scholarly journals Association of perioperative serum carcinoembryonic antigen level and recurrence in low-risk stage IIA colon cancer

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252566
Author(s):  
Han-Gil Kim ◽  
Seung Yoon Yang ◽  
Yoon Dae Han ◽  
Min Soo Cho ◽  
Byung Soh Min ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Cancer Recurrence ◽  
Colon Adenocarcinoma ◽  
Low Risk ◽  
Survival Group ◽  
Serum Carcinoembryonic Antigen ◽  
Before And After ◽  
Serum Cea ◽  
Colon Cancer Recurrence

Background The purpose is to investigate prognosis according to serum CEA levels before and after surgery in patients with stage IIA colon cancer who do not show high-risk features. Methods Among the patients diagnosed with colon adenocarcinoma between April 2011 and December 2017, 462 patients were confirmed as low-risk stage IIA after surgery and enrolled. The ROC curve was used to determine cut-off values of pre- and postoperative CEA. Patients were classified into three groups using these new cut-off values. Results All recurrence occurred in 52 of 463 patients (11.2%). However, recurrence in group H was 15.9%, which was slightly higher than the other two groups (P = 0.04). Group L and M showed 10.5% and 12.8% overall survival, group H was higher at 21.0% (P = 0.005). Recurrence was the only risk factor in group H was significantly higher in group L (HR 2.008, 95% CI, 1.123–3.589, P = 0.019). Mortality was similar to recurrence (HR 1.975, 95% CI 1.091–3.523, P = 0.044). Conclusion Among patients with low-risk stage IIA colon cancer, recurrence and mortality rates were higher when perioperative serum CEA levels were above a certain level. Therefore, high CEA level should be considered a high-risk feature and adjuvant chemotherapy should be performed.

scholarly journals Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

2020 ◽  
Author(s):  
Hui Li ◽  
Hao Zeng ◽  
Linyan Chen ◽  
Qimeng Liao ◽  
Jianrui Ji ◽  
...  
Keyword(s):  
High Risk ◽  
Colon Adenocarcinoma ◽  
Genomic Data ◽  
Low Risk ◽  
Support Vector ◽  
Training Set ◽  
Survival Group ◽  
Risk Patients ◽  
Histopathological Images ◽  
Testing Set

Abstract Background: Colon adenocarcinoma (COAD) is one of the highest morbidity cancers all over the world. Its 5-year survival is no more than 60% even in European countries with the highest survival rates. The histopathological information is crucial for the prognosis and therapy of COAD. Application of the digital whole slide imaging system enables us to read histopathological sections digitally. Apart from that, cancer genomics is also an important prognostic factor.Methods: To identify prognosis biomarkers of COAD, we downloaded whole-slide histopathological images from TCIA database. After processing these images, histopathological features were extracted by CellProfiler. Least Absolute Shrinkage and Selection Operator and Support Vector Machine Recursive Feature Elimination were followed applied, screening out 5 prognosis-related features. Weighted gene co-expression network analysis (WGCNA) was operated to find co-expression gene module correlated with prognosis-related features. The samples were divided into a training set and a testing set on a scale of 70% and 30%. Random forest was applied to construct histopathologic-genomic prognosis factor (HGPF) using prognosis-related features and genomic data. After that, we combined HGPF and clinical characteristics with nomogram and verify its predictive efficacy.Results: The time-dependent ROC was drawn to evaluate the efficacy of prognostic model. In the training set, 1-year, 3-year and 5-year AUCs are respectively 0.948, 0.916, 0.933. In the testing set, 1-year, 3-year and 5-year AUCs are respectively 0.913, 0.894, 0.924. In addition, patients were separated into high-risk survival group and low-risk survival group by HGPF. Survival analysis indicates that the low-risk patients’ survival was significantly better than high-risk patients’ in both training set and testing set. It is suggested that histopathological image features have certain ability to predict COAD survival, which can be further improved by means of multi-omics combination.Conclusions: In conclusion, this study constructs an integrative prognosis model based on histopathological and genomic features, which may have some guidance effect on prognosis and clinical decision of COAD patients. Furthermore, the underlying biological mechanisms of this multi-omics model require further study.

scholarly journals A Prediction Model for Metachronous Peritoneal Carcinomatosis in Patients with Stage T4 Colon Cancer after Curative Resection

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2808
Author(s):  
Tzong-Yun Tsai ◽  
Jeng-Fu You ◽  
Yu-Jen Hsu ◽  
Jing-Rong Jhuang ◽  
Yih-Jong Chern ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Prediction Model ◽  
Prediction Error ◽  
Area Under The Curve ◽  
Low Risk ◽  
Brier Score ◽  
Individual Risk ◽  
T4 Colon Cancer ◽  
Testing Set

(1) Background: The aim of this study was to develop a prediction model for assessing individual mPC risk in patients with pT4 colon cancer. Methods: A total of 2003 patients with pT4 colon cancer undergoing R0 resection were categorized into the training or testing set. Based on the training set, 2044 Cox prediction models were developed. Next, models with the maximal C-index and minimal prediction error were selected. The final model was then validated based on the testing set using a time-dependent area under the curve and Brier score, and a scoring system was developed. Patients were stratified into the high- or low-risk group by their risk score, with the cut-off points determined by a classification and regression tree (CART). (2) Results: The five candidate predictors were tumor location, preoperative carcinoembryonic antigen value, histologic type, T stage and nodal stage. Based on the CART, patients were categorized into the low-risk or high-risk groups. The model has high predictive accuracy (prediction error ≤5%) and good discrimination ability (area under the curve >0.7). (3) Conclusions: The prediction model quantifies individual risk and is feasible for selecting patients with pT4 colon cancer who are at high risk of developing mPC.

scholarly journals Accurate population-based model for individual prediction of colon cancer recurrence

2021 ◽  
pp. 1-9
Author(s):  
E. Osterman ◽  
J. Ekström ◽  
T. Sjöblom ◽  
H. Kørner ◽  
T. Å. Myklebust ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Recurrence ◽  
Population Based ◽  
Colon Cancer Recurrence

scholarly journals A CASE OF ASCENDING COLON CANCER RECURRENCE WITH INTRALUMINAL URETERAL DISSEMINATION MIMICKING PRIMARY URETERAL CANCER, DETECTED DURING INVESTIGATION FOR FEVER

2015 ◽  
Vol 106 (4) ◽  
pp. 264-268
Author(s):  
Ryuichi Nishiyama ◽  
Masashi Kubota ◽  
Toru Kanno ◽  
Takashi Okada ◽  
Yoshihito Higashi ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Recurrence ◽  
Ascending Colon ◽  
Ascending Colon Cancer ◽  
Colon Cancer Recurrence

scholarly journals TWO CASES OF COLON CANCER RECURRENCE AT THE HAND-SEWN ANASTOMOSIS SITE AFTER COLECTOMY

2011 ◽  
Vol 72 (3) ◽  
pp. 732-736
Author(s):  
Chihiro NAKAHARA ◽  
Kumi SUYAMA ◽  
Toshimitsu IWASHITA ◽  
Satoshi TOYOSHIMA
Keyword(s):  
Colon Cancer ◽  
Cancer Recurrence ◽  
Anastomosis Site ◽  
Colon Cancer Recurrence

scholarly journals Establishment of an immune-related gene pairs model to predict colon adenocarcinoma prognosis

2020 ◽  
Author(s):  
Jihang Luo ◽  
Puyu Liu ◽  
Leibo Wang ◽  
Yi Huang ◽  
Yuanyan Wang ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Risk Group ◽  
Colon Adenocarcinoma ◽  
Risk Groups ◽  
High Risk Group ◽  
Related Gene ◽  
Immune Genes ◽  
Gene Pairs ◽  
Immune Related Gene

Abstract Background Colon cancer is the most common type of gastrointestinal cancer and has high morbidity and mortality. Colon adenocarcinoma(COAD) is the main pathological type of colon cancer. There is a lot of evidence describing the correlation between the prognosis of COAD and the immune system. The objective of the current study was the development of a robust prognostic immune-related gene pairs (IRGPs) model for estimating overall survival of COAD. Methods The gene expression profiles and clinical information of patients with colon adenocarcinoma come from TCGA and GEO databases and are divided into training and validation cohorts. Immune genes were selected which show significantly association with prognosis. Results Among 1647 immune genes, a 17 IRGPs model was built which was significantly associated with OS in the training cohort. In the training and validation data set, the IRGPs model divided patients into high-risk groups and low-risk groups, and the prognosis of the high-risk group was significantly worse( P <0.001). Univariate and multivariate Cox proportional hazard analysis confirmed the feasibility of this model. Functional analysis confirmed that multiple tumor progression and stem cell growth-related pathways in high-risk groups were up-regulated. T cells regulatory and Macrophage M0 were significantly highly expressed in the high-risk group. Conclusion We successfully constructed an IRGPs model that can predict the prognosis of COAD, which provides new insights into the treatment strategy of COAD.

scholarly journals An immune-related lncRNA signature as a prognostic immunotherapy target for colon cancer

2020 ◽  
Author(s):  
Bin Wu ◽  
Yi Yao ◽  
Yi Dong ◽  
Si Qi Yang ◽  
Deng Jing Zhou ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Risk Score ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Gene Set Enrichment ◽  
Immunotherapy Target

Abstract Background：We aimed to investigate an immune-related long non-coding RNA (lncRNA) signature that may be exploited as a potential immunotherapy target in colon cancer. Materials and methods: Colon cancer samples from The Cancer Genome Atlas (TCGA) containing available clinical information and complete genomic mRNA expression data were used in our study. We then constructed immune-related lncRNA co-expression networks to identify the most promising immune-related lncRNAs. According to the risk score developed from screened immune-related lncRNAs, the high-risk and low-risk groups were separated on the basis of the median risk score, which served as the cutoff value. An overall survival analysis was then performed to confirm that the risk score developed from screened immune-related lncRNAs could predict colon cancer prognosis. The prediction reliability was further evaluated in the independent prognostic analysis and receiver operating characteristic curve (ROC). A principal component analysis (PCA) and gene set enrichment analysis (GSEA) were performed for functional annotation. Results: Information for a total of 514 patients was included in our study. After multiplex analysis, 12 immune-related lncRNAs were confirmed as a signature to evaluate the risk scores for each patient with cancer. Patients in the low-risk group exhibited a longer overall survival (OS) than those in the high-risk group. Additionally, the risk scores were an independent factor, and the Area Under Curve (AUC) of ROC for accuracy prediction was 0.726. Moreover, the low-risk and high-risk groups displayed different immune statuses based on principal components and gene set enrichment analysis.Conclusions: Our study suggested that the signature consisting of 12 immune-related lncRNAs can provide an accessible approach to measuring the prognosis of colon cancer and may serve as a valuable antitumor immunotherapy.

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