AIMC Topic: Biomarkers, Tumor

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Advances in exosome plasmonic sensing: Device integration strategies and AI-aided diagnosis.

Biosensors & bioelectronics
Exosomes, as next-generation biomarkers, has great potential in tracking cancer progression. They face many detection limitations in cancer diagnosis. Plasmonic biosensors have attracted considerable attention at the forefront of exosome detection, d...

Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data.

Journal of proteomics
Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. ...

Identification of cancer stem cell-related genes through single cells and machine learning for predicting prostate cancer prognosis and immunotherapy.

Frontiers in immunology
BACKGROUND: Cancer stem cells (CSCs) are a subset of cells within tumors that possess the unique ability to self-renew and give rise to diverse tumor cells. These cells are crucial in driving tumor metastasis, recurrence, and resistance to treatment....

A prognostic biomarker of disulfidptosis constructed by machine learning framework model as potential reporters of pancreatic adenocarcinoma.

Cellular signalling
BACKGROUND: Pancreatic adenocarcinoma (PAAD), known for its high lethality, has not been thoroughly explored in terms of its mechanisms and patterns of immune infiltration. Disulfidptosis, a newly identified mode of cell death, is likely associated w...

Early detection of pancreatic cancer by comprehensive serum miRNA sequencing with automated machine learning.

British journal of cancer
BACKGROUND: Pancreatic cancer is often diagnosed at advanced stages, and early-stage diagnosis of pancreatic cancer is difficult because of nonspecific symptoms and lack of available biomarkers.

Artificial intelligence: A transformative tool in precision oncology.

Oncotarget
Artificial intelligence (AI) is revolutionizing society and healthcare, offering new possibilities for precision medicine. Immunotherapy in oncology (IO) has similarly transformed cancer treatment through novel mechanisms of therapeutic action, but h...

Research on biomarkers using innovative artificial intelligence systems in breast cancer.

International journal of clinical oncology
Cancer is highly diverse and heterogeneous. Accurate and rapid analysis of the characteristics of individual cancer cells, using a complex array of big data that includes various clinicopathological features and molecular mechanisms, is crucial for a...

Machine learning developed a macrophage signature for predicting prognosis, immune infiltration and immunotherapy features in head and neck squamous cell carcinoma.

Scientific reports
Macrophages played an important role in the progression and treatment of head and neck squamous cell carcinoma (HNSCC). We employed weighted gene co-expression network analysis (WGCNA) to identify macrophage-related genes (MRGs) and classify patients...

is a novel marker for bladder cancer prognosis: evidence based on experimental studies, machine learning and single-cell sequencing.

Frontiers in immunology
BACKGROUND: Bladder cancer, a highly fatal disease, poses a significant threat to patients. Positioned at 19q13.2-13.3, LIG1, one of the four DNA ligases in mammalian cells, is frequently deleted in tumour cells of diverse origins. Despite this, the ...

Machine learning analysis of oxidative stress-related phenotypes for specific gene screening in ovarian cancer.

Environmental toxicology
BACKGROUND: Oxidative stress serves a crucial role in tumor development. However, the relationship between ovarian cancer and oxidative stress remains unknown. We aimed to create an oxidative stress-related prognostic signature to enhance the prognos...