AIMC Topic: Biomarkers, Tumor

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A machine-learning informed circulating microbial DNA signature for early diagnosis of esophageal adenocarcinoma.

Gut microbes
Esophageal adenocarcinoma (EAC) has seen a dramatic rise in incidence in developed countries over the past three decades. Early detection of its precursors-gastroesophageal reflux disease (GERD), Barrett's esophagus (BE), and high-grade dysplasia (HG...

Entropy-driven signal amplification integrated with machine learning in multiplex lateral flow immunoassay for sensitive Point-of-Care colon cancer diagnosis.

Journal of nanobiotechnology
Investigations on epithelial-mesenchymal transition (EMT) events occurring on circulating tumor cells (CTCs) are poised to significantly advance nanoliquid biopsy methodologies. This study presented a colorimetric multiplex lateral flow immunoassay s...

A novel prognostic model for lung squamous cell carcinoma based on multi-omics analysis and machine learning.

PloS one
Lung squamous-cell carcinoma (LUSC) is a highly aggressive malignancy with a poor prognosis. Tertiary lymphoid structures (TLS) play a crucial role in the immune response and significantly influence the efficacy of immunotherapy. However, the prognos...

Predicting the influence of homologous recombination repair deficiency genes on glioma heterogeneity and patient prognosis using multi-omics analysis and machine learning.

PloS one
BACKGROUND: Glioma is the most common malignant tumor of the central nervous system, and homologous recombination deficiency (HRD) may play a crucial role in its progression. Our study aimed to predict the impact of HRD on glioma heterogeneity and pa...

The role of gut microbiota in breast cancer: biomarker identification and therapeutic applications.

Antonie van Leeuwenhoek
Recent studies have established the gut microbiome as a crucial player in breast cancer diagnosis, progression, and treatment. Distinct microbial patterns have shown promise as non-invasive diagnostic and prognostic biomarkers, supporting patient str...

Machine Learning-Driven Extracellular Vesicles Peptidomics Powers Precision Classification of Endometrial Cancer.

Analytical chemistry
Endometrial cancer (EC) molecular subtyping is critical for prognosis and treatment but remains hindered by reliance on invasive tissue biopsies and time-consuming genomic sequencing. Here, we present a minimally invasive approach integrating MALDI-T...

LAC-TME classifier: machine learning-driven model predicts survival and prioritizes targeted therapy in clear cell renal cell carcinoma.

Journal of cancer research and clinical oncology
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a major type of kidney cancer, making up about 80% of cases, with advanced stages showing low survival rates. Current treatments face challenges like toxicity and drug resistance. Studies indicat...

Unravelling TPX2-centered co-expression networks as key drivers of aggressive prostate cancer.

Scientific reports
Prostate cancer (PCa) progression is driven by complex molecular reprogramming, yet distinguishing indolent from aggressive disease remains a challenge. We performed an integrative transcriptomic analysis of 1232 PCa samples spanning normal prostate ...

Proteomics-Driven Cancer Biomarkers for Early Detection and Targeted Therapy: Insights from the Middle East.

Journal of proteome research
Proteomics has become a transformative tool in oncology, offering unique opportunities for early detection, diagnosis, and cancer stratification. By enabling large-scale analysis of protein expression, interactions, and post-translational modificatio...

The alternative splicing landscape of hepatocellular carcinoma and its potential for HCC detection.

Hepatology communications
BACKGROUND: Pre-mRNA alternative splicing contributes to oncogenic gene expression in hepatocellular carcinoma (HCC), and some oncogenic isoforms escape the tumor into circulation. This study aimed to characterize the alternative splicing landscape o...