MIRS: An AI scoring system for predicting the prognosis and therapy of breast cancer

Huang, Chen; Deng, Min; Leng, Dongliang; Sun, Baoqing; Zheng, Peiyan; Zhang, Xiaohua Douglas

doi:10.1016/j.isci.2023.108322

iScience

2023

DOI: 10.1016/j.isci.2023.108322

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MIRS: An AI scoring system for predicting the prognosis and therapy of breast cancer

Chen Huang,

Min Deng,

Dongliang Leng

et al.

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2024

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Cited by 2 publications

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A Neural Network–Based Scoring System for Predicting Prognosis and Therapy in Breast Cancer

Deng,

Chen,

Qiu

et al. 2024

Current Protocols

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Breast cancer is a prevalent malignancy affecting women worldwide. Currently, there are no precise molecular biomarkers with immense potential for accurately predicting breast cancer development, which limits clinical management options. Recent evidence has highlighted the importance of metastatic and tumor‐infiltrating immune cells in modulating the antitumor therapy response. However, the prognostic value of using these features in combination, and their potential for guiding individualized treatment for breast cancer, remains vague. To address this challenge, we recently developed the metastatic and immunogenomic risk score (MIRS), a comprehensive and user‐friendly scoring system that leverages advanced bioinformatics methods to facilitate transcriptomics data analysis. To help users become familiar with the MIRS tool and apply it effectively in analyzing new breast cancer datasets, we describe detailed protocols that require no advanced programming skills. © 2024 Wiley Periodicals LLC.Basic Protocol 1: Calculating a MIRS score from transcriptomics dataBasic Protocol 2: Predicting clinical outcomes from MIRS scoresBasic Protocol 3: Evaluating treatment responses and guiding therapeutic strategies in breast cancer patientsBasic Protocol 4: Guidelines for utilizing the MIRS webtool

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A Neural Network–Based Scoring System for Predicting Prognosis and Therapy in Breast Cancer

Deng,

Chen,

Qiu

et al. 2024

Current Protocols

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Metabolic Reprogramming Induced by Aging Modifies the Tumor Microenvironment

Chen,

Wang,

Zhu

et al. 2024

Cells

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Aging is an important risk factor for tumorigenesis. Metabolic reprogramming is a hallmark of both aging and tumor initiation. However, the manner in which the crosstalk between aging and metabolic reprogramming affects the tumor microenvironment (TME) to promote tumorigenesis was poorly explored. We utilized a computational approach proposed by our previous work, MMP3C (Modeling Metabolic Plasticity by Pathway Pairwise Comparison), to characterize aging-related metabolic plasticity events using pan-cancer bulk RNA-seq data. Our analysis revealed a high degree of metabolically organized heterogeneity across 17 aging-related cancer types. In particular, a higher degree of several energy generation pathways, i.e., glycolysis and impaired oxidative phosphorylation, was observed in older patients. Similar phenomena were also found via single-cell RNA-seq analysis. Furthermore, those energy generation pathways were found to be weakened in activated T cells and macrophages, whereas they increased in exhausted T cells, immunosuppressive macrophages, and Tregs in older patients. It was suggested that aging-induced metabolic switches alter glucose utilization, thereby influencing immune function and resulting in the remodeling of the TME. This work offers new insights into the associations between tumor metabolism and the TME mediated by aging, linking with novel strategies for cancer therapy.

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MIRS: An AI scoring system for predicting the prognosis and therapy of breast cancer

Cited by 2 publications

References 78 publications

A Neural Network–Based Scoring System for Predicting Prognosis and Therapy in Breast Cancer

A Neural Network–Based Scoring System for Predicting Prognosis and Therapy in Breast Cancer

Metabolic Reprogramming Induced by Aging Modifies the Tumor Microenvironment

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