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

ABSTRACTBackgroundAberrant methylation of DNA acts epigenetically to skew the gene transcription rate up or down. In this study, we have developed a comprehensive computational framework for the stage-differentiated modelling of DNA methylation landscapes in colorectal cancer. Methods: The methylation β - matrix was derived from the public-domain TCGA data, converted into M-value matrix, annotated with sample stages, and analysed for stage-salient genes using multiple approaches involving stage-differentiated linear modelling of methylation patterns and/or expression patterns. Differentially methylated genes (DMGs) were identified using a contrast against control samples (adjusted p-value <0.001 and |log fold-change of M-value| >2). These results were filtered using a series of all possible pairwise stage contrasts (p-value <0.05) to obtain stage-salient DMGs. These were then subjected to a consensus analysis, followed by Kaplan–Meier survival analysis to explore the relationship between methylation and prognosis for the consensus stage-salient biomarkers.ResultsWe found significant genome-wide changes in methylation patterns in cancer samples relative to controls agnostic of stage. Our stage-differentiated analysis yielded the following stage-salient genes: one stage-I gene (FBN1), one stage-II gene (FOXG1), one stage-III gene (HCN1) and four stage-IV genes (NELL1, ZNF135, FAM123A, LAMA1). All the biomarkers were hypermethylated, indicating down-regulation and signifying a CpG island Methylator Phenotype (CIMP) manifestation. A prognostic signature consisting of FBN1 and FOXG1was significantly associated with patient survival (p-value < 0.01) and could be used as a biomarker panel for early-stage CRC prognosis.ConclusionOur workflow for stage-differentiated consensus analysis has yielded stage-salient diagnostic biomarkers as well as an early-stage prognostic biomarker panel. In addition, our studies have affirmed a novel CIMP-like signature in colorectal cancer, urging clinical validation.

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Novel genomic classifier for early stage colorectal cancer patients

Annals of Oncology ◽

10.1093/annonc/mdy269.105 ◽

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

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

Cancers ◽

10.3390/cancers13112762 ◽

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.

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Automated Assay of a Four-Protein Biomarker Panel for Improved Detection of Ovarian Cancer

Cancers ◽

10.3390/cancers13020325 ◽

2021 ◽

Vol 13 (2) ◽

pp. 325

Author(s):

Christopher Walker ◽

Tuan-Minh Nguyen ◽

Shlomit Jessel ◽

Ayesha B. Alvero ◽

Dan-Arin Silasi ◽

...

Keyword(s):

Ovarian Cancer ◽

Early Detection ◽

Predictive Value ◽

Early Stage ◽

Ca 125 ◽

Healthy Controls ◽

Classification Models ◽

Serum Samples ◽

Protein Biomarker ◽

Biomarker Panel

Background: Mortality from ovarian cancer remains high due to the lack of methods for early detection. The difficulty lies in the low prevalence of the disease necessitating a significantly high specificity and positive-predictive value (PPV) to avoid unneeded and invasive intervention. Currently, cancer antigen- 125 (CA-125) is the most commonly used biomarker for the early detection of ovarian cancer. In this study we determine the value of combining macrophage migration inhibitory factor (MIF), osteopontin (OPN), and prolactin (PROL) with CA-125 in the detection of ovarian cancer serum samples from healthy controls. Materials and Methods: A total of 432 serum samples were included in this study. 153 samples were from ovarian cancer patients and 279 samples were from age-matched healthy controls. The four proteins were quantified using a fully automated, multi-analyte immunoassay. The serum samples were divided into training and testing datasets and analyzed using four classification models to calculate accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results: The four-protein biomarker panel yielded an average accuracy of 91% compared to 85% using CA-125 alone across four classification models (p = 3.224 × 10−9). Further, in our cohort, the four-protein biomarker panel demonstrated a higher sensitivity (median of 76%), specificity (median of 98%), PPV (median of 91.5%), and NPV (median of 92%), compared to CA-125 alone. The performance of the four-protein biomarker remained better than CA-125 alone even in experiments comparing early stage (Stage I and Stage II) ovarian cancer to healthy controls. Conclusions: Combining MIF, OPN, PROL, and CA-125 can better differentiate ovarian cancer from healthy controls compared to CA-125 alone.

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

Biomarkers ◽

10.1080/1354750x.2021.1950209 ◽

2021 ◽

pp. 1-21

Author(s):

Chunyang Dai ◽

Xiaolei Zhang ◽

Yanling Ma ◽

Zhaowu Chen ◽

Shaohua Chen ◽

...

Keyword(s):

Colorectal Cancer ◽

Early Stage ◽

Diagnostic Biomarker

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

Scientific Reports ◽

10.1038/s41598-021-83199-9 ◽

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.

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

Annals of Oncology ◽

10.1093/annonc/mdy431.042 ◽

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

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

Journal of International Medical Research ◽

10.1177/0300060520958808 ◽

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.

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