AIMC Topic: Biomarkers, Tumor
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Uncovering subtype-specific metabolic signatures in breast cancer through multimodal integration, attention-based deep learning, and self-organizing maps.
Scientific reports
Jul 1, 2025
This study integrates multimodal metabolomic data from three platforms-LC-MS, GC-MS, and NMR-to systematically identify biomarkers distinguishing breast cancer subtypes. A feedforward attention-based deep learning model effectively selected 99 signif...
Fabrication of serum-based SERS-tailored 3D structures for thyroid cancer diagnosis.
Scientific reports
Jul 1, 2025
Early detection of thyroid cancer improves patient survival rate from 51.9% to 99.9%. Fine needle aspiration cytology is the primary method for diagnosing thyroid cancer; however, this method is associated with limitations, including diagnostic uncer...
Machine learning developed LKB1-AMPK signaling related signature for prognosis and drug sensitivity in hepatocellular carcinoma.
Scientific reports
Jul 1, 2025
Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide, posing a significant threat to the life and health of people globally. LKB1-AMPK signaling pathway plays a significant role in the regulation of cellular metabolism, prolifera...
Identification and verification of immune and oxidative stress-related diagnostic indicators for malignant lung nodules through WGCNA and machine learning.
Scientific reports
Jul 1, 2025
Early detection of lung nodules (LNs) is critical for prevention and treatment of lung cancer. However, current noninvasive diagnostic methods face significant challenges in reliably distinguishing benign from malignant nodules. Thus, there is an urg...
Prognostic model of lung adenocarcinoma from the perspective of cancer-associated fibroblasts using single-cell and bulk RNA-sequencing.
Scientific reports
Jul 1, 2025
Cancer-associated fibroblasts (CAFs) play important roles in the progression of lung adenocarcinoma (LUAD). We examined CAF subgroups via gene ontology, pseudo-time, and cell communication analyses and explored their prognostic value in LUAD using a ...
Harnessing artificial intelligence for detection of pancreatic cancer: a machine learning approach.
Clinical and experimental medicine
Jul 1, 2025
PURPOSE: Pancreatic cancer (PC) is one of the most lethal malignancies, often presenting with nonspecific symptoms and a dismal prognosis. Despite advancements in treatments, the 5-year survival rate remains low, highlighting the urgent need for effe...
Developing a novel medulloblastoma diagnostic with miRNA biomarkers and machine learning.
Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Jun 30, 2025
BACKGROUND: Medulloblastoma (MB) is the most common malignant brain tumor in children. Current diagnostic methods, such as MRI and lumbar puncture, are invasive and not sensitive enough, making early diagnosis challenging. MicroRNAs (miRNAs) have eme...
3Mont: A multi-omics integrative tool for breast cancer subtype stratification.
PloS one
Jun 27, 2025
Breast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types,...
Innovative technologies and their clinical prospects for early lung cancer screening.
Clinical and experimental medicine
Jun 18, 2025
BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide, due to lacking effective early-stage screening approaches. Imaging, such as low-dose CT, poses radiation risk, and biopsies can induce some complications. Additi...
A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning.
eLife
Jun 18, 2025
Radiotherapy resistance in nasopharyngeal carcinoma (NPC) is a major cause of recurrence and metastasis. Identifying radiotherapy-related biomarkers is crucial for improving patient survival outcomes. This study developed the nasopharyngeal carcinoma...
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