AIMC Topic: Biomarkers, Tumor

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Single cell and machine learning identify type II pneumocyte-derived biomarkers HN1/OCIAD2/SFTA2 for non-small cell lung cancer prognosis and immune regulation.

European journal of medical research
BACKGROUND: Non-small cell lung cancer (NSCLC) is one of the most prevalent malignancies and currently shows a poor clinical prognosis. Type II pneumocyte, as one of the main sources of cancer cells in NSCLC, is important to explore the molecular fun...

A macro-micro-macro radiogenomic framework identifies FIBCD1 as a key immune-modulating biomarker in breast cancer.

Journal of translational medicine
BACKGROUND: Breast cancer prognosis remains challenging due to tumor heterogeneity and the limited predictive power of conventional clinical models. Integrating imaging features with molecular data may improve individualized risk stratification and c...

Cell-free DNA methylation and fragmentomics-based liquid biopsy for accurate esophageal cancer detection.

BMC cancer
BACKGROUND: Cell-free DNA is a promising source of biomarkers for early cancer detection and carries tumor-driven methylation and fragmentation features that have achieved good diagnostic efficacy across various cancers. However, there were no studie...

Machine learning driven multiomics analysis identifies disulfidptosis associated molecular subtypes in ovarian cancer.

Scientific reports
Precision oncology enables molecularly guided cancer therapy through multi-omics profiling, AI-driven classification, and biomarker-targeted interventions. Disulfidptosis has emerged as a promising therapeutic target, yet no ovarian cancer classifica...

Identification of novel biomarkers for epithelial ovarian cancer through machine learning and explainable artificial intelligence using in silico and in vitro analysis.

Scientific reports
Epithelial ovarian cancer (EOC) is a lethal gynecological malignancy. Ongoing research aimed to identify novel biomarkers and develop combined algorithms to improve diagnosis and prognosis prediction for EOC. RNA-seq related to EOC were obtained from...

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...

MarkerPredict: predicting clinically relevant predictive biomarkers with machine learning.

NPJ systems biology and applications
Precision oncology relies on predictive biomarkers for selecting targeted cancer therapies. Network-based properties of proteins, together with structural features such as intrinsic disorder, are likely to shape their potential as biomarkers. We ther...

Multi-omics strategies for biomarker discovery and application in personalized oncology.

Molecular biomedicine
Multi-omics strategies, integrating genomics, transcriptomics, proteomics, and metabolomics, have revolutionized biomarker discovery and enabled novel applications in personalized oncology. Despite rapid technological developments, a comprehensive sy...

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...