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Development and validation of a lipogenic genes panel for diagnosis and recurrence of colorectal cancer.

ABSTRACT: BACKGROUND & AIM:Accumulated evidence indicates that the elevation of lipid metabolism is an essential step in colorectal cancer (CRC) development, and analysis of the key lipogenic mediators may lead to identifying the new clinically useful prognostic gene signatures. METHODS:The expression pattern of 61 lipogenic genes was assessed between CRC tumors and matched adjacent normal tissues in a training set (n = 257) with the Mann-Whitney U test. Cox's proportional hazards model and the Kaplan-Meier method were used to identifying a lipogenic-biomarkers signature associated with the prognosis of CRC. The biomarkers signature was then confirmed in two independent validation groups, including a set of 223 CRC samples and an additional set of 203 COAD profiles retrieving from the Cancer Genome Atlas (TCGA). RESULTS:Five genes, including ACOT8, ACSL5, FASN, HMGCS2, and SCD1, were significantly enhanced in CRC tumors. Using the cutoff value 0.493, the samples were classified into high risk and low risk. The AUC of panel for discriminating of all, early (I-II stages), and advanced CRC (III-IV stages) were 0.8922, 0.8446, and 0.9162 (Training set), along with 0.8800, 0.8205, and 0.7351 (validation set I), and 0.9071, 0.8946, and 0.9107 (Validation set II), respectively. There was a reverse correlation between the high predicted point of panel and worse OS of CRC patients in training set (HR (95% CI): 0.1096 (0.07089-0.1694), P < 0.001), validation set I (HR (95% CI): 0.3350 (0.2116-0.5304), P < 0.001), and validation set II (HR (95% CI): 0.1568 (0.1090-0.2257), P < 0.001). CONCLUSION:Our study showed that the panel of ACOT8/ACSL5/FASN/HMGBCS2/SCD1 genes had a better prognostic performance than validated clinical risk scales and is applicable for early detection of CRC and tumor recurrence.

SUBMITTER: Gharib E

PROVIDER: S-EPMC7064220 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Development and validation of a lipogenic genes panel for diagnosis and recurrence of colorectal cancer.

Gharib Ehsan E Nasrinasrabadi Parinaz P Zali Mohammad Reza MR

PloS one 20200310 3

<h4>Background & aim</h4>Accumulated evidence indicates that the elevation of lipid metabolism is an essential step in colorectal cancer (CRC) development, and analysis of the key lipogenic mediators may lead to identifying the new clinically useful prognostic gene signatures.<h4>Methods</h4>The expression pattern of 61 lipogenic genes was assessed between CRC tumors and matched adjacent normal tissues in a training set (n = 257) with the Mann-Whitney U test. Cox's proportional hazards model and ...[more]

PMID: 32155177

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Project description:BackgroundAmong men and women diagnosed with colorectal cancer (CRC), 20-50% will develop a cancer recurrence. Cancer recurrences are not routinely captured by most population-based registries; however, linkage across Danish registries allows for the development of predictive models to detect recurrence. Successful application of such models in population-based settings requires validation against a gold standard to ensure the accuracy of recurrence identification.ObjectiveWe apply a recently developed validation study design for prospectively collected validation data to validate predicted CRC recurrences against gold standard diagnoses from medical records in an actively followed cohort of CRC patients in Denmark.MethodsWe use a Bayesian monitoring framework, traditionally used in clinical trials, to iteratively update classification parameters (positive and negative predictive values, and sensitivity and specificity) in an adaptive validation substudy design. This design allows determination of the sample size necessary to estimate the corresponding parameters and to identify when validation efforts can cease based on predefined criteria for parameter values and levels of precision.ResultsAmong 355 men and women diagnosed with CRC in Denmark and actively followed semi-annually, there were 63 recurrences diagnosed by active follow-up and 70 recurrences identified by a predictive algorithm. The adaptive validation design met stopping criteria for the classification parameters after 120 patients had their recurrence information validated. This stopping point yielded parameter estimates for the classification parameters similar to those obtained when the entire cohort was validated, with 66% less patients needed for the validation study.ConclusionIn this proof of concept application of the adaptive validation study design for outcome misclassification, we demonstrated the ability of the method to accurately determine when sufficient validation data have been collected. This method serves as a novel validation substudy design for prospectively collected data with simultaneous implementation of a validation study.

Project description:Background:One of the most significant challenges in colorectal cancer (CRC) management is the use of compliant early stage population-based diagnostic tests as adjuncts to confirmatory colonoscopy. Despite the near curative nature of early clinical stage surgical resection, mortality remains unacceptably high-as the majority of patients diagnosed by faecal haemoglobin followed by colonoscopy occur at latter stages. Additionally, current population-based screens reliant on fecal occult blood test (FOBT) have low compliance (~ 40%) and tests suffer low sensitivities. Therefore, blood-based diagnostic tests offer survival benefits from their higher compliance (≥ 97%), if they can at least match the sensitivity and specificity of FOBTs. However, discovery of low abundance plasma biomarkers is difficult due to occupancy of a high percentage of proteomic discovery space by many high abundance plasma proteins (e.g., human serum albumin). Methods:A combination of high abundance protein ultradepletion (e.g., MARS-14 and an in-house IgY depletion columns) strategies, extensive peptide fractionation methods (SCX, SAX, High pH and SEC) and SWATH-MS were utilized to uncover protein biomarkers from a cohort of 100 plasma samples (i.e., pools of 20 healthy and 20 stages I-IV CRC plasmas). The differentially expressed proteins were analyzed using ANOVA and pairwise t-tests (p < 0.05; fold-change > 1.5), and further examined with a neural network classification method using in silico augmented 5000 patient datasets. Results:Ultradepletion combined with peptide fractionation allowed for the identification of a total of 513 plasma proteins, 8 of which had not been previously reported in human plasma (based on PeptideAtlas database). SWATH-MS analysis revealed 37 protein biomarker candidates that exhibited differential expression across CRC stages compared to healthy controls. Of those, 7 candidates (CST3, GPX3, CFD, MRC1, COMP, PON1 and ADAMDEC1) were validated using Western blotting and/or ELISA. The neural network classification narrowed down candidate biomarkers to 5 proteins (SAA2, APCS, APOA4, F2 and AMBP) that had maintained accuracy which could discern early (I/II) from late (III/IV) stage CRC. Conclusion:MS-based proteomics in combination with ultradepletion strategies have an immense potential of identifying diagnostic protein biosignature.

Project description:BackgroundHeterogeneity of prostate cancer (PCa) contributes to inaccurate cancer screening and diagnosis, unnecessary biopsies, and overtreatment. We intended to develop non-invasive urine tests for accurate PCa diagnosis to avoid unnecessary biopsies.MethodsUsing a machine learning program, we identified a 25-Gene Panel classifier for distinguishing PCa and benign prostate. A non-invasive test using pre-biopsy urine samples collected without digital rectal examination (DRE) was used to measure gene expression of the panel using cDNA preamplification followed by real-time qRT-PCR. The 25-Gene Panel urine test was validated in independent multi-center retrospective and prospective studies. The diagnostic performance of the test was assessed against the pathological diagnosis from biopsy by discriminant analysis. Uni- and multivariate logistic regression analysis was performed to assess its diagnostic improvement over PSA and risk factors. In addition, the 25-Gene Panel urine test was used to identify clinically significant PCa. Furthermore, the 25-Gene Panel urine test was assessed in a subset of patients to examine if cancer was detected after prostatectomy.ResultsThe 25-Gene Panel urine test accurately detected cancer and benign prostate with AUC of 0.946 (95% CI 0.963-0.929) in the retrospective cohort (n?=?614), AUC of 0.901 (0.929-0.873) in the prospective cohort (n?=?396), and AUC of 0.936 (0.956-0.916) in the large combination cohort (n?=?1010). It greatly improved diagnostic accuracy over PSA and risk factors (p?<?0.0001). When it was combined with PSA, the AUC increased to 0.961 (0.980-0.942). Importantly, the 25-Gene Panel urine test was able to accurately identify clinically significant and insignificant PCa with AUC of 0.928 (95% CI 0.947-0.909) in the combination cohort (n?=?727). In addition, it was able to show the absence of cancer after prostatectomy with high accuracy.ConclusionsThe 25-Gene Panel urine test is the first highly accurate and non-invasive liquid biopsy method without DRE for PCa diagnosis. In clinical practice, it may be used for identifying patients in need of biopsy for cancer diagnosis and patients with clinically significant cancer for immediate treatment, and potentially assisting cancer treatment follow-up.

Project description:BackgroundColorectal cancer (CRC) is one of the world's most common malignancies. Epigenetics is the study of heritable changes in characteristics beyond the DNA sequence. Epigenetic information is essential for maintaining specific expression patterns of genes and the normal development of individuals, and disorders of epigenetic modifications may alter the expression of oncogenes and tumor suppressor genes and affect the development of cancer. This study elucidates the relationship between epigenetics and the prognosis of CRC patients by developing a predictive model to explore the potential value of epigenetics in the treatment of CRC.MethodsGene expression data of CRC patients' tumor tissue and controls were downloaded from GEO database. Combined with the 720 epigenetic-related genes (ERGs) downloaded from EpiFactors database, prognosis-related epigenetic genes were selected by univariate cox and LASSO analyses. The Kaplan-Meier and ROC curve were used to analyze the accuracy of the model. Data of 238 CRC samples with survival data downloaded from the GSE17538 were used for validation. Finally, the risk model is combined with the clinical characteristics of CRC patients to perform univariate and multivariate cox regression analysis to obtain independent risk factors and draw nomogram. Then we evaluated the accuracy of its prediction by calibration curves.ResultsA total of 2906 differentially expressed genes (DEGs) were identified between CRC and control samples. After overlapping DEGs with 720 ERGs, 56 epigenetic-related DEGs (DEERGs) were identified. Combining univariate and LASSO regression analysis, the 8 epigenetic-related genes-based risk score model of CRC was established. The ROC curves and survival difference of high and low risk groups revealed the good performance of the risk score model based on prognostic biomarkers in both training and validation sets. A nomogram with good performance to predict the survival of CRC patients were established based on age, NM stage and risk score. The calibration curves showed that the prognostic model had good predictive performance.ConclusionIn this study, an epigenetically relevant 8-gene signature was constructed that can effectively predict the prognosis of CRC patients and provide potential directions for targeted therapies for CRC.

Dataset Information

Development and validation of a lipogenic genes panel for diagnosis and recurrence of colorectal cancer.

Publications

Development and validation of a lipogenic genes panel for diagnosis and recurrence of colorectal cancer.

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