AIMC Topic: Cell Line, Tumor

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Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma.

Scientific reports
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death globally, characterized by high morbidity and poor prognosis. The complex molecular and immune landscape of HCC makes accurate patient stratification and personalized treatment...

Fluorescence Guided Raman Spectroscopy enables the training of robust support vector machines for the detection of tumour marker proteins.

Scientific reports
Raman spectroscopy provides comprehensive biochemical information on a sample's composition, yet it is often used to analyze aggregated spectra rather than specific shifts. We introduce Fluorescence Guided Raman Spectroscopy (FGRS) as a methodology e...

Activated cancer-associated fibroblasts correlate with poor survival and decreased lymphocyte infiltration in infiltrative type distal cholangiocarcinoma.

Scientific reports
Cancer-associated fibroblasts promote tumor progression through growth facilitation, invasion, and immune evasion. This study investigated the impact of activated cancer-associated fibroblasts (aCAFs) on survival outcomes, immune response, and molecu...

Identification of CXCR4 as a potential preventive gene in clear cell renal cell carcinoma from machine learning and immune analysis.

Scientific reports
Clear cell renal cell carcinoma (ccRCC) represents a prevalent malignant kidney tumor characterized by high metastatic potential and recurrence rates. Investigations into the molecular mechanisms and therapeutic targets of ccRCC have provided novel d...

Potential role of TNFRSF12A in linking glioblastoma and alzheimer's disease via shared tumour suppressor pathways.

Scientific reports
Tumor suppressor genes (TSGs) are critical regulators of cellular homeostasis and are extensively studied in cancer biology. However, their roles in neurodegenerative diseases, particularly Alzheimer's disease (AD), remain poorly understood. Recent e...

Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.

Scientific reports
In the field of cancer therapy, the diversity and heterogeneity of cancer genomes in clinical patients complicate and challenge the effective use of non-targeted drugs, as these drugs often fail to address specific genetic events. Recent advancements...

Machine learning developed LKB1-AMPK signaling related signature for prognosis and drug sensitivity in hepatocellular carcinoma.

Scientific reports
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...

A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning.

eLife
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...

Self-Driving and Detachable Lab-Microrobots Tailor Drug Delivery for Closed-Loop Stimulation of the Antitumor Immune Cycle.

ACS nano
Hypoxia arises in most solid tumors with insufficient blood flow, which hinders the delivery and efficacy of therapeutic agents to tumors. In this work, utilizing anaerobic bacteria capable of seeking out hypoxic areas for flourishing, we constructed...