Noninvasive early diagnosis of intestinal diseases based on artificial intelligence in genomics and microbiome

Role of artificial intelligence in Crohn's disease intestinal strictures and fibrosis

Crohn's disease (CD) is a chronic inflammatory disorder of the gastrointestinal tract. Intestinal fibrosis or stricture is one of the most prevalent complications in CD with a high recurrence rate. Manual examination of intestinal fibrosis or stricture by physicians may be biased or inefficient. A rapid development of artificial intelligence (AI) technique in recent years facilitates the detection of existing or possible intestinal fibrosis and stricture in CD through various modalities, including endoscopy, imaging examination, and serological biomarkers. We reviewed the articles on AI application in diagnosing intestinal fibrosis and stricture in CD during the past decade and categorized them into three aspects based on the detection methods, and found that AI helps accurate and expedient identification and prediction of intestinal fibrosis and stenosis in CD.

Blood miRNAs miR-549a, miR-552, and miR-592 serve as potential disease-specific panels to diagnose colorectal cancer

Introduction

miRNAs originating from colorectal cancer (CRC) tissue receive significant focus in the early diagnosis of CRC due to their stability in body fluids. However, if these miRNAs originated from alternative organs, their prognostic value will diminish. Thus, in this study, we aim to identify disease-specific miRNAs for colorectal cancer (CRC) by employing bioinformatics and experimental methodologies.

Method

To identify CRC-specific miRNAs, we retrieved miRNA profiles of CRC and normal tissues from the Cancer Genome Atlas (TCGA) database. Subsequently, computational strategies were utilized to select potential candidate miRNAs. Following this, the expression levels of the potent miRNAs were assessed through RT-qPCR in both CRC tissue and serum samples from patients (N = 46), as well as healthy individuals (N = 46). Additionally, the associations between clinicopathological characteristics, survival outcomes, and diagnostic accuracy were evaluated.

Results

A total of 8893 RNA-seq expression data were acquired from TCGA, comprising 8250 data from 19 distinct cancer types and 643 corresponding healthy samples. Based on the computational methodology, miR-549a, miR-552, and miR-592 were identified as the principal expressed miRNAs in colorectal cancer (CRC). Within these miRNAs, miR-552 displayed a substantial association with tumors at the N and T stages. miR-549a and miR-592 were observed to be linked exclusively to the invasion of tumor depth and tumor stage (TNM), respectively. The receiver operating characteristic (ROC) analysis conducted on the miRNA expression in serum samples revealed that all miRNAs exhibited an area under the ROC curve (AUC) of up to 0.86, thereby indicating their high diagnostic accuracy.

Conclusion

Considering the strong associations of these three identified miRNAs with CRC, they can collectively serve as a panel for specific discrimination of CRC from other types of cancer within the body. Although this study focused solely on CRC, this approach can potentially be applied to other cancer types as well.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types

Machine learning (ML) includes a broad class of computer programs that improve with experience and shows unique strengths in performing tasks such as clustering, classification and regression. Over the past decade, microbial communities have been implicated in influencing the onset, progression, metastasis, and therapeutic response of multiple cancers. Host-microbe interaction may be a physiological pathway contributing to cancer development. With the accumulation of a large number of high-throughput data, ML has been successfully applied to the study of human cancer microbiomics in an attempt to reveal the complex mechanism behind cancer. In this review, we begin with a brief overview of the data sources included in cancer microbiomics studies. Then, the characteristics of the ML algorithm are briefly introduced. Secondly, the application progress of ML in cancer microbiomics is also reviewed. Finally, we highlight the challenges and future prospects facing ML in cancer microbiomics. On this basis, we conclude that the development of cancer microbiomics can not be achieved without ML, and that ML can be used to develop tumor-targeting microbial therapies, ultimately contributing to personalized and precision medicine.

Dynamic Network Construction for Identifying Early Warning Signals Based On a Data-Driven Approach: Early Diagnosis Biomarker Discovery for Gastric Cancer

During the development of complex diseases, there is a critical transition from one status to another at a tipping point, which can be an early indicator of disease deterioration. To effectively enhance the performance of early risk identification, a novel dynamic network construction algorithm for identifying early warning signals based on a data-driven approach (EWS-DDA) was proposed. In EWS-DDA, the shrunken centroid was introduced to measure dynamic expression changes in assumed pathway reactions during the progression of complex disease for network construction and to define early warning signals by means of a data-driven approach. We applied EWS-DDA to perform a comprehensive analysis of gene expression profiles of gastric cancer (GC) from The Cancer Genome Atlas database and the Gene Expression Omnibus database. Six crucial genes were selected as potential biomarkers for the early diagnosis of GC. The experimental results of statistical analysis and biological analysis suggested that the six genes play important roles in GC occurrence and development. Then, EWS-DDA was compared with other state-of-the-art network methods to validate its performance. The theoretical analysis and comparison results suggested that EWS-DDA has great potential for a more complete presentation of disease deterioration and effective extraction of early warning information.

The diagnostic potential and barriers of microbiome based therapeutics

High throughput technological innovations in the past decade have accelerated research into the trillions of commensal microbes in the gut. The 'omics' technologies used for microbiome analysis are constantly evolving, and large-scale datasets are being produced. Despite of the fact that much of the research is still in its early stages, specific microbial signatures have been associated with the promotion of cancer, as well as other diseases such as inflammatory bowel disease, neurogenerative diareses etc. It has been also reported that the diversity of the gut microbiome influences the safety and efficacy of medicines. The availability and declining sequencing costs has rendered the employment of RNA-based diagnostics more common in the microbiome field necessitating improved data-analytical techniques so as to fully exploit all the resulting rich biological datasets, while accounting for their unique characteristics, such as their compositional nature as well their heterogeneity and sparsity. As a result, the gut microbiome is increasingly being demonstrating as an important component of personalised medicine since it not only plays a role in inter-individual variability in health and disease, but it also represents a potentially modifiable entity or feature that may be addressed by treatments in a personalised way. In this context, machine learning and artificial intelligence-based methods may be able to unveil new insights into biomedical analyses through the generation of models that may be used to predict category labels, and continuous values. Furthermore, diagnostic aspects will add value in the identification of the non invasive markers in the critical diseases like cancer.

Coronary Atherosclerotic Disease and Cancer: Risk Factors and Interrelation

Background

In our clinical work, we found that cancer patients were susceptible to coronary atherosclerotic heart disease (CAD). However, less is known about the relationship between CAD and cancer. The present study aimed to identify the risk factors for CAD and cancer, as well as the relationship between CAD and cancer.

Methods

In this retrospective study, 1600 patients between January 2012 and June 2019 were enrolled and divided into groups according to whether they had CAD or cancer. Single-factor and multivariate analysis methods were applied to examine the risk factors for CAD and cancer.

Results

(1) Cancer prevalence was significantly higher in patients with CAD than in patients without CAD (47.2 vs. 20.9%). The prevalence of CAD in cancer and non-cancer patients was 78.9 and 52.4%, respectively. (2) Multivariable logistic regression showed that patients with cancer had a higher risk of developing CAD than non-cancer patients (OR: 2.024, 95% CI: 1.475 to 2.778, p < 0.001). Respiratory (OR: 1.981, 95% CI: 1.236–3.175, p = 0.005), digestive (OR: 1.899, 95% CI: 1.177–3.064, p = 0.009) and urogenital (OR: 3.595, 95% CI: 1.696–7.620, p = 0.001) cancers were significantly associated with a higher risk of CAD compared with no cancer. (3) Patients with CAD also had a higher risk of developing cancer than non-CAD patients (OR = 2.157, 95% CI: 1.603 to 2.902, p < 0.001). Patients in the Alanine aminotransferase (ALT) level ≥ 40 U/L group had a lower risk of cancer than patients in the ALT level < 20 U/L group (OR: 0.490, 95% CI: 0.333–0.722, p < 0.001). (4) An integrated variable (Y = 0.205 × 10–1 age − 0.595 × 10–2 HGB − 0.116 × 10–1 ALT + 0.135 FIB) was identified for monitoring the occurrence of cancer among CAD patients, with an AUC of 0.720 and clinical sensitivity/specificity of 0.617/0.711.

Conclusion

(1) We discovered that CAD was an independent risk factor for cancer and vice versa. (2) Digestive, respiratory and urogenital cancers were independent risk factors for CAD. (3) We created a formula for the prediction of cancer among CAD patients. (4) ALT, usually considered a risk factor, was proven to be a protective factor for cancer in this study.

Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer

Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.

Functional profile of host microbiome indicates Clostridioides difficile infection

Clostridioides difficile infection (CDI) is a gastro-intestinal (GI) infection that illustrates how perturbations in symbiotic host-microbiome interactions render the GI tract vulnerable to the opportunistic pathogens. CDI also serves as an example of how such perturbations could be reversed via gut microbiota modulation mechanisms, especially fecal microbiota transplantation (FMT). However, microbiome-mediated diagnosis of CDI remains understudied. Here, we evaluated the diagnostic capabilities of the fecal microbiome on the prediction of CDI. We used the metagenomic sequencing data from ten previous studies, encompassing those acquired from CDI patients treated by FMT, CDI-negative patients presenting other intestinal health conditions, and healthy volunteers taking antibiotics. We designed a hybrid species/function profiling approach that determines the abundances of microbial species in the community contributing to its functional profile. These functionally informed taxonomic profiles were then used for classification of the microbial samples. We used logistic regression (LR) models using these features, which showed high prediction accuracy (with an average A U C ≥ 0.91 ), substantiating that the species/function composition of the gut microbiome has a robust diagnostic prediction of CDI. We further assessed the confounding impact of antibiotic therapy on CDI prediction and found that it is distinguishable from the CDI impact. Finally, we devised a log-odds score computed from the output of the LR models to quantify the likelihood of CDI in a gut microbiome sample and applied it to evaluating the effectiveness of FMT based on post-FMT microbiome samples. The results showed that the gut microbiome of patients exhibited a gradual but steady improvement after receiving successful FMT, indicating the restoration of the normal microbiome functions.

Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.