancer progression. Weighted gene co-expression network analysis (WGCNA) was performed to construct a co-expression network and identify gene modules correlated with TNM clinical staging of COAD patients. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed with the module gene. Protein-protein interaction (PPI) network and hub gene identification were explored with Cytoscape software. Finally, the hub gene mRNA level was validated in Oncomine database. Five gene modules, related with the pathological TNM stage, were constructed, and the gene module was enriched in cell proliferation, invasion and migration related GO terms and metabolic related KEGG pathways. A total of top 10 hub genes was identified, and in which six of the hub genes show a significant up-regulation in COAD as compared to normal tissue, including IVL, KRT16, KRT6C, KRT6A, KRT78 and SBSN. In conclusion, we identified five gene modules and six candidate biomarkers correlated with the TNM staging of COAD patients. These findings may help us to understand the tumor progression of COAD and provide prognostic biomarkers as well as therapeutic targets.

Identification of key pathways and genes in colorectal cancer to predict the prognosis based on mRNA interaction network

The aim of the present study was to identify key genes in colorectal cancer (CRC) that could be used to reliably diagnose this disease and to explore the potential underlying mechanisms in silico. The gene expression profiles of primary human cancer datasets GSE21510 and GSE32323 were downloaded from the Gene Expression Omnibus database. The limma R software package was used to identify differentially expressed (DE) genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on DE genes using the Database for Annotation, Visualization and Integrated Discovery. The Search Tool for the Retrieval of Interacting Genes/Proteins database was used to construct a protein-protein interaction (PPI) network of the DE genes. Survival rate was analyzed and visualized using The Cancer Genome Atlas (TCGA). A total of 1,126 genes were significantly DE in the present study. All DE genes were enriched in KEGG pathways including 'cell cycle', 'mineral absorption', 'pancreatic secretion', 'pathways in cancer', 'metabolic pathways', 'aldosterone-regulated sodium reabsorption' and 'Wnt signaling pathway'. A total of 5 hub genes enriched in cell cycle and tumor-associated pathways, including E2F2, SKP2, MYC, CDKN1A and CDKN2B, were significantly DE and validated between tumor and normal tissues. CDKN1A and CDKN2B were identified within the PPI network using the Molecular Complex Detection algorithm. Survival and content distribution analyses of 362 clinical samples from TCGA revealed that CDKN1A effectively predicted the prognosis of patients. The present study identified key genes and potential signaling pathways involved in CRC. These findings may provide new insights for survival assessment during the clinical diagnosis of CRC.