Machine learning in the development of targeting microRNAs in human disease

Advancing miRNA cancer research through artificial intelligence: from biomarker discovery to therapeutic targeting

MicroRNAs (miRNAs), a class of small non-coding RNAs, play a vital role in regulating gene expression at the post-transcriptional level. Their discovery has profoundly impacted therapeutic strategies, particularly in cancer treatment, where RNA therapeutics, including miRNA-based targeted therapies, have gained prominence. Advances in RNA sequencing technologies have facilitated a comprehensive exploration of miRNAs-from fundamental research to their diagnostic and prognostic potential in various diseases, notably cancers. However, the manual handling and interpretation of vast RNA datasets pose significant challenges. The advent of artificial intelligence (AI) has revolutionized biological research by efficiently extracting insights from complex data. Machine learning algorithms, particularly deep learning techniques are effective for identifying critical miRNAs across different cancers and developing prognostic models. Moreover, the integration of AI has led to the creation of comprehensive miRNA databases for identifying mRNA and gene targets, thus facilitating deeper understanding and application in cancer research. This review comprehensively examines current developments in the application of machine learning techniques in miRNA research across diverse cancers. We discuss their roles in identifying biomarkers, elucidating miRNA targets, establishing disease associations, predicting prognostic outcomes, and exploring broader AI applications in cancer research. This review aims to guide researchers in leveraging AI techniques effectively within the miRNA field, thereby accelerating advancements in cancer diagnostics and therapeutics.

6-Shogaol alleviates high-fat diet induced hepatic steatosis through miR-3066-5p/Grem2 pathway

The purpose of this study is to investigate the mechanism by which 6-shogaol ameliorates hepatic steatosis via miRNA-mRNA interaction analysis. C57BL/6 J mice were fed a high-fat diet (HFD) for 12 weeks, during which 6-shogaol was administered orally. The liver lipid level, liver function and oxidative damage in mice were evaluated. mRNA sequencing, miRNA sequencing, and RT-qPCR were employed to compare the expression profiles between the HFD group and the 6-shogaol-treated group. High-throughput sequencing was used to construct the mRNA and miRNA libraries. Target prediction and integration analysis identified eight potential miRNA-mRNA pairs involved in hepatic steatosis, which were subsequently validated in liver tissues and AML12 cells. The findings revealed that 6-shogaol modulates the miR-3066-5p/Grem2 pathway, thereby improving hepatic steatosis. This study provides new insights into the mechanisms through which 6-shogaol alleviates hepatic steatosis, establishing a foundation for future research on natural active compounds for the treatment of metabolic diseases.

Development of a Diagnostic Model for Pancreatic Ductal Adenocarcinoma Using Machine Learning and Blood-Based miRNAs

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major cancers due to a lack of symptoms in early stages, early detection tools, and optimal therapies for late-stage patients. Thus, effective and non-invasive diagnostic tests are greatly needed. Recently, circulating miRNAs have been reported to be altered in PDAC. They are promising biomarkers because of stability in the blood, ease of non-invasive detection, and convenient screening methods. This study aimed to use blood-based miRNA biomarkers and various analysis methods in the development of a machine-learning (ML) model for PDAC.

METHODS

Blood-based miRNAs associated with PDAC were collected from open sources. miRNA sequences, targeted genes, and involved pathways were used to construct a set of descriptors for an ML model.

RESULTS

Bioinformatics analysis revealed that most genes in pancreatic cancer and insulin signaling pathways were targeted by the PDAC-related miRNAs. The best-performing ML model with the Random Forest classifier was able to achieve an accuracy of 88.4%. Model evaluations of an independent PDAC-associated miRNAs test set had 100% accuracy while non-cancer miRNAs had 52.4% accuracy, indicating specificity to PDAC.

CONCLUSIONS

Our results suggest an ML model developed using blood-based miRNA biomarkers' target gene, pathway, and sequence features could be potentially implicated in PDAC diagnostics.

Analysis of Amblyomma americanum microRNAs in response to Ehrlichia chaffeensis infection and their potential role in vectorial capacity

Background

MicroRNAs (miRNAs) represent a subset of small noncoding RNAs and carry tremendous potential for regulating gene expression at the post-transcriptional level. They play pivotal roles in distinct cellular mechanisms including inhibition of bacterial, parasitic, and viral infections via immune response pathways. Intriguingly, pathogens have developed strategies to manipulate the host's miRNA profile, fostering environments conducive to successful infection. Therefore, changes in an arthropod host's miRNA profile in response to pathogen invasion could be critical in understanding host-pathogen dynamics. Additionally, this area of study could provide insights into discovering new targets for disease control and prevention. The main objective of the present study is to investigate the functional role of differentially expressed miRNAs upon Ehrlichia chaffeensis, a tick-borne pathogen, infection in tick vector, Amblyomma americanum.

Methods

Small RNA libraries from uninfected and E. chaffeensis-infected Am. americanum midgut and salivary gland tissues were prepared using the Illumina Truseq kit. Small RNA sequencing data was analyzed using miRDeep2 and sRNAtoolbox to identify novel and known miRNAs. The differentially expressed miRNAs were validated using a quantitative PCR assay. Furthermore, a miRNA inhibitor approach was used to determine the functional role of selected miRNA candidates.

Results

The sequencing of small RNA libraries generated >147 million raw reads in all four libraries and identified a total of >250 miRNAs across the four libraries. We identified 23 and 14 differentially expressed miRNAs in salivary glands, and midgut tissues infected with E. chaffeensis, respectively. Three differentially expressed miRNAs (miR-87, miR-750, and miR-275) were further characterized to determine their roles in pathogen infection. Inhibition of target miRNAs significantly decreased the E. chaffeensis load in tick tissues, which warrants more in-depth mechanistic studies.

Conclusions

The current study identified known and novel miRNAs and suggests that interfering with these miRNAs may impact the vectorial capacity of ticks to harbor Ehrlichia. This study identified several new miRNAs for future analysis of their functions in tick biology and tick-pathogen interaction studies.

Analysis of Amblyomma americanum microRNAs in response to Ehrlichia chaffeensis infection and their potential role in vectorial capacity

Background

MicroRNAs (miRNAs) represent a subset of small noncoding RNAs and carry tremendous potential for regulating gene expression at the post-transcriptional level. They play pivotal roles in distinct cellular mechanisms including inhibition of bacterial, parasitic, and viral infections via immune response pathways. Intriguingly, pathogens have developed strategies to manipulate the host's miRNA profile, fostering environments conducive to successful infection. Therefore, changes in an arthropod host's miRNA profile in response to pathogen invasion could be critical in understanding host-pathogen dynamics. Additionally, this area of study could provide insights into discovering new targets for disease control and prevention. The main objective of the present study is to investigate the functional role of differentially expressed miRNAs upon Ehrlichia chaffeensis, a tick-borne pathogen, infection in tick vector, Amblyomma americanum.

Methods

Small RNA libraries from uninfected and E. chaffeensis-infected Am. americanum midgut and salivary gland tissues were prepared using the Illumina Truseq kit. Small RNA sequencing data was analyzed using miRDeep2 and sRNAtoolbox to identify novel and known miRNAs. The differentially expressed miRNAs were validated using a quantitative PCR assay. Furthermore, a miRNA inhibitor approach was used to determine the functional role of selected miRNA candidates.

Results

The sequencing of small RNA libraries generated >147 million raw reads in all four libraries and identified a total of >250 miRNAs across the four libraries. We identified 23 and 14 differentially expressed miRNAs in salivary glands, and midgut tissues infected with E. chaffeensis, respectively. Three differentially expressed miRNAs (miR-87, miR-750, and miR-275) were further characterized to determine their roles in pathogen infection. Inhibition of target miRNAs significantly decreased the E. chaffeensis load in tick tissues, which warrants more in-depth mechanistic studies.

Conclusions

The current study identified known and novel miRNAs and suggests that interfering with these miRNAs may impact the vectorial capacity of ticks to harbor Ehrlichia. This study identified several new miRNAs for future analysis of their functions in tick biology and tick-pathogen interaction studies.

What are the changes in the hotspots and frontiers of microRNAs in hepatocellular carcinoma over the past decade?

BACKGROUND

Emerging research suggests that microRNAs (miRNAs) play an important role in the development of hepatocellular carcinoma (HCC). A comprehensive analysis of recent research concerning miRNAs in HCC development could provide researchers with a valuable reference for further studies.

AIM

To make a comprehensive analysis of recent studies concerning miRNAs in HCC.

METHODS

All relevant publications were retrieved from the Web of Science Core Collection database. Bibliometrix software, VOSviewer software and CiteSpace software were used to visually analyze the distribution by time, countries, institutions, journals, and authors, as well as the keywords, burst keywords and thematic map.

RESULTS

A total of 9426 publications on this topic were found worldwide. According to the keywords analysis, we found that the studies of miRNAs focused on their expression level, effects, and mechanisms on the biological behaviour of HCC. Keywords bursting analysis showed that in the early years (2013-2017), "microRNA expression", "gene expression", "expression profile", "functional polymorphism", "circulating microRNA", "susceptibility" and "mir 21" started to attract attention. In the latest phase (2018-2022), the hot topics turned to "sorafenib resistance", "tumor microenvironment" and so on.

CONCLUSION

This study provides a comprehensive overview of the role of miRNAs in HCC development based on bibliometric analysis. The hotspots in this field focus on miRNAs expression level, effects, and mechanisms on the biological behavior of HCC. The frontiers turned to sorafenib resistance, tumor microenvironment and so on.

The leading global health challenges in the artificial intelligence era

Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.