scholarly journals Identification of MicroRNA 15b-3p as a Diagnostic Marker for Early Stage of Colorectal Cancer Through Comprehensive RNA Analysis

2021 ◽  
Author(s):  
RYOICHI TSUKAMOTO ◽  
MASAKI HOSOYA ◽  
MIDORI FUKAYA ◽  
NORIHIKO YOKOYAMA ◽  
SHINGO KAWANO ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Diagnostic Marker ◽  
Early Stage ◽  
Rna Analysis

scholarly journals Novel genomic classifier for early stage colorectal cancer patients

2018 ◽  
Vol 29 ◽  
pp. viii33-viii34
Author(s):  
E. Letellier ◽  
M. Schmitz ◽  
A. Ginolhac ◽  
E. Koncina ◽  
M. Marchese ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Patients ◽  
Early Stage ◽  
Colorectal Cancer Patients

scholarly journals A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2762
Author(s):  
Samantha Di Donato ◽  
Alessia Vignoli ◽  
Chiara Biagioni ◽  
Luca Malorni ◽  
Elena Mori ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Stratification ◽  
Elderly Patients ◽  
Metastatic Colorectal Cancer ◽  
Early Stage ◽  
Prospective Trial ◽  
Proton Nuclear Magnetic Resonance ◽  
P Value ◽  
Resonance Spectroscopy ◽  
Metabolomic Fingerprinting

Adjuvant treatment for patients with early stage colorectal cancer (eCRC) is currently based on suboptimal risk stratification, especially for elderly patients. Metabolomics may improve the identification of patients with residual micrometastases after surgery. In this retrospective study, we hypothesized that metabolomic fingerprinting could improve risk stratification in patients with eCRC. Serum samples obtained after surgery from 94 elderly patients with eCRC (65 relapse free and 29 relapsed, after 5-years median follow up), and from 75 elderly patients with metastatic colorectal cancer (mCRC) obtained before a new line of chemotherapy, were retrospectively analyzed via proton nuclear magnetic resonance spectroscopy. The prognostic role of metabolomics in patients with eCRC was assessed using Kaplan–Meier curves. PCA-CA-kNN could discriminate the metabolomic fingerprint of patients with relapse-free eCRC and mCRC (70.0% accuracy using NOESY spectra). This model was used to classify the samples of patients with relapsed eCRC: 69% of eCRC patients with relapse were predicted as metastatic. The metabolomic classification was strongly associated with prognosis (p-value 0.0005, HR 3.64), independently of tumor stage. In conclusion, metabolomics could be an innovative tool to refine risk stratification in elderly patients with eCRC. Based on these results, a prospective trial aimed at improving risk stratification by metabolomic fingerprinting (LIBIMET) is ongoing.

Serum Macrophage Inhibitory Cytokine-1 Serves as a Novel Diagnostic Biomarker of Early-Stage Colorectal Cancer

Biomarkers ◽  
2021 ◽  
pp. 1-21
Author(s):  
Chunyang Dai ◽  
Xiaolei Zhang ◽  
Yanling Ma ◽  
Zhaowu Chen ◽  
Shaohua Chen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Early Stage ◽  
Diagnostic Biomarker

scholarly journals New polyp image classification technique using transfer learning of network-in-network structure in endoscopic images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Young Jae Kim ◽  
Jang Pyo Bae ◽  
Jun-Won Chung ◽  
Dong Kyun Park ◽  
Kwang Gi Kim ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Transfer Learning ◽  
Test Data ◽  
State Of The Art ◽  
Early Stage ◽  
Statistical Significance ◽  
Recall Rate ◽  
Training Data ◽  
Fine Tuning ◽  
Accuracy Evaluation

AbstractWhile colorectal cancer is known to occur in the gastrointestinal tract. It is the third most common form of cancer of 27 major types of cancer in South Korea and worldwide. Colorectal polyps are known to increase the potential of developing colorectal cancer. Detected polyps need to be resected to reduce the risk of developing cancer. This research improved the performance of polyp classification through the fine-tuning of Network-in-Network (NIN) after applying a pre-trained model of the ImageNet database. Random shuffling is performed 20 times on 1000 colonoscopy images. Each set of data are divided into 800 images of training data and 200 images of test data. An accuracy evaluation is performed on 200 images of test data in 20 experiments. Three compared methods were constructed from AlexNet by transferring the weights trained by three different state-of-the-art databases. A normal AlexNet based method without transfer learning was also compared. The accuracy of the proposed method was higher in statistical significance than the accuracy of four other state-of-the-art methods, and showed an 18.9% improvement over the normal AlexNet based method. The area under the curve was approximately 0.930 ± 0.020, and the recall rate was 0.929 ± 0.029. An automatic algorithm can assist endoscopists in identifying polyps that are adenomatous by considering a high recall rate and accuracy. This system can enable the timely resection of polyps at an early stage.

scholarly journals Prevalence of vitamin D deficiency in Chinese patients with early stage colorectal cancer

2018 ◽  
Vol 29 ◽  
pp. ix41
Author(s):  
D. Ng ◽  
R. Tan ◽  
R. Sultana ◽  
M. Ang ◽  
W. Lim ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Vitamin D ◽  
Vitamin D Deficiency ◽  
Early Stage ◽  
Chinese Patients

scholarly journals Machine learning identifies two autophagy-related genes as markers of recurrence in colorectal cancer

2020 ◽  
Vol 48 (10) ◽  
pp. 030006052095880
Author(s):  
Jianping Wu ◽  
Sulai Liu ◽  
Xiaoming Chen ◽  
Hongfei Xu ◽  
Yaoping Tang
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Protein Interactions ◽  
Early Stage ◽  
Predictive Ability ◽  
Improve Patient Care ◽  
Active Role ◽  
Risk Groups ◽  
Recurrence Risk ◽  
Machine Learning Algorithms

Objective Colorectal cancer (CRC) is the most common cancer worldwide. Patient outcomes following recurrence of CRC are very poor. Therefore, identifying the risk of CRC recurrence at an early stage would improve patient care. Accumulating evidence shows that autophagy plays an active role in tumorigenesis, recurrence, and metastasis. Methods We used machine learning algorithms and two regression models, univariable Cox proportion and least absolute shrinkage and selection operator (LASSO), to identify 26 autophagy-related genes (ARGs) related to CRC recurrence. Results By functional annotation, these ARGs were shown to be enriched in necroptosis and apoptosis pathways. Protein–protein interactions identified SQSTM1, CASP8, HSP80AB1, FADD, and MAPK9 as core genes in CRC autophagy. Of 26 ARGs, BAX and PARP1 were regarded as having the most significant predictive ability of CRC recurrence, with prediction accuracy of 71.1%. Conclusion These results shed light on prediction of CRC recurrence by ARGs. Stratification of patients into recurrence risk groups by testing ARGs would be a valuable tool for early detection of CRC recurrence.

407 A novel biomarker panel for prediction of recurrence in early stage colorectal cancer among orientals

2015 ◽  
Vol 51 ◽  
pp. S81
Author(s):  
T.H. Wang ◽  
P.C. Lin ◽  
Y.P. Lee ◽  
B.W. Lin ◽  
P.F. Kuo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Early Stage ◽  
Biomarker Panel

scholarly journals 250 CADM1 is a diagnostic marker in early-stage mycosis fungoides

2017 ◽  
Vol 137 (5) ◽  
pp. S43
Author(s):  
A. Yuki ◽  
R. Abe ◽  
H. Fujikawa ◽  
R. Hayashi ◽  
E. Homma ◽  
...  
Keyword(s):  
Mycosis Fungoides ◽  
Diagnostic Marker ◽  
Early Stage

Tumor-Infiltrating FOXP3+ T Regulatory Cells Show Strong Prognostic Significance in Colorectal Cancer

2009 ◽  
Vol 27 (2) ◽  
pp. 186-192 ◽  
Author(s):  
Paul Salama ◽  
Michael Phillips ◽  
Fabienne Grieu ◽  
Melinda Morris ◽  
Nik Zeps ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colonic Mucosa ◽  
Tumor Tissue ◽  
Early Stage ◽  
Prognostic Significance ◽  
High Density ◽  
Prognostic Indicators ◽  
Normal Colonic Mucosa ◽  
Solid Cancer ◽  
Histopathologic Features

Purpose To determine the prognostic significance of FOXP3+ lymphocyte (Treg) density in colorectal cancer compared with conventional histopathologic features and with CD8+ and CD45RO+ lymphocyte densities. Patients and Methods Tissue microarrays and immunohistochemistry were used to assess the densities of CD8+, CD45RO+, and FOXP3+ lymphocytes in tumor tissue and normal colonic mucosa from 967 stage II and stage III colorectal cancers. These were evaluated for associations with histopathologic features and patient survival. Results FOXP3+ Treg density was higher in tumor tissue compared with normal colonic mucosa, whereas CD8+ and CD45RO+ cell densities were lower. FOXP3+ Tregs were not associated with any histopathologic features, with the exception of tumor stage. Multivariate analysis showed that stage, vascular invasion, and FOXP3+ Treg density in normal and tumor tissue were independent prognostic indicators, but not CD8+ and CD45RO+. High FOXP3+ Treg density in normal mucosa was associated with worse prognosis (hazard ratio [HR] = 1.51; 95% CI, 1.07 to 2.13; P = .019). In contrast, a high density of FOXP3+ Tregs in tumor tissue was associated with improved survival (HR = 0.54; 95% CI, 0.38 to 0.77; P = .001). Conclusion FOXP3+ Treg density in normal and tumor tissue had stronger prognostic significance in colorectal cancer compared with CD8+ and CD45RO+ lymphocytes. The finding of improved survival associated with a high density of tumor-infiltrating FOXP3+ Tregs in colorectal cancer contrasts with several other solid cancer types. The inclusion of FOXP3+ Treg density may help to improve the prognostication of early-stage colorectal cancer.

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