AIMC Topic: Immunotherapy

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Autoencoder techniques for survival analysis on renal cell carcinoma.

PloS one Jan 1, 2025
Survival is the gold standard in oncology when determining the real impact of therapies in patients outcome. Thus, identifying molecular predictors of survival (like genetic alterations or transcriptomic patterns of gene expression) is one of the mos...
Sunitinib Proportional Hazards Models Carcinoma, Renal Cell Autoencoder Axitinib Humans Transcriptome Immunotherapy Antibodies, Monoclonal, Humanized Progression-Free Survival Survival Analysis Kidney Neoplasms
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Development of an immune-related gene signature applying Ridge method for improving immunotherapy responses and clinical outcomes in lung adenocarcinoma.

PeerJ Jan 1, 2025
BACKGROUND: Lung adenocarcinoma (LUAD) is a major cause of cancer mortality. Considering the critical role of tumor infiltrating lymphocytes in effective immunotherapy, this study was designed to screen molecular markers related to tumor infiltrating...
Humans Prognosis Female Lung Neoplasms Immunotherapy Biomarkers, Tumor Gene Expression Regulation, Neoplastic Male Lymphocytes, Tumor-Infiltrating Gene Expression Profiling Adenocarcinoma of Lung Transcriptome
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Artificial Intelligence-Guided Identification of IGFBP7 as a Critical Indicator in Lactic Metabolism Determines Immunotherapy Response in Stomach Adenocarcinoma.

Journal of cellular and molecular medicine Jan 1, 2025
Due to considerable tumour heterogeneity, stomach adenocarcinoma (STAD) has a poor prognosis and varies in response to treatment, making it one of the main causes of cancer-related mortality globally. Recent data point to a significant role for metab...
Artificial Intelligence Prognosis Stomach Neoplasms Biomarkers, Tumor Humans Insulin-Like Growth Factor Binding Proteins Cell Proliferation Adenocarcinoma Male Cell Movement Cell Line, Tumor Lactic Acid Immunotherapy Gene Expression Regulation, Neoplastic
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Deciphering the Role of SLFN12: A Novel Biomarker for Predicting Immunotherapy Outcomes in Glioma Patients Through Artificial Intelligence.

Journal of cellular and molecular medicine Dec 1, 2024
Gliomas are the most prevalent form of primary brain tumours. Recently, targeting the PD-1 pathway with immunotherapies has shown promise as a novel glioma treatment. However, not all patients experience long-lasting benefits, underscoring the necess...
Cell Line, Tumor Immunotherapy Gene Expression Regulation, Neoplastic Cell Proliferation Prognosis Tumor Microenvironment Biomarkers, Tumor Brain Neoplasms Treatment Outcome Glioma Programmed Cell Death 1 Receptor Humans Artificial Intelligence
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Unveiling Varied Cell Death Patterns in Lung Adenocarcinoma Prognosis and Immunotherapy Based on Single-Cell Analysis and Machine Learning.

Journal of cellular and molecular medicine Nov 1, 2024
Programmed cell death (PCD) pathways hold significant influence in the etiology and progression of a variety of cancer forms, particularly offering promising prognostic markers and clues to drug sensitivity for lung adenocarcinoma (LUAD) patients. We...
Lung Neoplasms Immunotherapy Gene Expression Regulation, Neoplastic Apoptosis Cell Death Adenocarcinoma of Lung Biomarkers, Tumor Machine Learning Male Prognosis Cell Line, Tumor Humans Female Single-Cell Analysis Tumor Microenvironment
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Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.

Journal of cellular and molecular medicine Jul 1, 2024
Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogeneity, yet research on this aspect in cutaneous melanoma remains limited. In this study, we utilized single-cell data from 92,521 cells to explore the tu...
Single-Cell Analysis Immunotherapy Cluster Analysis Gene Expression Regulation, Neoplastic Humans Melanoma, Cutaneous Malignant Melanoma Cell Proliferation Machine Learning Skin Neoplasms Prognosis Biomarkers, Tumor Cell Line, Tumor
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Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature.

Journal of cellular and molecular medicine Jul 1, 2024
Lung adenocarcinoma (LUAD) is a leading cause of cancer-related deaths, and improving prognostic accuracy is vital for personalised treatment approaches, especially in the context of immunotherapy. In this study, we constructed an artificial intellig...
CDC2-CDC28 Kinases Cell Proliferation Prognosis Neoplastic Stem Cells Biomarkers, Tumor Cell Movement Gene Expression Profiling Adenocarcinoma of Lung Transcriptome Cell Line, Tumor Lung Neoplasms Humans Immunotherapy Artificial Intelligence Gene Expression Regulation, Neoplastic
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Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers.

Journal of cellular and molecular medicine Jul 1, 2024
The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (IAC) involves a complex evolution of tumour cell clusters, the mechanisms of which remain largely unknown. By integrating single-cel...
Humans Machine Learning Prognosis Tumor Microenvironment Biomarkers, Tumor Disease Progression Gene Expression Profiling Adenocarcinoma of Lung Single-Cell Analysis Lung Neoplasms Immunotherapy Gene Expression Regulation, Neoplastic
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Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi-omics consensus machine learning approach.

Journal of cellular and molecular medicine Jul 1, 2024
Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, there is a dearth of precise, individualized treatment plans. We integrated mR...
Biomarkers, Tumor Gene Expression Profiling Lung Neoplasms Humans MicroRNAs Multiomics Immunotherapy Machine Learning Adenocarcinoma of Lung Gene Expression Regulation, Neoplastic Prognosis
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Improving Prediction of Survival and Progression in Metastatic Non-Small Cell Lung Cancer After Immunotherapy Through Machine Learning of Circulating Tumor DNA.

JCO precision oncology Jun 1, 2024
PURPOSE: To use modern machine learning approaches to enhance and automate the feature extraction from the longitudinal circulating tumor DNA (ctDNA) data and to improve the prediction of survival and disease progression, risk stratification, and tre...
Immunotherapy Male Antibodies, Monoclonal, Humanized Circulating Tumor DNA Humans Middle Aged Carcinoma, Non-Small-Cell Lung Machine Learning Disease Progression Progression-Free Survival Female Lung Neoplasms Aged
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