AIMC Topic: Gene Expression Regulation, Neoplastic

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A SWI/SNF complex-related genes signature predicts prognosis and immune infiltration in ccRCC with KCNK5 as a novel biomarker.

Scientific reports
Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC). Although we have made many achievements in the therapy of RCC with the progress of medicine, the clinical management of metastatic RCC remains a dauntin...

Machine learning-enhanced discovery of a basement membrane-related gene signature in glioblastoma via single-cell and Spatial transcriptomics.

Journal of translational medicine
BACKGROUND: The complex invasiveness and heterogeneity of glioblastoma multiforme (GBM) hinder the complete eradication of the tumor. The invasion of the basement membrane (BM) occurs before the spread to the meninges and the metastasis of glioma cel...

Constructing a sixteen lactate-related gene risk signature for LUAD to predict the prognosis and TME by machine learning.

Scientific reports
Although it is the most common subtype of lung cancer in clinical practice, lung adenocarcinoma (LUAD) was proven to be associated with a poor prognosis. In recent years, lactate metabolism has been considered an important biological mechanism in lun...

Macrophage mitophagy-related genes predict prognosis and therapeutic response in lung adenocarcinoma.

Scientific reports
Mitochondrial autophagy (mitophagy) in macrophages is crucial yet poorly understood within the lung adenocarcinoma (LUAD) tumor microenvironment. This study aimed to identify key macrophage mitophagy-related genes and develop a robust prognostic mode...

Regulators of homologous recombination deficiency identified by machine learning using somatic multi-omics data.

Life science alliance
Homologous recombination deficiency (HRD) is a critical biomarker for guiding targeted therapies, yet the full range of somatic alterations driving HRD across cancers remains incompletely characterized. Here, we present a tumor-agnostic machine learn...

Construction and validation of gene signature for prognosis and drug sensitivity in cholangiocarcinoma based on cellular senescence related genes.

Scientific reports
Cholangiocarcinoma is a very deadly epithelial cell cancer with poor clinical outcome. Cellular senescence plays a vital role in the oncogenesis and the aggressiveness of cholangiocarcinoma. Integrative machine learning procedure including 10 methods...

Biologically explainable multi-omics feature demonstrates greater learning potential by identifying tissue of origin, stages, and subtypes for pan-cancer classification.

Scientific reports
Cancer is a complex disease characterized by uncontrolled cell growth, which can invade surrounding tissues and spread to distant organs. Most of the conventional methods of diagnosis fails to identify the primary organ when cancer spreads to other o...

Integrated machine learning and single-cell analysis identify chromatin-remodeling gene signature for diagnosis and prognosis in nasopharyngeal carcinoma.

Clinical and experimental medicine
This study examines the function of chromatin-remodeling genes (CRGs) in nasopharyngeal carcinoma (NPC), with an emphasis on their potential as prognostic and diagnostic biomarkers. We examined gene expression information collected from multiple data...

Multi-omics unravel heterogeneity of glucose metabolism reprogramming in gastric cancer.

Clinical and experimental medicine
Gastric cancer (GC) presents striking survival disparities: 85-100% for early-stage versus only 5-20% for advanced disease. Glucose metabolic reprogramming (GMS)-a cancer hallmark linked to the Warburg effect-fuels tumor progression and immune evasio...

Graph attention networks-based prediction of MicroRNA-disease causality in head and neck neoplasms.

Scientific reports
Head and neck cancers represent a critical global health issue, contributing to substantial morbidity and mortality. Recent research has explored the role of microRNAs (miRNAs) in these cancers by constructing miRNA-associated disease networks using ...