constructing-visualizing-models-using-mime-based-machine-learning

Constructing and Visualizing Models using Mime-based Machine-learning Framework

The widespread high-throughput sequencing technology has significantly enhanced our understanding of biology and cancer heterogeneity. Machine learning ...

Central South University

Furong Laboratory

The optimal choice for first- and second-line antiseizure medications for pediatric patients with convulsive status epilepticus remains ambiguous. The present study aimed to estimate the comparative effect on the efficacy and safety of different antiseizure medications in pediatric patients with status epilepticus and provide evidence for clinical practice.

Preferential Antiseizure Medications in Pediatric Patients with Convulsive Status Epilepticus: A Systematic Review and Network Meta-Analysis.

To compare the efficacy and compliance of up-to-date disease modifying therapies (DMTs) in patients with remitting-relapsing MS (RRMS).

Disease modifying therapies in relapsing-remitting multiple sclerosis: A systematic review and network meta-analysis.

The prognostic factors and optimal treatment for the elderly patient with glioblastoma (GBM) were poorly understood. This study extracted 4975 elderly patients (≥ 65 years old) with histologically confirmed GBM from Surveillance, Epidemiology and End Results (SEER) database. Firstly, Cumulative incidence function and cox proportional model were utilized to illustrate the interference of non-GBM related mortality in our cohort. Then, the Fine-Gray competing risk model was applied to determine the prognostic factors for GBM related mortality. Age ≥ 75 years old, white race, size > 5.4 cm, frontal lobe tumor, and overlapping lesion were independently associated with more GBM related death, while Gross total resection (GTR) (HR 0.87, 95%CI 0.80-0.94, P = 0.010), radiotherapy (HR 0.64, 95%CI 0.55-0.74, P < 0.001), chemotherapy (HR 0.72, 95%CI 0.59-0.90, P = 0.003), and chemoRT (HR 0.43, 95%CI 0.38-0.48, P < 0.001) were identified as independently protective factors of GBM related death. Based on this, a corresponding nomogram was conducted to predict 3-, 6- and 12-month GBM related mortality, the C-index of which were 0.763, 0.718, and 0.694 respectively. The calibration curve showed that there was a good consistency between the predicted and the actual mortality probability. Concerning treatment options, GTR followed by chemoRT is suggested as optimal treatment. Radiotherapy and chemotherapy alone also provide moderate clinical benefits.

Competing risk model to determine the prognostic factors and treatment strategies for elderly patients with glioblastoma.

Myotonic dystrophy (DM), the most common muscular dystrophy in adults, is a group of autosomal inherited neuromuscular disorders characterized by progressive muscle weakness, myotonia, and cardiac conduction abnormalities. Due to the different gene mutations, DM has been subclassified into DM type 1 (DM1) and type 2 (DM2). However, the prevalence studies on DM and its subtypes are insufficient.

Global Prevalence of Myotonic Dystrophy: An Updated Systematic Review and Meta-Analysis.

Transcriptional programs are often dysregulated in cancers. A comprehensive investigation of potential regulons is critical to the understanding of tumorigeneses. We first constructed the regulatory networks from single-cell RNA sequencing data in human lung adenocarcinoma (LUAD). We next introduce LPRI (Lung Cancer Prognostic Regulon Index), a precision oncology framework to identify new biomarkers associated with prognosis by leveraging the single cell regulon atlas and bulk RNA sequencing or microarray datasets. We confirmed that LPRI could be a robust biomarker to guide prognosis stratification across lung adenocarcinoma cohorts. Finally, a multi-omics data analysis to characterize molecular alterations associated with LPRI was performed from The Cancer Genome Atlas (TCGA) dataset. Our study provides a comprehensive chart of regulons in LUAD. Additionally, LPRI will be used to help prognostic prediction and developing personalized treatment for future studies.

Integration of single-cell regulon atlas and multi-omics data for prognostic stratification and personalized treatment prediction in human lung adenocarcinoma.

Immunotherapies targeting immune checkpoints have gained increasing attention in cancer treatment, emphasizing the need for predictive biomarkers. Circular RNAs (circRNAs) have emerged as critical regulators of tumor immunity, particularly in the PD-1/PD-L1 pathway, and have shown potential in predicting immunotherapy efficacy. Yet, the detailed roles of circRNAs in cancer immunotherapy are not fully understood. While existing databases focus on either circRNA profiles or immunotherapy cohorts, there is currently no platform that enables the exploration of the intricate interplay between circRNAs and anti-tumor immunotherapy. A comprehensive resource combining circRNA profiles, immunotherapy responses, and clinical outcomes is essential to advance our understanding of circRNA-mediated tumor-immune interactions and to develop effective biomarkers.

TCCIA: a comprehensive resource for exploring CircRNA in cancer immunotherapy.

Glioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment. The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. After collecting 18 RCD-related signatures from the opening literature, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. Here, based on bulk and single-cell sequencing samples, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed an RCD gene pair scoring system, named RCD.GP signature, showing a reliable and robust performance in predicting the prognosis of glioblastoma. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified that the SLC43A3 highly expressed in glioma grades and glioma cell lines through qRT-PCR. Our study provided comprehensive insights into the RCD roles in glioma, developed a robust RCD gene pair signature for predicting the prognosis of glioma patients, constructed a machine learning framework for screening the core candidates and identified the SLC43A3 as an oncogenic role and a prediction biomarker in glioblastoma.

Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients.

Regulated cell death (RCD) plays a pivotal role in various biological processes, including development, tissue homeostasis, and immune response. However, a comprehensive assessment of RCD status and its associated features at the pan-cancer level remains unexplored. Furthermore, despite significant advancements in immune checkpoint inhibitors (ICI), only a fraction of cancer patients currently benefit from treatments. Given the emerging evidence linking RCD and ICI efficacy, we hypothesize that the RCD status could serve as a promising biomarker for predicting the ICI response and overall survival (OS) in patients with malignant tumors. We defined the RCD levels as the RCD score, allowing us to delineate the RCD landscape across 30 cancer types, 29 normal tissues in bulk, and 2,573,921 cells from 82 scRNA-Seq datasets. By leveraging large-scale datasets, we aimed to establish the positive association of RCD with immunity and identify the RCD signature. Utilizing 7 machine-learning algorithms and 18 ICI cohorts, we developed an RCD signature (RCD.Sig) for predicting ICI response. Additionally, we employed 101 combinations of 10 machine-learning algorithms to construct a novel RCD survival-related signature (RCD.Sur.Sig) for predicting OS. Furthermore, we obtained CRISPR data to identify potential therapeutic targets. Our study presents an integrative framework for assessing RCD status and reveals a strong connection between RCD status and ICI effectiveness. Moreover, we establish two clinically applicable signatures and identify promising potential therapeutic targets for patients with tumors.

Pan-cancer evaluation of regulated cell death to predict overall survival and immune checkpoint inhibitor response.

Gliomas, the most lethal tumours in brain, have a poor prognosis despite accepting standard treatment. Limited benefits from current therapies can be attributed to genetic, epigenetic and microenvironmental cues that affect cell programming and drive tumour heterogeneity. Through the analysis of Hi-C data, we identified a potassium-chloride co-transporter SLC12A5 associated with disrupted topologically associating domain which was downregulated in tumour tissues. Multiple independent glioma cohorts were included to analyse the characterization of SLC12A5 and found it was significantly associated with pathological features, prognostic value, genomic alterations, transcriptional landscape and drug response. We constructed two SLC12A5 overexpression cell lines to verify the function of SLC12A5 that suppressed tumour cell proliferation and migration in vitro. In addition, SLC12A5 was also positively associated with GABA receptor activity and negatively associated with pro-tumour immune signatures and immunotherapy response. Collectively, our study provides a comprehensive characterization of SLC12A5 in glioma and supports SLC12A5 as a potential suppressor of disease progression.

A potassium-chloride co-transporter with altered genome architecture functions as a suppressor in glioma.

The widespread use of high-throughput sequencing technologies has revolutionized the understanding of biology and cancer heterogeneity. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients' outcome and clinical response. However, an open-source R package covering state-of-the-art machine-learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct a machine learning-based integration model with elegant performance (Mime). Mime streamlines the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting the outcomes of patients compared with other published models. Furthermore, the PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from the PIEZO1-associated signatures demonstrated high potential as a glioma target. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields. The Mime package is available on GitHub (https://github.com/l-magnificence/Mime).

Article	Year
Preferential Antiseizure Medications in Pediatric Patients with Convulsive Status Epilepticus: A Systematic Review and Network Meta-Analysis. Clinical drug investigation\| PubMed ID: 33145680	2021
Disease modifying therapies in relapsing-remitting multiple sclerosis: A systematic review and network meta-analysis. Autoimmunity reviews\| PubMed ID: 33878488	2021
Competing risk model to determine the prognostic factors and treatment strategies for elderly patients with glioblastoma. Scientific reports\| PubMed ID: 33927308	2021
Global Prevalence of Myotonic Dystrophy: An Updated Systematic Review and Meta-Analysis. Neuroepidemiology\| PubMed ID: 35483324	2022
Integration of single-cell regulon atlas and multi-omics data for prognostic stratification and personalized treatment prediction in human lung adenocarcinoma. Journal of translational medicine\| PubMed ID: 37491302	2023
TCCIA: a comprehensive resource for exploring CircRNA in cancer immunotherapy. Journal for immunotherapy of cancer\| PubMed ID: 38212124	2024
Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients. Scientific reports\| PubMed ID: 38378721	2024
Pan-cancer evaluation of regulated cell death to predict overall survival and immune checkpoint inhibitor response. NPJ precision oncology\| PubMed ID: 38538696	2024
A potassium-chloride co-transporter with altered genome architecture functions as a suppressor in glioma. Journal of cellular and molecular medicine\| PubMed ID: 38685685	2024
Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection. Computational and structural biotechnology journal\| PubMed ID: 39055398	2024

Yihao Zhang

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