AIMC Topic: Gene Expression Regulation, Neoplastic

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Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration.

Annals of medicine
OBJECTIVE: Diagnosis of hepatocellular carcinoma (HCC) remains challenging for clinicians. Machine learning approaches and big data analyses are viable strategies for identifying HCC diagnostic markers.

Machine learning-based integration develops a disulfidptosis-related lncRNA signature for improving outcomes in gastric cancer.

Artificial cells, nanomedicine, and biotechnology
Gastric cancer remains one of the deadliest cancers globally due to delayed detection and limited treatment options, underscoring the critical need for innovative prognostic methods. Disulfidptosis, a recently discovered programmed cell death trigger...

Decoding Dendritic Cell Subtypes via Integrated Radiogenomics: A Stacked Ensemble Model for Predicting Immunotherapy Response in NSCLC.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
We pioneer a multimodal framework integrating single-cell RNA sequencing (scRNA-seq), radiomics, and deep learning to decipher dendritic cell (DC)-mediated mechanisms underlying anti-PD-1 response in non-small cell lung cancer (NSCLC). Single-cell RN...

Interplay of PRMTs and Identification of Biomarkers Through Machine Learning Algorithms in Pan-Cancer, Highlighting PRMT3 as a Biomarker in Pancreatic Cancer.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Protein arginine methylation was a common post-translational modification, playing a key role in many biological processes and disease. But the regulatory mechanisms of protein arginine methyltransferases (PRMTs) in cancer were not well understood. T...

Comprehensive Analysis of Epigenetic Signatures in Non-Small Cell Lung Cancer: Development and Validation of an Epigenetics-Based Prognostic Model for Drug Sensitivity Prediction.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Non-small cell lung cancer (NSCLC) exhibits complex epigenetic dysregulation that impacts treatment response and prognosis, yet comprehensive analysis linking epigenetic signatures to clinical outcomes remains limited. We integrated single-cell RNA s...

Identification and validation of feature genes in hepatocellular carcinoma based on bioinformatics and machine learning: An observational study.

Medicine
The incidence of hepatocellular carcinoma (HCC) has risen significantly in recent years, while current diagnostic and therapeutic approaches remain suboptimal. This study aimed to identify novel biomarkers and therapeutic targets to improve early det...

Genetic Control of tRNA-Derived Fragments Contributes to Cancer Risk.

Cancer research
UNLABELLED: tRNA-derived fragments (tRF) are a class of small noncoding RNAs that have exhibited several functions in cancer. Recent studies have shown that mutations in tRNA genes can lead to global changes in tRF expression levels and may affect tR...

Heterogeneous Driving Effects Guide Personalized Tumor Treatments Targeting N6-Methyladenosine.

Cancer research
UNLABELLED: Alterations to N6-methyladenosine (m6A) modifications can promote malignant progression by modulating gene expression through regulation of transcript metabolism. Quantifying the causal impact of m6A dysregulation at the population level ...

Leveraging machine learning and single-cell RNA sequencing strategies to develop a risk prognosis scoring based on liquid-liquid phase separation feature genes in pediatric hepatoblastoma.

Computers in biology and medicine
BACKGROUND: Considerable evidence highlights the intricate association between liquid-liquid phase separation (LLPS) and tumorigenesis, progression, and therapy resistance. However, there has been limited exploration of the role of LLPS in hepatoblas...