AIMC Topic: Gene Expression Regulation, Neoplastic

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MZB1-Driven Endoplasmic reticulum stress model as a predictor of breast cancer progression and survival.

Functional & integrative genomics
Endoplasmic reticulum (ER) stress and its associated unfolded protein response (UPR) have been demonstrated to play a crucial role in cancer's progression, but their prognostic significance in breast cancer (BC) remains unclear. In this study, a reli...

Machine learning-based identification of diagnostic and prognostic mitotic cell cycle genes in hepatocellular carcinoma.

PloS one
Mitotic cell cycle (MCC) is a critical process in cell growth and division, and dysregulation of MCC genes may contribute to tumorigenesis. In this study, to identify diagnostic and prognostic value of MCC genes, differentially expressed MCC genes be...

Environmental exposure to perfluorooctane sulfonate and its role in esophageal cancer progression: a comprehensive bioinformatics and experimental study.

Scientific reports
Esophageal cancer (ESCA) is a significant malignancy with rising global incidence rates and considerable impacts on patient survival and quality of life. Current diagnostic and therapeutic strategies face limitations, necessitating research into its ...

Machine learning-assisted radiogenomic analysis for miR-15a expression prediction in renal cell carcinoma.

BMC cancer
BACKGROUND: Renal cell carcinoma (RCC) is a prevalent malignancy with highly variable outcomes. MicroRNA-15a (miR-15a) has emerged as a promising prognostic biomarker in RCC, linked to angiogenesis, apoptosis, and proliferation. Radiogenomics integra...

Machine learning model for early diagnosis of breast cancer based on PiRNA expression with CA153.

Scientific reports
PIWI-interacting RNAs (piRNAs) have been implicated in the biological processes of various cancers. This study aimed to investigate the diagnostic potential of circulating piRNAs in breast cancer (BC) using machine learning (ML) frameworks. A serum t...

Integrating miRNA profiling and machine learning for improved prostate cancer diagnosis.

Scientific reports
Prostate cancer (PCa) diagnosis remains challenging due to overlapping clinical features with benign prostatic hyperplasia (BPH) and limitations of existing diagnostic tools like PSA tests, which yield high false-positive rates. This study investigat...

Anoikis-related genes predicts prognosis and therapeutic response in renal cell carcinoma.

Annals of medicine
BACKGROUND: Metastasis represents the primary cause of cancer-related mortality, with a high incidence observed in renal cell carcinoma (RCC). Anoikis, a specialized form of apoptosis, plays a crucial role in preventing displaced cells from adhering ...

Machine learning-based construction of Immunogenic cell death-related score for improving prognosis and personalized treatment in glioma.

Scientific reports
Immunogenic cell death (ICD) is capable of activating both innate and adaptive immune responses. In this study, we aimed to develop an ICD-related signature in glioma patients and facilitate the assessment of their prognosis and drug sensitivity. Con...

Screening, Validation, and Machine Learning-Based Evaluation of Serum Protein Biomarkers for Esophageal Squamous Cell Carcinoma Based on Single-Cell Subtype-Specific Genes.

Journal of proteome research
Cellular heterogeneity of epithelial cells and fibroblasts is critical in esophageal squamous cell carcinoma development (ESCC). Identifying dysregulated subtype-specific genes in these cells is essential for early diagnosis and treatment. In this st...