AIMC Topic: Biomarkers, Tumor

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Programmed cell death-related genes define distinct molecular subtypes and risk profiles in hepatocellular carcinoma.

Scientific reports
Hepatocellular carcinoma (HCC) is a biologically and clinically heterogeneous malignancy, whose initiation and progression are increasingly recognized to be driven by the aberrant regulation of programmed cell death (PCD) pathways. To elucidate this ...

Global DNA methylation signatures associated with chemoresistance and poor prognosis of high grade serous ovarian cancer.

Scientific reports
Ovarian cancer (OVCA) is third most lethal gynecologic cancers and acquired chemoresistance is the key link in the high mortality rate of OVCA patients. Currently, there are no reliable methods to predict chemoresistance in OVCA. In our study, we ide...

Development and Validation of an Extra Spindle Pole Bodies-like 1-Based Diagnostic and Prognostic Model for Hepatitis B Virus-Related Hepatocellular Carcinoma: Retrospective Cohort Study.

JMIR medical informatics
BACKGROUND: Early diagnosis of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B virus (HBV) is challenging. Models that combine novel biomarkers with clinical features may improve both early diagnosis and risk stratification, but f...

Integrated Chemical Array and SERS Profiling of Plasma Small Extracellular Vesicles for Breast Cancer Diagnosis.

Nano letters
Small extracellular vesicles (sEVs) are nanoscale vesicles carrying biomolecules reflective of their cellular origin, making them attractive biomarkers for cancer diagnosis. In this study, we present a high-throughput strategy integrating amphiphile-...

Multimodal deep learning model for prediction of breast cancer recurrence risk and correlation with oncotype DX.

Breast cancer research : BCR
BACKGROUND: Proper stratification of recurrence risk in breast cancer is crucial for guiding treatment decisions. This study aims to predict the recurrence risk of breast cancer patients using a multimodal deep learning model that integrates multiple...

A prognostic model for gastric cancer constructed by multiple machine learning algorithms.

Journal of molecular histology
Gastric cancer (GC) is a highly heterogeneous disease that requires highly accurate prognostic models. Machine learning is a powerful tool for identifying predictive biomarkers and developing prognostic models. Here, we aim to integrate bioinformatic...

Development of a serum protein biomarker panel for the diagnosis of pancreatic ductal adenocarcinoma using a machine learning approach.

Scientific reports
Early detection of pancreatic ductal adenocarcinoma (PDA) remains a major clinical challenge due to the lack of reliable biomarkers. We developed and validated a machine learning (ML)-based serum protein biomarker panel to enhance PDA diagnosis. Seru...

Integrating AI and RNA biomarkers in cancer: advances in diagnostics and targeted therapies.

Cell communication and signaling : CCS
Early detection and personalized treatment strategies are essential for enhancing patient outcomes, as cancer continues to be a significant cause of mortality on a global basis. In clinical practice, the identification and validation of reliable biom...

Machine learning analysis of coagulation-related genes for breast cancer diagnosis and prognosis prediction.

Scientific reports
The purpose of this study was to investigate the relationship between coagulation related genes (CRGs) and breast cancer (BC). First, we found that most CRGs are abnormally expressed in BC patients and correlated with their prognosis. Therefore, we e...