MicroRNA, mRNA, and Proteomics Biomarkers and Therapeutic Targets for Improving Lung Cancer Treatment Outcomes

Ye, Qing; Raese, Rebecca; Luo, Dajie; Cao, Shixun; Wan, Ying-Wooi; Qian, Yong; Guo, Nancy Lan

doi:10.3390/cancers15082294

Cited by 4 publications

(2 citation statements)

References 99 publications

(97 reference statements)

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“…Cas13a-Cas12a Amplification System (Zhao et al 2023) Detection of cancerous miRNA indicators is also accurately performed by the CRISPR/Cas9 system. A study examined 73 miRNAs that could detect lung cancer and determined that 4 were oncogenic via CRISPR-CAs9/RNA interference assays (RNAi) (Ye et al 2023). This method of miRNA detection takes a sample of isolated possible oncogenic miRNAs within the patient's samples and selects specific tumor proliferation miRNAs after a CRISPR-CAs9/RNAi assay (See Figure 7).…”

Section: How Crispr Can Detect Mirna and Diagnose Cancermentioning

confidence: 99%

CRISPR Based Gene Editing Technologies in Cancer Research and Detection via miRNA

Carod-Diaz,

Prince

2023

J Stud Res

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The CRISPR system of genetic editing has been a breakthrough in all fields of science and medicine, including that of cancer research, therapy, and diagnosis. In the last two years, research has narrowed down on CRISPRs ability to serve as a tool to detect oncogenes in patients to diagnose cancer in a quick and efficient manner. CRISPR and the three key enzymes (cas9, cas12, and cas13) are a revolutionary tool for medicine and cancer trials. MicroRNAs (miRNAs) are short noncoding RNA which regulate gene expression and gene functions, both crucial to cell functions. However, aberrant miRNA is expressed in all tumor proliferation diseases, including cancer. Studies have shown CRISPRs ability to detect cancerous levels of oncogenic MicroRNAs in human samples through a variety of methods such as CRISPR-Cas9/RNAi, CRISPR/Cas12 and RCA coupling, CRISPR/Cas13 exosome coupling, and Cas13a-Cas12a amplification. All of these methods provide extremely quick high sensitivity and high specificity detection of oncogenic miRNA in samples with concentrations below 1 fM. This paper is a synthesis of the current research on the methods mentioned above to aid in the further research of CRISPR/Cas systems in cancer diagnosis and cancer therapeutics.

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Section: How Crispr Can Detect Mirna and Diagnose Cancermentioning

confidence: 99%

CRISPR Based Gene Editing Technologies in Cancer Research and Detection via miRNA

Carod-Diaz,

Prince

2023

J Stud Res

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“…Multiple microRNAs are being studied as potential biomarkers for LC. For example, the expression levels of certain miRNAs were associated with specific histological tumor subtypes, patient prognosis, and response to treatment [ 14 ]. Several microRNAs are known to be involved in a number of carcinogenic pathways in LC [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Smoking-Mediated miR-301a/IRF1 Axis Controlling Immunotherapy Response in Lung Squamous Cell Carcinoma Revealed by Bioinformatic Analysis

Perevalova,

Kononchuk,

Kalinina

et al. 2024

Cancers

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Smoking is an established risk factor for a variety of malignant tumors, the most well-known of which is lung cancer. Various molecular interactions are known to link tobacco smoke exposure to lung cancer, but new data are still emerging on the effects of smoking on lung cancer development, progression, and tumor response to therapy. In this study, we reveal in further detail the previously established association between smoking and hsa-mir-301a activity in lung squamous cell carcinoma, LUSC. Using different bioinformatic tools, we identified IRF1 as a key smoking-regulated target of hsa-mir-301a in LUSC. We further confirmed this relationship experimentally using clinical LUSC tissue samples and intact lung tissue samples. Thus, increased hsa-mir-301a levels, decreased IRF1 mRNA levels, and their negative correlation were shown in LUSC tumor samples. Additional bioinformatic investigation for potential pathways impacted by such a mechanism demonstrated IRF1’s multifaceted role in controlling the antitumor immune response in LUSC. IRF1 was then shown to affect tumor immune infiltration, the expression of immune checkpoint molecules, and the efficacy of immune checkpoint blockade therapy. As a result, here we suggest a smoking-regulated mir301a/IRF1 molecular axis that could modulate the antitumor immune response and immunotherapy efficacy in LUSC, opening up novel opportunities for future research.

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Generative Adversarial Matrix Completion Network based on Multi-Source Data Fusion for miRNA–Disease Associations Prediction

Wang

Zhang

et al. 2023

Briefings in Bioinformatics

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Numerous biological studies have shown that considering disease-associated micro RNAs (miRNAs) as potential biomarkers or therapeutic targets offers new avenues for the diagnosis of complex diseases. Computational methods have gradually been introduced to reveal disease-related miRNAs. Considering that previous models have not fused sufficiently diverse similarities, that their inappropriate fusion methods may lead to poor quality of the comprehensive similarity network and that their results are often limited by insufficiently known associations, we propose a computational model called Generative Adversarial Matrix Completion Network based on Multi-source Data Fusion (GAMCNMDF) for miRNA–disease association prediction. We create a diverse network connecting miRNAs and diseases, which is then represented using a matrix. The main task of GAMCNMDF is to complete the matrix and obtain the predicted results. The main innovations of GAMCNMDF are reflected in two aspects: GAMCNMDF integrates diverse data sources and employs a nonlinear fusion approach to update the similarity networks of miRNAs and diseases. Also, some additional information is provided to GAMCNMDF in the form of a ‘hint’ so that GAMCNMDF can work successfully even when complete data are not available. Compared with other methods, the outcomes of 10-fold cross-validation on two distinct databases validate the superior performance of GAMCNMDF with statistically significant results. It is worth mentioning that we apply GAMCNMDF in the identification of underlying small molecule-related miRNAs, yielding outstanding performance results in this specific domain. In addition, two case studies about two important neoplasms show that GAMCNMDF is a promising prediction method.

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MicroRNA, mRNA, and Proteomics Biomarkers and Therapeutic Targets for Improving Lung Cancer Treatment Outcomes

Cited by 4 publications

References 99 publications

CRISPR Based Gene Editing Technologies in Cancer Research and Detection via miRNA

CRISPR Based Gene Editing Technologies in Cancer Research and Detection via miRNA

Smoking-Mediated miR-301a/IRF1 Axis Controlling Immunotherapy Response in Lung Squamous Cell Carcinoma Revealed by Bioinformatic Analysis

Generative Adversarial Matrix Completion Network based on Multi-Source Data Fusion for miRNA–Disease Associations Prediction

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