The anatomy of the TNM for colon cancer

Henson, Donald E.; Hueman, Matthew T.; Chen, Dechang; Patel, Jigar A.; Wang, Huan; Schwartz, Arnold M.

doi:10.21037/jgo.2016.11.10

Cited by 6 publications

(4 citation statements)

References 11 publications

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“… 25 , 26 In addition, due to molecular heterogeneity, patients with the same TNM stage or similar risk factors may have different clinical outcomes. 27 , 28 Therefore we aimed to establish a risk-scoring model for the early diagnosis of COAD.…”

Section: Discussionmentioning

confidence: 99%

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

Chen¹,

Gao²,

Xiang³

et al. 2022

IJGM

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Purpose Endoplasmic reticulum (ER) stress has a significant effect on cancer cells. Increasing numbers of studies indicate that long non-coding RNAs (lncRNAs) promote the development of colon adenocarcinoma (COAD), but the relationship between ER stress-related lncRNAs and the prognosis of COAD remains unclear. The aim of the current study was to construct and validate an ER stress-related lncRNA prognostic signature to predict COAD prognoses. Methods Gene expression data and clinical information from the Cancer Genome Atlas and the Gene Expression Omnibus with COAD were downloaded and analyzed. Cox regression and least absolute shrinkage and selection operator regression were then used to develop an ER stress-related lncRNA signature. COAD patients were then divided into high-risk and low-risk groups based on the median risk score to analyze prognoses. Tumor mutation burdens (TMBs) and the differences in copy number variations (CNVs) between the two groups were also analyzed. Lastly, gene set enrichment analysis (GSEA) was used to explore the enrichment pathways and biological processes associated with differentially expressed genes in the high-risk and low-risk groups, and lncRNA expression in the model was validated via quantitative real-time PCR in colon cancer and paracancerous tissues. Results A signature including 8 ER stress-related lncRNAs was constructed. COAD prognoses were significantly poorer in the high-risk group than in the low-risk group. There were few differences in TMBs and CNVs between the two groups. In GSEA analysis, in the high-risk group highly expressed genes associated with extracellular matrix pathways were significantly enriched. Conclusion The 8-ER stress-related lncRNA derived from the present study is a potential indicator of COAD prognosis.

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Section: Discussionmentioning

confidence: 99%

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

Chen¹,

Gao²,

Xiang³

et al. 2022

IJGM

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“…to the first treatment, but many of them may experience resistance to oxaliplatin and subsequently develop recurrence, resulting in a dismal prognostic outcome (31,32). Currently, the Tumor, Lymph node, Metastasis (TNM) classification system is widely utilized as the standard to predict relapse among colon cancer cases (33). However, it cannot achieve satisfactory performance in predicting prognosis and managing colon cancer.…”

Section: Discussionmentioning

confidence: 99%

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

2021

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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.

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“…The database here were based upon data generated by the TCGA Research Network (). The expression levels of CDKN1A and CDKN2B were also determined at different clinical stages according to the Tumor-Node-Metastasis staging system (20) in CRC. Expression level analysis was performed using a one-way ANOVA, using the pathological stage as variable for calculating differential expression.…”

Section: Methodsmentioning

confidence: 99%

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

Zhu

Li³

et al. 2019

Oncol Lett

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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.

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The anatomy of the TNM for colon cancer

Cited by 6 publications

References 11 publications

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

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

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

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

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