RPS27 selectively regulates the expression and alternative splicing of inflammatory and immune response genes in thyroid cancer cells

Wan, Jieqing; Juan, Lv; Wang, Chun; Zhang, Li

doi:10.17219/acem/147271

Cited by 4 publications

(2 citation statements)

References 74 publications

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Section: Discussionmentioning

confidence: 99%

“…RPS27 , as a specifical gene identified by mRMR, has been widely reported to be a housekeeping gene in the colon [ 75 ]. However, further studies showed that during pathological immune activation, RPS27 has shown to be abnormally regulated [ 75 ]. Therefore, such a gene may help recognize activated immune cells from our candidates, including plasma B cells and activated CD4 T cells.…”

Section: Discussionmentioning

confidence: 99%

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Identification of Colon Immune Cell Marker Genes Using Machine Learning Methods

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Immune cell infiltration that occurs at the site of colon tumors influences the course of cancer. Different immune cell compositions in the microenvironment lead to different immune responses and different therapeutic effects. This study analyzed single-cell RNA sequencing data in a normal colon with the aim of screening genetic markers of 25 candidate immune cell types and revealing quantitative differences between them. The dataset contains 25 classes of immune cells, 41,650 cells in total, and each cell is expressed by 22,164 genes at the expression level. They were fed into a machine learning-based stream. The five feature ranking algorithms (last absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, minimum redundancy maximum relevance, and random forest) were first used to analyze the importance of gene features, yielding five feature lists. Then, incremental feature selection and two classification algorithms (decision tree and random forest) were combined to filter the most important genetic markers from each list. For different immune cell subtypes, their marker genes, such as KLRB1 in CD4 T cells, RPL30 in B cell IGA plasma cells, and JCHAIN in IgG producing B cells, were identified. They were confirmed to be differentially expressed in different immune cells and involved in immune processes. In addition, quantitative rules were summarized by using the decision tree algorithm to distinguish candidate immune cell types. These results provide a reference for exploring the cell composition of the colon cancer microenvironment and for clinical immunotherapy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of Colon Immune Cell Marker Genes Using Machine Learning Methods

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et al. 2023

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Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

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et al. 2024

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Cardiovascular diseases (CVDs) are multifactorial diseases, requiring personalized assessment and treatment. The advancements in multi-omics technologies, namely RNA-seq and whole genome sequencing, have offered translational researchers a comprehensive view of the human genome; utilizing this data, we can reveal novel biomarkers and segment patient populations based on personalized risk factors. Limitations in these technologies in failing to capture disease complexity can be accounted for by using an integrated approach, characterizing variants alongside expression related to emerging phenotypes. Designed and implemented data analytics methodology is based on a nexus of orthodox bioinformatics, classical statistics, and multimodal artificial intelligence and machine learning techniques. Our approach has the potential to reveal the intricate mechanisms of CVD that can facilitate patient-specific disease risk and response profiling. We sourced transcriptomic expression and variants from CVD and control subjects. By integrating these multi-omics datasets with clinical demographics, we generated patient-specific profiles. Utilizing a robust feature selection approach, we reported a signature of 27 transcripts and variants efficient at predicting CVD. Here, differential expression analysis and minimum redundancy maximum relevance feature selection elucidated biomarkers explanatory of the disease phenotype. We used Combination Annotation Dependent Depletion and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVDs. Overall, we observed an XGBoost model hyperparameterized using Bayesian optimization perform the best (AUC 1.0). Using SHapley Additive exPlanations, we compiled risk assessments for patients capable of further contextualizing these predictions in a clinical setting. We discovered a 27-component signature explanatory of phenotypic differences in CVD patients and healthy controls using a feature selection approach prioritizing both biological relevance and efficiency in machine learning. Literature review revealed previous CVD associations in a majority of these diagnostic biomarkers. Classification models trained on this signature were able to predict CVD in patients with high accuracy. Here, we propose a framework generalizable to other diseases and disorders.

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Integrating single‐cell transcriptomics and machine learning to predict breast cancer prognosis: A study based on natural killer cell‐related genes

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Breast cancer (BC) is the most commonly diagnosed cancer in women globally. Natural killer (NK) cells play a vital role in tumour immunosurveillance. This study aimed to establish a prognostic model using NK cell‐related genes (NKRGs) by integrating single‐cell transcriptomic data with machine learning. We identified 44 significantly expressed NKRGs involved in cytokine and T cell‐related functions. Using 101 machine learning algorithms, the Lasso + RSF model showed the highest predictive accuracy with nine key NKRGs. We explored cell‐to‐cell communication using CellChat, assessed immune‐related pathways and tumour microenvironment with gene set variation analysis and ssGSEA, and observed immune components by HE staining. Additionally, drug activity predictions identified potential therapies, and gene expression validation through immunohistochemistry and RNA‐seq confirmed the clinical applicability of NKRGs. The nomogram showed high concordance between predicted and actual survival, linking higher tumour purity and risk scores to a reduced immune score. This NKRG‐based model offers a novel approach for risk assessment and personalized treatment in BC, enhancing the potential of precision medicine.

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RPS27 selectively regulates the expression and alternative splicing of inflammatory and immune response genes in thyroid cancer cells

Abstract: RPS27 selectively regulates the expression and alternative splicing of inflammatory and immune response genes in thyroid cancer cells [published online as ahead of print on May 12, 2022].

Cited by 4 publications

References 74 publications

Identification of Colon Immune Cell Marker Genes Using Machine Learning Methods

Identification of Colon Immune Cell Marker Genes Using Machine Learning Methods

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

Integrating single‐cell transcriptomics and machine learning to predict breast cancer prognosis: A study based on natural killer cell‐related genes

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