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Gene Expression Regulation, Neoplastic

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Comprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset.

Briefings in bioinformatics
Breast cancer is an alarming global health concern, including a vast and varied set of illnesses with different molecular characteristics. The fusion of sophisticated computational methodologies with extensive biological datasets has emerged as an ef...

Unveiling Varied Cell Death Patterns in Lung Adenocarcinoma Prognosis and Immunotherapy Based on Single-Cell Analysis and Machine Learning.

Journal of cellular and molecular medicine
Programmed cell death (PCD) pathways hold significant influence in the etiology and progression of a variety of cancer forms, particularly offering promising prognostic markers and clues to drug sensitivity for lung adenocarcinoma (LUAD) patients. We...

Integrated machine learning developed a prognosis-related gene signature to predict prognosis in oesophageal squamous cell carcinoma.

Journal of cellular and molecular medicine
The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM systems cannot accurately predict its prognosis, thus necessitating a predictive model. In this study, a 17-gene prognosis-related gene signature (PRS...

Machine Learning Diagnostic Model for Hepatocellular Carcinoma Based on Liquid-Liquid Phase Separation and Ferroptosis-Related Genes.

The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology
BACKGROUND/AIMS: Hepatocellular carcinoma (HCC) represents a primary liver malignancy with a multifaceted molecular landscape. The interplay between liquid-liquid phase separation (LLPS) and ferroptosis-a regulated form of cell death-has garnered int...

Deep contrastive learning for predicting cancer prognosis using gene expression values.

Briefings in bioinformatics
Recent advancements in image classification have demonstrated that contrastive learning (CL) can aid in further learning tasks by acquiring good feature representation from a limited number of data samples. In this paper, we applied CL to tumor trans...

Identification of novel M2 macrophage-related molecule ATP6V1E1 and its biological role in hepatocellular carcinoma based on machine learning algorithms.

Journal of cellular and molecular medicine
Hepatocellular carcinoma (HCC) remains the most prevalent form of primary liver cancer, characterized by late detection and suboptimal response to current therapies. The tumour microenvironment, especially the role of M2 macrophages, is pivotal in th...

Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations.

Cancer medicine
BACKGROUND: Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastric cancer diagnostic biomarkers in the homologous recombination pathway and then used radiomic featur...

Integrating single-cell transcriptomics and machine learning to predict breast cancer prognosis: A study based on natural killer cell-related genes.

Journal of cellular and molecular medicine
Breast cancer (BC) is the most commonly diagnosed cancer in women globally. Natural killer (NK) cells play a vital role in tumour immunosurveillance. This study aimed to establish a prognostic model using NK cell-related genes (NKRGs) by integrating ...

Establishment of diagnostic model and identification of diagnostic markers between liver cancer and cirrhosis based on multi-chip and machine learning.

Clinical and experimental pharmacology & physiology
OBJECTIVE: Most cases of hepatocellular carcinoma (HCC) arise as a consequence of cirrhosis. In this study, our objective is to construct a comprehensive diagnostic model that investigates the diagnostic markers distinguishing between cirrhosis and H...