Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer

Guan, Qingzhou; Yan, Haidan; Chen, Yanhua; Zheng, Buhong; Cai, Hao; He, Jun; Song, Kai; Guo, You; Ao, Lu; Liu, Huaping; Zhao, Wenyuan; Wang, Xianlong; Guo, Zheng

doi:10.1186/s12864-018-4446-y

Cited by 41 publications

(46 citation statements)

References 69 publications

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“…The need for a reference population to compare with individual gene expressions could make it difficult to implement the method clinically. Qualitative rather than quantitative transcriptional diagnostic signatures have been proposed as a stable approach to gene expression profiling for diagnostic purposes 183 , which makes use of expression ratios between gene pairs within the same individual instead of relative quantities in relation to other samples. This approach could be investigated for CD diagnostics.…”

Section: Discussionmentioning

confidence: 99%

Biomarkers of Inflammation and Intestinal Mucosa Pathology in Celiac Disease

Bragde

2019

Linköping University Medical Dissertations

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Celiac disease (CD) is a chronic small intestinal immune-mediated enteropathy triggered by gluten. The only currently available treatment is complying with a lifelong gluten-free diet, which should not be commenced before a CD diagnosis has been established by diagnostic test results (including histopathologic assessment of small intestinal biopsies and CD-specific antibody levels). This makes diagnostic swiftness and accuracy important. In cases with low CD-specific antibody levels and/or low-grade intestinal injuries the diagnosis can be difficult to establish. The main objective of this thesis was to complement and improve CD diagnostics by identifying and implementing new biomarkers, mainly based on gene expression, in small intestinal biopsies and blood. In paper I, genes were selected to reflect villous height, crypt elongation, immune response, and epithelial integrity. The results showed that a subset of those genes could discriminate active CD mucosa from mucosa without CD-related changes and grade the intestinal injury. In paper III, an unbiased investigation of gene expression in CD mucosa was performed using transcriptome analysis. Active CD and non-CD mucosa showed differential expression in a subset of genes, and some were differentially expressed in CD mucosa before histopathologic assessment could confirm intestinal alterations compatible with a CD diagnosis. Gene set analysis revealed that there are many biological processes affected in CD mucosa, including those associated with immune response, microbial infection, phagocytosis, intestinal barrier function, metabolism, and transportation. In parallel, gene expression was investigated in stabilised whole blood. Blood is a more accessible sampling material than intestinal biopsies, and stabilised blood is suitable for routine diagnostics since transcript levels are preserved at sampling. In paper II, expressions from a selection of genes were quantified in stabilised whole blood (RNA) and/or plasma (protein). Three genes with differential expression in CD were identified. Compared to the CD-specific autoantibodies against tissue transglutaminase (anti-TG2) alone, the addition of the information from the new potential markers resulted in a non-significant contribution to the diagnostic capacity of anti-TG2. An unbiased investigation using transcriptome analysis (paper IV) showed that gene level expression differences in stabilised whole blood were small between CD and non-CD. However, expression differences on a gene set level could potentially be used in CD diagnostics. CD-associated biological processes suggested UBD ubiquitin D ULN upper limit of normal ZFR zinc finger RNA binding protein Contents ix Contents Introduction .

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Section: Discussionmentioning

confidence: 99%

Biomarkers of Inflammation and Intestinal Mucosa Pathology in Celiac Disease

Bragde

2019

Linköping University Medical Dissertations

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“…The existed diagnostic signatures are mainly on the basis of risk scores obtained from signature genes' expression (Wurmbach et al, 2007;Archer et al, 2009;Zhou et al, 2015Zhou et al, , 2017Qu et al, 2019), which are highly sensitive to measurement batch effects (Guan et al, 2018) and are hardly applied in clinical settings. Luckily the relative expression orderings (REO)-based strategy (Zhang et al, 2013;Zhou et al, 2013;Wang et al, 2015;Li et al, 2016), which was firstly proposed by Eddy et al (2010), is highly robust against experimental batch effects (Cai et al, 2015;Ao et al, 2016;Zhao et al, 2016) and platform differences (Guan et al, 2016), partial RNA degradation (Chen et al, 2017;Liao et al, 2017Liao et al, , 2018Tang et al, 2018) and uncertain sampling sites within the same cancer tissue (Cheng et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method

Zhang

Tan

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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Hepatocellular carcinoma (HCC) is a serious cancer which ranked the fourth in cancerrelated death worldwide. Hence, more accurate diagnostic models are urgently needed to aid the early HCC diagnosis under clinical scenarios and thus improve HCC treatment and survival. Several conventional methods have been used for discriminating HCC from cirrhosis tissues in patients without HCC (CwoHCC). However, the recognition successful rates are still far from satisfactory. In this study, we applied a computational approach that based on machine learning method to a set of microarray data generated from 1091 HCC samples and 242 CwoHCC samples. The within-sample relative expression orderings (REOs) method was used to extract numerical descriptors from gene expression profiles datasets. After removing the unrelated features by using maximum redundancy minimum relevance (mRMR) with incremental feature selection, we achieved "11-gene-pair" which could produce outstanding results. We further investigated the discriminate capability of the "11-gene-pair" for HCC recognition on several independent datasets. The wonderful results were obtained, demonstrating that the selected gene pairs can be signature for HCC. The proposed computational model can discriminate HCC and adjacent non-cancerous tissues from CwoHCC even for minimum biopsy specimens and inaccurately sampled specimens, which can be practical and effective for aiding the early HCC diagnosis at individual level.

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“…[28][29][30][31] The robustness property of the REO enables researchers to integrate multiple datasets produced by the same or similar platforms for developing disease signatures or classifiers, 32,33 which makes it more likely to find robust signatures. 10,32,34 In addition, the qualitative transcriptional characteristics are highly robust against varied proportions of the tumor epithelial cell in specimens sampled from different tumor locations of the same patients, 26 partial RNA degradation during specimen preparation and storage, 25 and amplification bias for minimum specimens, 27 which are the common factors that lead to the failure of quantitative transcriptional signatures in clinical practice. Therefore, it is worth exploiting the within-sample REOs to identify a robust qualitative signature for the early diagnosis of CRC.…”

Section: Introductionmentioning

confidence: 99%

A qualitative transcriptional signature for the early diagnosis of colorectal cancer

et al. 2019

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Currently, using biopsy specimens for the early diagnosis of colorectal cancer (CRC) is not entirely reliable due to insufficient sampling amount and inaccurate sampling location. Thus, it is necessary to develop a signature that can accurately identify patients with CRC under these clinical scenarios. Based on the relative expression orderings of genes within individual samples, we developed a qualitative transcriptional signature to discriminate CRC tissues, including CRC adjacent normal tissues from non‐CRC individuals. The signature was validated using multiple microarray and RNA sequencing data from different sources. In the training data, a signature consisting of 7 gene pairs was identified. It was well validated in both biopsy and surgical resection specimens from multiple datasets measured by different platforms. For biopsy specimens, 97.6% of 42 CRC tissues and 94.5% of 163 non‐CRC (normal or inflammatory bowel disease) tissues were correctly classified. For surgically resected specimens, 99.5% of 854 CRC tissues and 96.3% of 81 CRC adjacent normal tissues were correctly identified as CRC. Notably, we additionally measured 33 CRC biopsy specimens by the Affymetrix platform and 13 CRC surgical resection specimens, with different proportions of tumor epithelial cells, ranging from 40% to 100%, by the RNA sequencing platform, and all these samples were correctly identified as CRC. The signature can be used for the early diagnosis of CRC, which is also suitable for minimum biopsy specimens and inaccurately sampled specimens, and thus has potential value for clinical application.

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Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer

Cited by 41 publications

References 69 publications

Biomarkers of Inflammation and Intestinal Mucosa Pathology in Celiac Disease

Biomarkers of Inflammation and Intestinal Mucosa Pathology in Celiac Disease

Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method

A qualitative transcriptional signature for the early diagnosis of colorectal cancer

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