AIMC Topic: Biomarkers, Tumor

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Machine learning-driven classification and prognostic prediction of kidney renal clear cell carcinoma using APOBEC family expression signatures.

Scientific reports
Apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like (APOBEC) cytidine deaminases are a highly evolutionarily conserved protein family. Their members are closely associated with DNA damage repair and involved in the genesis and progressio...

Serum-MiR-CanPred: deep learning framework for pan-cancer classification and miRNA-targeted drug discovery.

RNA biology
Cancer diagnosis at an early stage is crucial for improving overall health outcomes. However, existing cancer diagnostic techniques are mostly invasive and tend to identify the disease only in its advanced stages. MicroRNAs (miRNAs), which are small ...

Machine learning-enhanced direct mass spectrometry analysis of non-volatile breath metabolites for rapid and accurate lung cancer screening.

Analytical methods : advancing methods and applications
Breath analysis by direct mass spectrometry faces significant challenges due to the inherent complexities in sample collection, low analyte concentrations, and accurate compound identification. While current breath analysis primarily focuses on volat...

Comprehensive analysis of coagulation-associated gene signature in bladder cancer diagnosis, prognosis, and immunotherapy.

Scientific reports
In recent years, research on the relationship between coagulation system abnormalities and tumor immunity has been widely reported. Bladder cancer (BC), as an immunogenic tumor, holds great promise in immunotherapy. The role of coagulation-related ge...

MX1 is a novel crucial prognostic and therapeutic target inducing chemoresistance in right-sided colon cancer: insights from machine learning-based multi-omics analysis.

Human genomics
BACKGROUND: Recent studies have increasingly emphasized the poorer survival outcomes and reduced treatment responses associated with right-sided colon cancer (RCC). However, the underlying molecular mechanisms remain poorly understood. This study aim...

Deciphering lactate/lactylation networks in AML: integrated scRNA-seq and transcriptomics reveal functions and prognostic model.

BMC cancer
Acute myeloid leukemia (AML) exhibits pronounced heterogeneity, necessitating deep molecular characterization for precision therapy. Lactate metabolism and histone lactylation, influencing tumor biology via epigenetic regulation and immune microenvir...

Identification and analysis of metabolic reprogramming-related genes in triple-negative breast cancer.

Clinical and experimental medicine
Triple-negative breast cancer (TNBC) is notorious for its rapid progression, tendency to metastasize, high recurrence rates, dismal outcomes, and limited treatment options, underscoring the urgent need to uncover new biomarkers and molecular pathways...

Ligand-receptor interaction profiling as a predictive biomarker for anti-PD-1 therapy response in melanoma.

Clinical and experimental medicine
Cell-to-cell communication through ligand-receptor (LR) interactions can fundamentally shape the tumor microenvironment and immune responses, but the full spectrum of these interactions in anti-PD-1 therapy remains unexplored. We developed a predicti...

Machine learning identifies exosome related gene signatures for early prediction of non-small cell lung cancer.

Scientific reports
Non-small cell lung cancer (NSCLC) remains a major health challenge worldwide, mainly due to the lack of effective early diagnostic biomarkers. Exosome-related genes have recently emerged as potential diagnostic markers due to their roles in tumor pr...