Dataset Information

Identification of a stool long non-coding RNAs panel as a potential biomarker for early detection of colorectal cancer.

ABSTRACT:

Background

The feces of colorectal cancer (CRC) patients contain tumor colonocytes, which constantly shed into the lumen area. Therefore, stool evaluation can be considered as a rapid and low-risk way to directly determine the colon and rectum status. As long non-coding RNAs (lncRNAs) alterations are important in cancer cells fate regulation, we aimed to assess the level of a panel of cancer-related lncRNAs in fecal colonocytes.

Methods

The population study consisted of 150 subjects, including a training set, a validation set, and a group of 30 colon polyps. The expression levels of lncRNAs were evaluated by quantitative real-time PCR (qRT-PCR). The NPInetr and EnrichR tools were used to identify the interactions and functions of lncRNAs.

Results

A total of 10 significantly dysregulated lncRNAs, including CCAT1, CCAT2, H19, HOTAIR, HULC, MALAT1, PCAT1, MEG3, PTENP1, and TUSC7, were chosen for designing a predictive panel. The diagnostic performance of the panel in distinguishing CRCs from the healthy group was AUC: 0.8554 in the training set and 0.8465 in the validation set. The AUC for early CRCs (I-II TNM stages) was 0.8554 in the training set and 0.8465 in the validation set, and for advanced CRCs (III-IV TNM stages) were 0.9281 in the training set and 0.9236 in the validation set. The corresponding AUC for CRCs vs polyps were 0.9228 (I-IV TNM stages), 0.9042 (I-II TNM stages), and 0.9362 (III-IV TNM stages).

Conclusions

These data represented the application of analysis of fecal colonocytes lncRNAs in early detection of CRC.

SUBMITTER: Gharib E

PROVIDER: S-EPMC7891513 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Identification of a stool long non-coding RNAs panel as a potential biomarker for early detection of colorectal cancer.

Gharib Ehsan E Nazemalhosseini-Mojarad Ehsan E Baghdar Kaveh K Nayeri Zahra Z Sadeghi Hossein H Rezasoltani Sama S Jamshidi-Fard Arezo A Larki Pegah P Sadeghi Amir A Hashemi Mehrdad M Asadzadeh Aghdaei Hamid H

Journal of clinical laboratory analysis 20201023 2

<h4>Background</h4>The feces of colorectal cancer (CRC) patients contain tumor colonocytes, which constantly shed into the lumen area. Therefore, stool evaluation can be considered as a rapid and low-risk way to directly determine the colon and rectum status. As long non-coding RNAs (lncRNAs) alterations are important in cancer cells fate regulation, we aimed to assess the level of a panel of cancer-related lncRNAs in fecal colonocytes.<h4>Methods</h4>The population study consisted of 150 subjec ...[more]

PMID: 33094859

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Project description:Gallbladder carcinoma (GBC) is a common malignancy and is usually diagnosed in the late stages of the disease. The identification of new effective early diagnostic biomarkers could represent an effective approach in reducing mortality in GBC. Altered expression of long non-coding RNAs (lncRNAs) is believed to be associated with the emergence and development of GBC. Our study aims to identify the expression of a range of circulating lncRNAs, including HOTAIR, ANRIL, H19, CCAT1 and MEG3, in matched serum and tissues of GBC for diagnosis and its association with clinicopathological features. The case and control study included matched serum and tissues from 63 GBC, 19 cholecystitis (CC), and 46 normal controls (NC). RNA extraction and cDNA synthesis from serum and fresh tissue match were performed using commercially available kits. Relative expression was assessed using SYBR Green real-time quantitative polymerase chain reaction. Circulating lncRNA levels including HOTAIR, ANRIL and H19 were upregulated in serum samples, while MEG3 and CCAT1 were downregulated in GBC compared to controls. The trend towards upregulation and downregulation was comparable in the tissue. HOTAIR and MEG3 levels were significantly different between serum CC and early-stage GBC (p = 0.0373, 0.0020), while H19 was significantly upregulated comparing early-stage GBC to advanced-stage GBC (p = 0.018). The expression of ANRIL was significant with M stage (p = 0.0488), H19 with stage (p = 0.009), M stage (p=<0.0001) & stage (0.009) and CCAT1 with M stage (0.044). When distinguishing GBC and NC, AUC for HOTAIR was 0.75, ANRIL 0.78, H19 0.74, CCAT1 0.80 and 0.96 for MEG3. The combination sensitivity for lncRNAs ranged from 84.13% (CI: 72.74-92.12%) to 100.0% (CI: 94.31-100.0%). Significant diagnostic value in discriminating pathologic stage was observed for ANRIL and MEG3 (p = 0.022, p = 0.0005). LncRNA show a significant change in expression in GBC and in discrimination of early stage from late-stage disease. The detection of 2 lncRNAs in panels, in coordination with radiology, could represent a potential serum-based biomarker for early-stage GBC diagnosis.

Project description:BackgroundRecent studies have indicated that a special class of long non-coding RNAs (lncRNAs), namely Transcribed-Ultraconservative Regions are transcribed from specific DNA regions (T-UCRs), 100[Formula: see text] conserved in human, mouse, and rat genomes. This is noticeable, as lncRNAs are usually poorly conserved. Despite their peculiarities, T-UCRs remain very understudied in many diseases, including cancer and, yet, it is known that dysregulation of T-UCRs is associated with cancer as well as with human neurological, cardiovascular, and developmental pathologies. We have recently reported the T-UCR uc.8+ as a potential prognostic biomarker in bladder cancer.ResultsThe aim of this work is to develop a methodology, based on machine learning techniques, for the selection of a predictive signature panel for bladder cancer onset. To this end, we analyzed the expression profiles of T-UCRs from surgically removed normal and bladder cancer tissues, by using custom expression microarray. Bladder tissue samples from 24 bladder cancer patients (12 Low Grade and 12 High Grade), with complete clinical data, and 17 control samples from normal bladder epithelium were analysed. After the selection of preferentially expressed and statistically significant T-UCRs, we adopted an ensemble of statistical and machine learning based approaches (i.e., logistic regression, Random Forest, XGBoost and LASSO) for ranking the most important diagnostic molecules. We identified a signature panel of 13 selected T-UCRs with altered expression profiles in cancer, able to efficiently discriminate between normal and bladder cancer patient samples. Also, using this signature panel, we classified bladder cancer patients in four groups, each characterized by a different survival extent. As expected, the group including only Low Grade bladder cancer patients had greater overall survival than patients with the majority of High Grade bladder cancer. However, a specific signature of deregulated T-UCRs identifies sub-types of bladder cancer patients with different prognosis regardless of the bladder cancer Grade.ConclusionsHere we present the results for the classification of bladder cancer (Low and High Grade) patient samples and normal bladder epithelium controls by using a machine learning application. The T-UCR's panel can be used for learning an eXplainable Artificial Intelligent model and develop a robust decision support system for bladder cancer early diagnosis providing urinary T-UCRs data of new patients. The use of this system instead of the current methodology will result in a non-invasive approach, reducing uncomfortable procedures (such as cystoscopy) for the patients. Overall, these results raise the possibility of new automatic systems, which could help the RNA-based prognosis and/or the cancer therapy in bladder cancer patients, and demonstrate the successful application of Artificial Intelligence to the definition of an independent prognostic biomarker panel.

Dataset Information

Identification of a stool long non-coding RNAs panel as a potential biomarker for early detection of colorectal cancer.

Background

Methods

Results

Conclusions

Publications

Identification of a stool long non-coding RNAs panel as a potential biomarker for early detection of colorectal cancer.

Similar Datasets

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